Investigating and applyingfree solution capillary ......Australasian Polymer Symposium proceedings. ISBN number: 978-0-646-57205-5 ISBN number: 978-0-646-57205-5 6 th International

Investigating and applying free solution capillary electrophoresis with direct UV

detection to bioethanol research

By

James D. Oliver

BSc (Gene Science) BSc (Honours) Class I

Principle Supervisor:

Dr Patrice Castignolles

Co-supervisors:

Dr Michael Phillips Julie Markham Prof. Paul Peiris

Submitted for the completion of a Doctor of Philosophy degree at the University of

Western Sydney

July 2014

This thesis is dedicated to my

loving and supportive family

and to my love Amy.

Statement of Authentication

The work presented in this thesis is, to the best of my knowledge and belief, original except as

acknowledged in the text. I hereby declare that I have not submitted this material, either in full or in

part, for a degree at this or any other institution.

……………………………….

James Oliver

Date

James Oliver CE for bioethanol research i

Abstract

Bioethanol fermentation is an important process that is reducing the global demand on fossil

fuels and remains a field of research for the foreseeable future. Carbohydrates are sourced from

crops and hydrolyzed into simpler sugars then fermented into ethanol. Fermentation of sugars

sourced from food crops presents a sustainability issue with a growing population. Fermentation of

lignocellulosic material is more sustainable since it is sourced from non-food crops or wastes. It is

comprised of a variety of both pentose and hexose sugars. The composition and ratio of these varies

depending on the source of the material. Accurate analysis of the material is essential for both the

monitoring of the hydrolysis and its fermentation to valuable end-products such as ethanol.

Although there have been major advances in novel fermentation processes and the discovery and

construction of novel microorganisms, development of methods for analysis of these complex

substrates and their fermentation to ethanol has not advanced as rapidly.

High Performance Liquid Chromatography (HPLC) is one of the most common techniques for

the analysis of these complex substrates. The resolution of popular HPLC modes was compared and

no mode was found to have complete separation of common fiber sugars. Free solution Capillary

Electrophoresis / capillary zone electrophoresis (CE) is used and recognized in both research and

industry as a viable technique for the separation of carbohydrates. Recent studies on the use of

direct UV detection for determination of underivatized carbohydrates have shown great promise

and, in this work, the technique was applied to lignocellulosic plant fiber analysis as well as

monitoring its fermentation to ethanol and sugar alcohols. All resolution values with CE were higher

than 0.5, in contrast to any HPLC mode investigated. The running cost of HPLC, for this application, is

also much higher than CE. Determination of carbohydrates from lignocellulosic fiber by both HPLC

on a cation exchange resin and by CE resulted in values 17-22 % higher with CE than HPLC. The

influence of the counter-ion in the BackGround Electrolyte (BGE) was found to affect the resolution

and time of the separation. 130 mM KOH was shown to be effective for a fast separation of simple

mixtures and a mixture of 65 mM NaOH and 65 mM LiOH achieved a better resolution with more

complex carbohydrate mixtures than the other BGE’s studied. In a quantitative study on

fermentation samples, CE, HPLC and High Performance Anion Exchange Chromatography (HPAEC)

closely agreed within experimental error (less than 7 % difference from the average total detected

amount).

James Oliver CE for bioethanol research ii

The most significant drawback to the CE method was the limited understanding of the

mechanism behind the direct UV detection. Carbohydrates are readily observed at 270 nm with this

method, although they were commonly deemed as not possessing any chromophores. An

investigation of the mechanism of detection showed that a photo-oxidation reaction was

responsible for the detection and not enediolate formation as previously theorized. The photo-

oxidation reaction produces an intermediate species that absorbs at 270 nm. This resolved the

controversy between two competing theories. The reaction pathway was found to be similar to a

free radical pathway previously investigated by Electron Spin Resonance (ESR) by Bruce Gilbert et al.,

(1982) where semidiones were identified as intermediates. The reaction pathway was investigated

with quantum mechanical calculations of the theoretical UV spectra of the semidiones as well as by 13C NMR (Nuclear Magnetic Resonance) spectroscopy of the photo-oxidation end products. Some of

the end-products were found to be carboxylates and not aldehydes as previously theorized. The

sensitivity of the detection of carbohydrates was increased by 42 % with the use of a stable radical

photo-initiator.

A weakness of this method was the inability to monitor ethanol. When investigating a

potential method, it was found that methanol, ethanol, isopropanol and triethylamine had a

negative effect on the sensitivity of the detection hence they could be detected by interference of

the photo-oxidation reaction. In the case of ethanol, it is assumed to have a hydrogen abstracted by

the oxygen centered radicals produced by the photo-oxidation of a carbohydrate, thereby

interfering in the pathway (Scheme A-1). Ethanol quantification was achieved though the detection

of photo-oxidation interference.

Scheme A-1: Summarized scheme of glucose photo-oxidation to UV-absorbing intermediates (circled

red) and its interference by ethanol.

James Oliver CE for bioethanol research iii

Ethanol was quantified in a vodka sample and a simple fermentation sample with good

recovery (100 and 110% respectively). The robustness of ethanol determination was successfully

tested with a sample from ethanol fermentation of lignocellulosic plant fiber. A limitation of this

method, is that ethanol and carbohydrates require two subsequent injections on the same CE

instrument. Future work can focus on single injection in order to improve the method for online

monitoring of ethanol fermentation of lignocellulose.

James Oliver CE for bioethanol research iv

Acknowledgements

I wish to acknowledge the contribution that made this project possible. First and foremost,

my principal supervisor Dr Patrice Castignolles for his continued guidance in the field of analytical

chemistry, his infinite patience and dedication to our work. My supervisors Dr Michael Philips, Prof

Paul Peiris and Julie Markham who helped form the basis of this project and encouraged me to

pursue the field of analytical chemistry to meet our goals. My collaborator Dr Marion Gaborieau for

her guidance and training on NMR, her continued support and supply of high quality coffee. My

collaborator Prof Emily Hilder (UTas) who, despite her high demanding role, made time for her

thoughts on the work, access to the facilities of ACROSS UTas, support for funding applications and

the opportunity to give a talk at ACROSS UTas that yielded many ideas that have come out in the

publications. My collaborators Dr Christopher Fellows (UNE), Dr Yohann Guillaneuf and Dr Jean-Louis

Clement (Aix-Marseille) for their insight to radical chemistry. Dr Naama Karu (UTas) for her training

on IC, Dr Mark Williams for his training on the HPLC in my first year and introducing me to the field

of analytical chemistry and Patrice. Carol Adkins, Jenny Nelson Julie Svanberg and Adam Hale for

ensuring everything I needed was available and Prof Barry McGlasson for his guidance on the plant

science aspect of this work.

To my fellow students Emily Groison, Fiona Loudon, Adam Sutton, Ashleigh Van Oosterum,

Alison Maniego, David Fania, Tim Murphy, Elizabeth Whitty, Michelle Toutounji, Danielle Taylor, Joel

Thevarajah and Kristina Eriksson-Scott, thank you for your support over my candidature. To my

family for their continued support and most importantly to my partner Amy for her support,

patience and faith in me.

James Oliver CE for bioethanol research v

Preface

The project started with a focus of using non-food plants, adaptable to Australian marginal

lands, for bioethanol production. We first attempted to determine the carbohydrate composition of

these plants however the use of the well-established High Performance Liquid Chromatography

(HPLC) methods were unsuccessful. Free solution Capillary Electrophoresis (CE) with direct UV

detection was a new application of a well-researched separation technique. We compared the

separation of fiber sugars by common HPLC modes to that of CE. We then analyzed the

carbohydrate content of acid treated fiber by CE in comparison to the most robust HPLC column.

One limitation at this point was that the mechanism of the direct detection in CE was not well

understood. There were, at the time, 2 different proposed pathways by different authors. We

investigated the detection with a combination of novel CE experiments as well as 1H Nuclear

Magnetic Resonance (NMR) spectroscopy, and we were able to determine which of the 2 pathways

was correct. We then used our new understanding of this mechanism for the detection and

quantification of ethanol. We then finally brought this PhD project back to the foundation of

fermentation science by developing CE as a tool for monitoring both carbohydrates and ethanol in

both simple and complex fermentation broths. A quantitative comparison between CE and the most

common separation techniques of HPLC and High Performance Anion Exchange Chromatography

(HPAEC) was also carried out. The aim of this PhD project was to provide the field of biotechnology a

robust, yet simple and cost effective means of analyzing prospective fermentation feedstocks, as

well as an understanding of the separation and detection mechanisms that make it possible.

This thesis by publication is formatted in United States English as to conform to the

publications. References that are noted in the publications can be found at the end of the

corresponding section and are in each Journal’s individual style. All other references can be found in

section 7. Published versions of the publications can be found in the appendix.

James Oliver CE for bioethanol research vi

Publications

Oliver, J. D.; Gaborieau, M.; Hilder, E. F.; Castignolles, P., “Simple and robust determination of

monosaccharides in plant fibers in complex mixtures by capillary electrophoresis and high

performance liquid chromatography” Journal of Chromatography A (IF 4.6, top 10 % of Analytical

Chemistry) 2013, 1291, 179-86. http://dx.doi.org/10.1016/j.chroma.2013.03.041

Oliver, J. D.; Rosser, A. A.; Fellows, C. M.; Guillaneuf, Y.; Clement, J.-L.; Gaborieau, M.; Castignolles,

P., “Understanding and improving direct UV detection of monosaccharides and disaccharides in free

solution capillary electrophoresis” Analytica Chimica Acta (IF 4.4, top 10 % of Analytical Chemistry)

2014, 809, 183-193. http://dx.doi.org/10.1016/j.aca.2013.12.001

Oliver, J. D.; Gaborieau, M.; Castignolles, P., "Ethanol determination using pressure mobilization and

free solution capillary electrophoresis by photo-oxidation assisted ultraviolet detection." Journal of

Chromatography A (IF 4.6, top 10 % of Analytical Chemistry) 2014, 1348, 150-157. - Invited

contribution http://dx.doi.org/10.1016/j.chroma.2014.04.076

Oliver, J. D.; Sutton, A.; Karu, N.; Phillips, M.; Markham, J.; Peiris, P.; Hilder, E. F.; Castignolles, P.,

“Simple and robust monitoring of ethanol fermentations by capillary electrophoresis” Biotechnology

and Applied Biochemistry (IF 1.348) Manuscript ID: BAB-14-0134.R1, Accepted 06/07/2014

James Oliver CE for bioethanol research vii

http://dx.doi.org/10.1016/j.chroma.2013.03.041

http://dx.doi.org/10.1016/j.aca.2013.12.001

http://dx.doi.org/10.1016/j.chroma.2014.04.076

Conference and seminar presentations

Conferences:

33rd Australasian Polymer Symposium (33 APS, http://www.34aps.org.au/2012/abstracts.php):

Oral presentation “Determination of Monosaccharides from Chemically Hydrolysed Polysaccharides

for the Biofuel Industry” James D. Oliver, Mark Williams, Patrice Castignolles. Published as 33rd

Australasian Polymer Symposium proceedings. ISBN number: 978-0-646-57205-5

6th International Symposium on the Separation and Characterization of Natural and Synthetic

Macromolecules (SCM-6, http://www.scm-6.de/SCM-6.2087.0.html):

Oral presentation “Simple and robust separation of hydrolysed pectin and hemicellulose by capillary

electrophoresis and high performance liquid chromatography” James D. Oliver, Marianne Gaborieau,

Emily F. Hilder, Patrice Castignolles

Poster presentation “Fermentation of complex polysaccharide mixes to ethanol and other valued

products” James D. Oliver, Naama Karu, Adam Sutton, Emily F. Hilder, Michael Phillips, Julie

Markham, Paul Peiris, Patrice Castignolles

James Oliver CE for bioethanol research viii

Seminar Presentations:

2014 School of Science and Health post-graduate forum presentation “Monitoring carbohydrates

and ethanol in complex fermentations” James D. Oliver, Adam T. Sutton, Prof. Emily F. Hilder, Dr

Michael Phillips, Julie Markham, Prof. Paul Peiris, Dr Marion Gaborieau, Dr Patrice Castignolles (June

2014)

ACROSS Seminar at the University of Tasmania (UTas) invited by Prof Emily Hilder “Optimising

Analysis of Carbohydrates in Plant Material for the Biofuel Industry” James D. Oliver and Patrice

Castignolles (Feb 2012)

School of Science and Health Research Seminar at the University of Western Sydney (UWS) “Simple

and robust separation of monosaccharides in complex mixtures by capillary electrophoresis and high

performance liquid chromatography” James D. Oliver, Marianne Gaborieau, Emily F. Hilder, Michael

Phillips, Julie Markham, Paul Peiris and Patrice Castignolles (Sept 2012)

2012 School of Science and Health forum presentation “Industrial ethanol from novel substrates”

James D. Oliver, Michael Phillips, Julie Markham, Paul Peiris and Patrice Castignolles (June 2012)

School of Natural Sciences Research Seminar at the University of Western Sydney (UWS) “Bioethanol

from Novel Substrates” James D. Oliver, Paul Peiris, Julie Markham and Michael Phillips (July 2011)

2011 School of Natural Sciences forum presentation “Bioethanol from novel substrates” James D.

Oliver, Paul Peiris, Julie Markham and Michael Phillips (June 2011)

2010 School of Natural Sciences forum presentation “Bioethanol from novel substrates” James D.

Oliver, Paul Peiris, Julie Markham and Michael Phillips (June 2010)

James Oliver CE for bioethanol research ix

Table of Contents

Statement of Authentication ............................................................................................. i

Abstract ........................................................................................................................... ii

Acknowledgements .......................................................................................................... v

Preface ............................................................................................................................ vi

Publications .................................................................................................................... vii

Conference and seminar presentations .......................................................................... viii

Table of Contents ............................................................................................................. x

List of figures .................................................................................................................. xii

List of schemes ............................................................................................................... xvi

List of tables .................................................................................................................. xvii

List of equations ............................................................................................................. xx

List of abbreviations ....................................................................................................... xxi

1. Introduction ............................................................................................................... 1

1.1 Background ........................................................................................................................................... 1

1.2 The structure, hydrolysis and fermentation of lignocellulosic material ............................................... 2 1.2.1 The structure of lignocellulosic plant fiber ....................................................................................... 2 1.2.2 Hydrolysis of lignocellulosic material................................................................................................ 5 1.2.3 Microbial ethanol fermentation ....................................................................................................... 9

1.3 Determination of carbohydrates in complex matrices ....................................................................... 13 1.3.1 Chemical assays .............................................................................................................................. 14 1.3.2 Separation methods ....................................................................................................................... 15 1.3.3 Determination of carbohydrates in complex matrices summary ................................................... 26

1.4 Determination of ethanol ................................................................................................................... 27

1.5 PhD project aim and objectives .......................................................................................................... 27

2. Publication “Simple and robust determination of monosaccharides in plant fibers in complex mixtures by capillary electrophoresis and high performance liquid chromatography” ........................................................................................................... 29

2.1 Contribution to PhD work, field, and candidates personal and professional development ............... 29 2.1.1 Advantages and limitations of CE with direct UV detection and HPLC for carbohydrate determination in lignocellulosic plant fiber ................................................................................................. 29 2.1.2 Theory of NMR spectroscopy ......................................................................................................... 30 2.1.3 Investigation of the direct UV detection ........................................................................................ 32 2.1.4 Contribution to my personal development .................................................................................... 32

2.2 Publication .......................................................................................................................................... 34

2.3 Publication supporting information .................................................................................................... 55

3. Publication “Understanding and improving direct UV detection of monosaccharides and disaccharides in free solution capillary electrophoresis” ........................................... 68

3.1 Contribution to PhD work, field, and candidates personal and professional development ............... 68 3.1.1 Investigation of the photo-oxidation reaction ................................................................................ 68

James Oliver CE for bioethanol research x

3.1.2 Theory of radical chemistry in relation to carbohydrate photo-oxidation ..................................... 68 3.1.3 Contribution to my personal development .................................................................................... 69

3.2 Publication .......................................................................................................................................... 71

3.3 Publication supporting information .................................................................................................... 92

4. Publication “Ethanol determination using pressure mobilization and free solution capillary electrophoresis by photo-oxidation assisted ultraviolet detection” .................. 111

4.1 Contribution to PhD work, field, and candidates personal and professional development ............. 111 4.1.1 Ethanol determination with CE ..................................................................................................... 111 4.1.2 Contribution to my personal development .................................................................................. 112

4.2 Publication ........................................................................................................................................ 114

4.3 Publication supporting information .................................................................................................. 136

5. Publication “Simple and robust monitoring of ethanol fermentations by capillary electrophoresis” ........................................................................................................... 154

5.1 Contribution to PhD work, field, and candidates personal and professional development ............. 154 5.1.1 Fermentation monitoring by CE.................................................................................................... 154 5.1.2 Contribution to my personal development .................................................................................. 155

5.2 Publication ........................................................................................................................................ 156

5.3 Publication supporting information .................................................................................................. 183

6. Conclusion and future directions ............................................................................ 205

6.1 Conclusion ......................................................................................................................................... 205

6.2 Future directions ............................................................................................................................... 208 6.2.1 Improving sensitivity and throughput .......................................................................................... 208 6.2.2 Fermentation monitoring ............................................................................................................. 208 6.2.3 Application to polysaccharide characterization ............................................................................ 209 6.2.4 Application to nutrition and health .............................................................................................. 210 6.2.5 Conclusion of future work ............................................................................................................ 210

7. References ............................................................................................................. 211

8. Appendix ............................................................................................................... 216

James Oliver CE for bioethanol research xi

List of figures

Figure 1.2-1: (A) Glucose with numbered carbons and (B) cellulose polymer with a DP of 2n+2 adapted from [14]. .................................................................................................................................. 3

Figure 1.2-2: The three structural units of lignin polymers (adapted from [21]). .................................. 3

Figure 1.2-3: Hemicellulose polymer (adapted from [25]). .................................................................... 4

Figure 1.2-4: Structure of pectin polysaccharide rhamnogalacturonan II (adapted from [29]). ............ 5

Figure 1.2-5: Summary flowchart of substrate hydrolysis. ..................................................................... 6

Figure 1.2-6: Action of cellulose enzymes [38]. ...................................................................................... 8

Figure 1.3-1: (A) Isomerization of glucose between open chain form and cyclic form (D-Glucopyranose) and (B) reaction of D-Glucose in open form and 3,5-dinitrosalicylic acid to gluconic acid (2,3,4,5,6-pentahydroxyhexanoic acid) and 3-amino-5-nitrosalicylic acid. .................................. 14

Figure 1.3-2: Derivatization of glucose to its alditol acetate by acetic anhydride adapted from [89]. 16

Figure 1.3-3: Movement of sodium hydroxide and glucose along the pellicular anion exchange resin (adapted from [101, 102]). ................................................................................................................... 18

Figure 1.3-4: Counter-EOF separation in free solution capillary electrophoresis. ............................... 20

Figure 1.3-7: Possible mechanism for direct UV detection of carbohydrates in CE by UV initiated photo-oxidation (adapted from [111]). ................................................................................................ 26

Figure 2.1-1: Ranges of 1H chemical shifts for different functional groups, adapted from [138]. ....... 30

Figure 2.1-2: Ranges of 13C chemical shifts for different functional groups, adapted from [139]. ...... 31

Figure 2.2-1: Separation of a fiber sample (A) and mixture of standard (B) and using HPX-87H column 1: cellobiose, 2: glucose, 3: galactose, 4: xylose, 5: rhamnose, 6: arabinose, 7: void volume, 8: galacturonic acid, 9: unknown. ............................................................................................................. 41

Figure 2.2-2: Fiber standard 250 m·gL-1 (A) and sample (B) plotted with electrophoretic mobility and migration time (C-i). Separation by CE via Rovio et al.’s method [33]. 1: Cellobiose, 3: galactose, 2: glucose, 5: rhamnose, 4: arabinose, 6: xylose and corresponding UV absorption spectra (C-ii) for glucose (dashed line), xylose (solid line) and arabinose (dotted line). ................................................. 43

Figure 2.2-3: Migration of 1 g·L-1 of glucose into 130 mmol NaOH electrolyte by 16 kV electric field (solid line) and with voltage for 2 min followed by 42 mbar pressure (dashed line). .......................... 48

Figure 2.2-4: Degradation of glucose in 130 mmol NaOH monitored by migration with voltage. The arrows indicate the evolution with increasing time (0 h: bold solid line, 1.5 h: bold dashed line, 4 h: solid line, 7 h: dotted line, 27 h: dashed line, 46 h: bold dotted line). ................................................. 49

Figure 2.2-5: 1H NMR of glucose (1 g·L-1 with 130 mmol NaOH in D2O) before (A) and after irradiation with CE deuterium lamp for 5 min (B), 30 min (C) and 60 min (D). The arrows indicate the region in which new signals appear. .................................................................................................................... 51

Figure 2.2-6: Detection of glucose (1 g·L-1) in 130 mM NaOH with 16 kV separation. Each peak represents a pass of the sugar though the lamp, after which the voltage was inverted. .................... 52

Figure 2.3-1: HPLC Separation of sugars on HPX-87C with water mobile phase (A), HPX-87P with water mobile phase (B) and LC-NH2 with 75:25 ACN:water mobile phase (C). Sol: Solvent peak. 1: Cellobiose, 2: Glucose 3: Galactose 4: Xylose 5: Rhamnose 6: Arabinose 7: Mannose ........................ 56

James Oliver CE for bioethanol research xii

Figure 2.3-2: Calibration curve of response with RID for the sugars in our fiber standard on the HPX-87H column. .......................................................................................................................................... 57

Figure 2.3-3: Comparison of electrophoretic mobility of DMSO (1) and methanol (2) in 130 mmol NaOH and 36 mmol Na2HPO4 , detected at 200 nm. ........................................................................... 59

Figure 2.3-4: Calibration curves with standards, showing R2 values (capillary of 60 cm total length). Equations are given in Table 2. ......................................................................................................... 59

Figure 2.3-5: Evolution of the area of the glucose peak monitored by CE for a solution of glucose 1 g.L-1 in 130 mM NaOH. ......................................................................................................................... 63

Figure 2.3-6: Degradation of glucose in 130 mmol NaOH monitored by migration with pressure. The arrows indicate the evolution with increasing time (0 h: bold solid line, 1.5 h: bold dashed line, 4 h: solid line, 7 h: dotted line, 27 h: dashed line, 46 h: bold dotted line). ................................................. 64

Figure 2.3-7: Photo-oxidation of glucose in CE. Adapted from Gilbert et al. (1982) [5]. ...................... 65

Figure 2.3-8: Separation and detection of glucose (1 g·L-1) in 130 mmol NaOH in water (dotted line) and in D2O (solid line). ..................................................................................................................... 66

Figure 2.3-9: 1H NMR of 1 g·L-1 glucose (A) in 130 mmol of NaOH after 2 hours (A-I) and 5 days (A-II) and of sucrose (B) in water (B-I), 130 mmol NaOH after 2 hours (B-II) and after 5 days (B-III) ........... 67

Figure 3.2-1: Proposed sequence of events leading to UV-absorbing intermediates and carboxylated end-products. ........................................................................................................................................ 71

Figure 3.2-2: 13C NMR spectrum of 1 g⋅L-1 13C-labelled glucose continuously and hydrodynamically injected into CE, after subtraction of the spectrum of the control glucose. Both original spectra are shown in supporting information (Figure 3.3-4). Corresponding molecules taken from [40] where ‘R’ refers to a saturated alkyl group........................................................................................................... 81

Figure 3.2-3: effect of hydrogen peroxide in BGE on peak area of 1 g⋅L-1 sucrose in 130 mM NaOH. The Increase in peak area is relative to 1 g⋅L-1 sucrose injected with 130 mM NaOH BGE (no hydrogen peroxide). The error bar in this graph indicates the highest and lowest value (n=2) for a given run, while the different points indicate different runs. Runs were carried out on the HP3D instrument (n=2) as well as the Agilent 7100. ......................................................................................................... 86

Figure 3.2-4: The effect of Irgacure® 2959 in BGE on peak area of 1 g⋅L-1 sucrose. (A) The increase in peak area is shown relative to 1 g⋅L-1 sucrose injected with 130 mM NaOH BGE. Separations were carried out in a conventional capillary (solid line) and a high sensitivity capillary (dotted line). Error bar indicates relative standard deviation (n=5) (B) Overlay of sucrose peak in a conventional capillary without Irgacure® 2959 (dash line) and with 1 × 10-4 M Irgacure® 2959 (solid line), in a high sensitivity capillary without Irgacure® 2959 (dotted line) and with 1 × 10-8 M Irgacure® 2959 (dash dotted line). .......................................................................................................................................... 87

Figure 3.3-3: 1HNMR spectra of 1 g⋅L-1 13C glucose in 130 mM NaOH before (black) and after (red) continuous and hydrodynamic injection into CE. ............................................................................... 101

Figure 3.3-4:13CNMR spectra of 1 g⋅L-1 13Cglucose in 130 mM NaOH before (black) and after (red) continuous hydrodynamic injection into a capillary. .......................................................................... 102

Figure 3.3-5: Experimental 13C NMR spectrum for malondialdehyde tetrabutylammonium salt in the same conditions as Figure 3.3-4 (A) and predicted 13C NMR chemical shifts for dihydroxyacetone (B) (predictions performed with ChemNMR at neutral pH). .................................................................... 103

Figure 3.3-6: 13C NMR spectrum (black) and DEPT-135 NMR spectrum (red) of 1 g⋅L-1 13C glucose in 130 mM NaOH after continuous hydrodynamic injection into a capillary. A DEPT-135 NMR spectrum exhibits positive CH and CH3 signals, negative CH2 signals, and no signal for other carbons. ........... 104

James Oliver CE for bioethanol research xiii

Figure 3.3-7: 1H NMR spectra of A. glycerol (solid black), B. sodium oxalate (solid red), C.sodium glycolate (dotted black), D. sodium gluconate (dotted red), E. sodium methanoate (dashed black) and F. gluconolactone (dashed red). The chemical shifts predicted with ChemNMR are shown on the molecules on the left. ......................................................................................................................... 105

Figure 3.3-8: 13C NMR spectra of A. glycerol (solid black), B. sodium oxalate (solid red), C. sodium glycolate (dotted black), D. sodium gluconate (dotted red), E. sodium methanoate (dashed black) and F. gluconolactone (dashed red). The chemical shifts predicted with ChemNMR are shown on the molecules on the left. ......................................................................................................................... 106

Figure 3.3-9: First step in photolysis of Irgacure® 2959 adapted from [8]. Products react further to form a variety of radicals. ................................................................................................................... 109

Figure 3.3-10: UV absorption spectra of Irgacure® 2959 at 1 × 10-3 M (red) and 1 × 10-8 M (black) in 130 mM NaOH, obtained using pressure mobilization in the 7100 CE instrument using a high sensitivity capillary and pressure mobilization. .................................................................................. 109

Figure 3.3-11: Separation of oligoacrylate in a high sensitivity capillary (black) and normal fuse silica capillary (red). The initiated monomer (AA1) peak [9] is identified with the blue box. Separation conditions: 30 kV, 25 °C, 75 mM sodium borate buffer. .................................................................... 110

Figure 4.2-1: Pressure mobilization at 50 mbar: (A) of 2 g·L-1 sucrose in 130 mM NaOH not spiked (solid line) or spiked with ethanol at 250 mg·L-1 (dotted line), 1 g·L-1 (dashed line) and 2 g·L-1 (dotted-dashed line), with NaOH 130 mM as the mobile phase. (B) of 2 g·L-1 sucrose in 130 mM NaOH (with 130 mM NaOH as the mobile phase, dotted line) and of 1 g·L-1 ethanol in 2 g·L-1 sucrose and 130 mM NaOH (with 130 mM NaOH with 2 g·L-1 sucrose as the mobile phase, solid line). Performed on MDQ instrument (n=5). ................................................................................................................................ 119

Figure 4.2-2: Hydrogen abstraction from ethanol by a free radical R·. Adapted from [18]. .............. 120

Figure 4.2-3: Interference of alcohols and triethylamine at 5 mM (white) and 44 mM (striped) with the photo-oxidation of 2 g·L-1 sucrose during pressure mobilization. Relative difference in peak height (PHRD) is calculated as 𝑃𝑃𝑃𝑃RD = 𝑃𝑃𝑃𝑃S − 𝑃𝑃𝑃𝑃EtOH𝑃𝑃𝑃𝑃S where ‘PHS’ is the height of the sucrose peak, ‘PHEtOH’ is the height of the peak of sucrose spiked with ethanol. 10 cm effective length, 50 mbar pressure mobilization (n=3), performed on MDQ instrument. ................................................. 121

Figure 4.2-4: Possible reaction scheme for the interference of ethanol with glucose photo-oxidation. ............................................................................................................................................................ 122

Figure 4.2-5: Peak heights in the pressure mobilization of 2 g·L-1 sucrose (black square), 2 g·L-1 sucrose and 250 mg·L-1 ethanol (circle) and 2 g·L-1 sucrose and 1 g·L-1 ethanol (cross) in 130 mM NaOH passing the detection window multiple times (A) and the relative difference in their peak height (B). Initial pressure was 50 mbar (outlet to inlet) for 6 min then reversed (inlet to outlet) for 3 min and reversed every 3 min for a total of 28 passes. Error bars show standard deviation (n=3). Peak overlay can be seen in Figure 4.3-3. Performed on MDQ instrument. ...................................... 123

Figure 4.2-6: 1H NMR of 2 g·L-1 ethanol in the presence of 1 g·L-1 fully labelled 13C glucose continuously and hydrodynamically injected into a 7100 CE instrument (solid line) with non-irradiated control of the same age (dotted line) as well as freshly prepared control (dashed line).The spectra were normalized by the number of scans (20480, 2400 and 800 respectively) and the dilution factor (the controls were undiluted, sample was diluted 1/4.46 as described in section 2.2.1). ...... 125

Figure 4.2-7: 13C NMR of 1 g·L-1 fully labelled 13C glucose in the presence of 2 g·L-1 ethanol continuously and hydrodynamically injected into a 7100 CE instrument (solid line, top) and control (dotted line, bottom). The rectangles indicate the ethanol signals. .................................................. 126

James Oliver CE for bioethanol research xiv

Figure 4.2-8: Sucrose peak height (solid line), sucrose spiked with 1 g·L-1 ethanol peak height (dotted line) and difference between sucrose peak heights with and without ethanol (dashed) after pressure mobilization at 50 mbar in a 90 cm (10 cm effective length) capillary (n = 5). Error bars on peak height difference are ± sum of the standard deviations of both peaks (n=5). Performed on MDQ instrument. ......................................................................................................................................... 129

Figure 4.2-9: Detection of ethanol and carbohydrates via CE (A) and detection of varying concentrations of ethanol by interference with the photo-oxidation (B). BGE in outlet and inlet was 130 mM NaOH, BGE in capillary was 130 mM NaOH + 2 g·L-1 of sucrose. Migration was by electric field (24 kV) for 12 min followed by pressure mobilization at 50 mbar. Assignment of ethanol concentrations for (B): 2 g·L-1 (solid line), 1 g·L-1 (short dotted line), 500 mg·L-1 (short dashed line), 250 mg·L-1 (dotted line), 125 mg·L-1 (dashed line) and 0 mg·L-1 (dashed-dotted line). Current was 147 µA. Performed on 7100 CE instrument. .............................................................................................. 133

Figure 4.3-1: Relationship between the multiplication of the analyte Refractive Index (RI) by the concentration of the analyte and the relative peak difference. The analytes are methanol (square), ethanol (triangle), isopropanol (star), tert-butanol (pentagon) and triethylamine (circle). RI values are 20 °C [1]. ............................................................................................................................................. 137

Figure 4.3-2: Blank of injection 130 mM NaOH (green), 1 g·L-1 Ethanol in 130 mM NaOH (blue), 2 g·L-1 sucrose in 130 mM NaOH (black) and 1 g·L-1 Ethanol in 2 g·L-1 sucrose in 130 mM NaOH (red). ...... 138

Figure 4.3-5: 1H NMR of 1 g·L-1 fully labelled 13C glucose in the presence of 2 g·L-1 ethanol continuously and hydrodynamically injected into a 7100CE instrument for 94.5 h (black), control with no UV exposure for the same length of time (blue) and prepared fresh (red). ................................. 141

Figure 4.3-6: 13C NMR of 1 g·L-1 fully labelled 13C glucose in the presence of 2 g·L-1 ethanol continuously and hydrodynamically injected into a 7100CE instrument for 94.5 h (black), control with no UV exposure for the same length of time (blue) and freshly prepared control (red). ................. 142

Figure 4.3-7: Oxidation of ethanol radical to acetic acid (a-d) adapted from [5] and to butan-2,3-diol (e). G-H represents glucose and G· represents glucose derived radical as shown in Figure 4.2-4. .... 144

Figure 4.3-8: Possible but unobserved products of glucose photo-oxidation in the presence of ethanol. Unobserved chemical shifts are in brackets. ........................................................................ 145

Figure 4.3-9: Possible interference of water derived radicals by ethanol. ......................................... 145

Figure 4.3-11: Peak areas of sucrose (solid line) and sucrose spiked with 1 g·L-1 ethanol (dotted line), as well as difference between sucrose peak areas with and without ethanol (dashed) after pressure mobilization at 50 mbar in a 90 cm (10 cm effective length) capillary (n = 5). Error bars on peak area difference are ± sum of the standard deviations of both peaks (n=5). Performed on MDQ instrument. ............................................................................................................................................................ 148

Figure 4.3-12: Sucrose peak at 500 mg·L-1 (black solid), 1000 mg·L-1 (black dotted), 2000 mg·L-1 (red solid) 4000 mg·L-1 (red dotted) and 8000 mg·L-1 (blue solid) without ethanol (A) with 1000 mg·L-1 ethanol (B). Performed on MDQ instrument. ..................................................................................... 149

Figure 4.3-13: Effect of sucrose concentration on the signal to noise ratio (S/N). ............................ 150

Figure 4.3-14: Standard curve obtained from MDQ (red) obtained from 4 separated days spaced over a month, 7100 (black) and a combination of the 2 (blue). ................................................................. 150

Figure 4.3-15: Calibration curve of ethanol concentration against difference in peak height for sucrose (black circles) and xylitol (red triangles) (n=5). ...................................................................... 151

Figure 4.3-16: Pressure mobilization of 5.8 mM sucrose (black) and xylitol (red) in the presence of 1 g·L-1 ethanol. Performed on MDQ instrument. ................................................................................... 151

James Oliver CE for bioethanol research xv

Figure 4.3-17: Comparison of the signal to noise ratio of a sucrose peak (2 g·L-1) between the 7100 CE and the MDQ instruments (n=5). ........................................................................................................ 152

Figure 4.3-18: CE of ethanol when the electric field (24 kV) was applied for the entire separation. Performed on 7100 CE instrument. .................................................................................................... 152

Figure 4.3-19: Detection of 1 g·L-1 ethanol via CE by interference with the photo-oxidation of sucrose. Indirect ethanol peak is shown in the dashed boxes. BGE in outlet and inlet was 130 mM NaOH, BGE in capillary was 130 mM NaOH + 2 g·L-1 of sucrose (black, S/N = 37) and 130 mM NaOH + 0.5 g·L-1 of sucrose (red, S/N = 36). Migration was by electric field (24 kV) for 12 min followed by pressure mobilization at 50 mbar. Current was 160 µA. Performed on 7100 CE instrument. .......................... 153

Figure 5.2-3: Quantitative comparison of glucose (A), arabinose (B) and arabitol (C) in a complex

fermentation sample by HPLC ( ) and CE ( ). Error bars represent ± STD (n=3). ........................ 173

Figure 5.2-4: Fermentation of hydrolyzed plant fiber to ethanol. Samples taken at 0 hours (A), 6 hours (B) and 24 hours (C). Peak assignments: (1) lactose (internal standard), (2) galactose, (3) glucose, (4) mannose, (5) fructose, (6) arabinose, (7) xylose, (8) arabitol, (9) unknown (for migration plot see Figure 5.3-8). Ethanol peak in sequential injection given as inverted peak for 0 h ( ), 6 h (

) and 24 h ( ). .............................................................................................................................. 174

Figure 5.3-2: Contour plot of the varying KOH and LiOH proportion in 130 mM total alkaline concentration (when relevant the third component is NaOH). Contour shows the distribution of inverse difference in electrophoretic mobility of glucose and mannose where the lowest value is shown by the darkest region. The labels (stars) display the relative position of rhamnose to glucose and mannose defined as (𝑚𝑚𝑚𝑚 − 𝑚𝑚𝐺𝐺)(𝑚𝑚𝑚𝑚 − 𝑚𝑚𝑚𝑚). ........................................................................ 187

Figure 5.3-4: Graphical determination of peak widths and retention times taken as an example and extracted from Figure 5.2-1A glucose and galactose peaks. .............................................................. 192

Figure 5.3-6: Separation of glucose (a) and fructose (b) (equal concentration) in 130 mM KOH with a fixed concentration of 500 mg·L-1 lactose (c) internal standard. Glucose and fructose at (A) 1000 mg·L-1 (B) 500 mg·L-1(C) 250 mg·L-1(D) 125 mg·L-1 (E) 62.5 mg·L-1 each............................................... 197

Figure 5.3-9: Separation of ethanol and carbohydrates in a 25 mg·L-1 standard (black) and fermentation sample (red) with HPAEC-PAD. Peak assignment: 1. Void peak, 2. Ethanol, 3. Elevated baseline indicating other analytes, 4. Media components, 5. Arabinose, 6. Glucose, 7. Fructose. PA1 column with a 30mM NaOH mobile phase at 1 mL•min-1 operating at room temp. ........................ 203

List of schemes

Scheme 2.3-1: Set-up of CE photo-oxidation experiment. ................................................................... 62

Scheme 3.2-2: Formation of semidione B from β-D-glucose adapted (and corrected to place missing radical in 1st and 2nd molecule), from Gilbert et al. [29]. It is noted that between the 4th and 5th stage, protonation followed by de-protonation of the alcohol on the 4th carbon is not necessary. .............. 76

Scheme 3.3-1: List of potential UV absorbing intermediates based on Gilbert et al.[2] and the assignments. ......................................................................................................................................... 96

Scheme 3.3-2: A second possibility for the oxidation of glucose in the presence of oxygen leading to sodium methanoate and sodium glycolate as well as sodium glycerate. .......................................... 107

James Oliver CE for bioethanol research xvi

List of tables

Table 1.3-1: The structure and pKa of some monosaccharides, disaccharides and sugar alcohols. ..... 22

Table 2.2-1: Experimental conditions used in HPLC for the different columns. ................................... 38

Table 2.2-2: Resolution values for consecutive peaks on each column and in CE. .............................. 40

Table 2.2-3: Electrophoretic mobility (µep) with its relative standard deviation (RSD) and calibration of response at 270 nm with its correlation coefficient R2, for the sugars in our fiber standard (capillary of 66 cm total length). ........................................................................................................... 44

Table 2.2-4: Comparison of the determined sugar concentration C (g·L-1), with their relative standard deviation RSD (%), by CE (capillary of 66 cm total length) and HPLC with HPX-87H column. .............. 46

Table 2.3-1: Calibration of response with RID with its relative standard deviation (RSD) of response at 270 nm with its correlation coefficient R2, for the sugars in our fiber standard on the HPX-87H column .................................................................................................................................................. 57

Table 2.3-2: Electrophoretic mobility (10-8 m2 V-1s-1) of common fiber sugars determined in this study (35 injections), before and after correction using lactose as the internal standard. RSD is the relative standard deviation (%). The values are compared with published values. .......................................... 58

Table 2.3-3: Estimate of the cost of the typical CE and HPLC separations ($ is for Australian dollar and prices are as for 2012). ......................................................................................................................... 60

Table 2.3-4: Comparison of the determined sugar concentration C (g•L-1), with their relative standard deviation RSD (%), by CE (capillary of 66 cm total length) and HPLC with HPX-87H column. Compared to Table 4, these separations have been fully reproduced with fresh solution and new capillaries. ............................................................................................................................................. 60

Table 2.3-5: Fraction experiments to determine the loss in HPLC in comparison to CE: comparison of the concentrations of glucose injected in HPLC, Cinj, eluted from HPLC according to RID detection, CRID, and as determined from CE, CCE. ................................................................................................... 62

Table 2.3-6: Peak areas of glucose degrading in 130 mM sodium hydroxide, separated by CE with 16 kV. ......................................................................................................................................................... 63

Table 3.2-2: Simulated spectral properties of possible UV absorbing intermediates. ......................... 80

Table 3.2-3: Possible identification of some products from photo-oxidation of 13C glucose according to their 13C and 1H NMR chemical shifts δ. The individual 1H and 13C NMR spectra are shown in supporting information. All compounds listed except sodium oxalate, malondialdehyde and sodium gluconolactone are potentially present in the sample. ........................................................................ 84

Table 3.2-4: Comparison of limit of detection (LOD) between different analytical separation and detection methods. CE separation with direct UV detection (this study) was at 24 kV in 90 cm (81.5 cm effective length) high sensitivity capillary in 130 mM NaOH with 1 × 10-8 M Irgacure® 2959. ...... 89

Table 3.3-1: Results of TD-B3LYP/6-31++G(2d, 2p) Calculations. Electronic energies (E), zero point energies (EZPE), thermal energies (U), enthalpies (H) and Gibbs Free Energies (G) in hartrees and entropies (S) in cal mol–1 K–1. ................................................................................................................ 97

Table 3.3-2: Principal Features of Predicted Spectra. ........................................................................... 98

Table 4.2-2: Linearity of ethanol quantification, LOD, LOQ and recovery in pressure mobilization and CE with sucrose and xylitol as background carbohydrates. n=5 for all standards and samples. ....... 131

James Oliver CE for bioethanol research xvii

Table 4.3-4: Estimate of the minimal concentration (E) of end products that, resulting from decomposition of ethanol, could be detected by 13C NMR. ............................................................... 143

Table 4.3-5: Predicted 13C shifts of potential end products of carbohydrate photo-oxidation in the presence of oxygen. Prediction done with ChemDraw Ultra 12. Bold, underlined chemical shifts are not observed in the 13C NMR spectrum (Figure 4.2-4). ...................................................................... 143

Table 4.3-6: Predicted 13C shifts of potential UV absorbing intermediates from carbohydrate photo-oxidation as studied by [4]. Prediction done with ChemDraw Ultra 12. Bold, underlined chemical shifts are not observed in the 13C NMR spectrum (Figure 4.2-4)........................................................ 143

Table 5.2-1: Electrophoretic mobility (µep) of carbohydrates and related fermentation end products (0.5 g·L-1 each) in different BGE (a more extensive version is given as Table 5.3-1). Conditions: Voltage 24 kV, temperature 15 °C, current of 160 ± 6 µA. The values are an average of three sequential injections. .......................................................................................................................... 163

Table 5.2-2: Resolution (expressed as orthogonal valley to peak ratio expressed as 100 x Vs/P) of the mixture of carbohydrates (the lowest value is given in bold). Separation conditions: 24 kV, 90 cm capillary (81.5 cm effective length). Mixture contains 0.5 g L-1 xylitol, arabitol, lactose, galactose, glucose, rhamnose, mannose, arabinose and xylose. n=3. The lowest values are indicated in bold. 167

Table 5.2-3: List of current/potential fermentation substrates and the recommended BGE to monitor the fermentation using CE. ................................................................................................................. 170

Table 5.3-1: Comparison of various Background Electrolytes (BGE) and their effect on electrophoretic mobility and electro-osmotic flow (EOF). Electrophoretic mobility was calculated using Equation 5.3-1. ......................................................................................................................................................... 185

Table 5.3-2: Electrophoretic mobility of carbohydrates and related fermentation end products (0.5 g·L-1 each) in LiOH with varying concentration. Conditions: Voltage 24 kV, temperature 15 °C. ....... 186

Table 5.3-4: Values for a, b, d, e, and f for exploration viscosity by Equation 5.3-5 .......................... 191

Table 5.3-5: Calculation of the ratio of ionic charge to hydrodynamic radius calculated by Equation 5.3-4 .................................................................................................................................................... 191

Table 5.3-8: Time to achieve a given resolution, Tres, based on Rovp, for a mixture of carbohydrates. Separation conditions: 24 kV, 90 cm capillary (81.5 cm effective length). Mixture contains 0.5 g·L-1 xylitol, arabitol, lactose, galactose, glucose, rhamnose, mannose, arabinose and xylose. Lowest Tres is in bold. ................................................................................................................................................ 195

Table 5.3-9: Tres based on Rvp (Table 5.3-8 for the equivalent values based on Rovp) for a mixture of carbohydrates. Separation conditions: 24 kV, 90 cm capillary (81.5 cm effective length). Mixture contains 0.5 g·L-1 xylitol, arabitol, lactose, galactose, glucose, rhamnose, mannose, arabinose and xylose. Lowest Tres is in bold. ............................................................................................................... 196

Table 5.3-10: TRes in BGE: 52 mM KOH 52 mM NaOH 26 mM LiOH (M5), capillary length 112 cm (103.5 cm effective length), 29.8 kV electric field. The lowest values are given in bold. ................... 197

Table 5.3-12: Repeatability of HPAEC injections of 2 fermentation samples in terms of determined concentration and retention time (n=5). BDL= below detectable limit. PA1 column with a 30mM NaOH mobile phase at 1 mL·min-1 operating at room temp............................................................... 198

Table 5.3-13: Repeatability of HPLC injections of 5 fermentation samples in terms of determined concentration and retention time (n=5). HPX-87H hydrogen form cation exchange resin with a mobile phase 5 mM H2SO4 at 0.60 mL·min-1 operating at 60 °C. ........................................................ 199

Table 5.3-14: Analysis of results displayed in Figure 5.2-2. ................................................................ 199

James Oliver CE for bioethanol research xviii

Table 5.3-15: Calibration curves for quantification of carbohydrates in fiber fermentation samples by CE. ....................................................................................................................................................... 200

Table 6.1-1: Comparison of HPLC, HPAEC and CE on various fermentation samples. ....................... 206

James Oliver CE for bioethanol research xix

List of equations

Equation 1.2-1: Aerobic respiration of glucose [44]. .............................................................................. 9

Equation 1.2-2: Anaerobic respiration of glucose by an ethanologen [44]. ........................................... 9

Equation 1.2-3: Microaerobic respiration of xylose by Pichia stipitis [49]. .......................................... 10

Equation 1.3-1: Relationship between apparent velocity (vapp), electroosmotic velocity (veof) and electrophoretic velocity (vep). ............................................................................................................... 20

Equation 1.3-2: Calculation of the velocity of the EOF (veof) and an analytes’ apparent velocity (vapp), where ‘v’ stands for either ‘vapp’ or ‘veof’. Where ‘Ld’ is the length to the detection window (or effective length) and ‘t’ is the time the analyte or EOF marker is detected. ....................................... 20

Equation 1.3-3: Relationship between an analytes’ electrophoretic mobility ‘µep’, its ionic velocity ‘v’ and the electric field ‘E’. ....................................................................................................................... 20

Equation 1.3-4: Calculation of the electric field strength where ‘Lt’ is the total length of the capillary and ‘V’ is the voltage. ............................................................................................................................ 21

Equation 1.3-5: Formula used to calculate electrophoretic mobility ‘µep’ of an analyte. .................... 21

Equation 1.3-6: Stokes law governing electrophoretic mobility........................................................... 21

Equation 3.1-1: Formation of oxygen biradicals. .................................................................................. 69

Equation 3.2-1 ....................................................................................................................................... 85

Equation 3.2-2 ....................................................................................................................................... 85

Equation 3.2-3 ....................................................................................................................................... 85

Equation 3.3-1 ..................................................................................................................................... 102

Equation 3.3-2 ..................................................................................................................................... 108

Equation 3.3-3 ..................................................................................................................................... 108

Equation 3.3-4 ..................................................................................................................................... 108

Equation 5.2-1 ..................................................................................................................................... 166

Equation 5.2-2 ..................................................................................................................................... 169

Equation 6.3-1: relationship between apparent velocity (vapp), electroosmotic velocity (veof) and electrophoretic velocity (vep) .............................................................................................................. 184

Equation 6.3-2: Formula used to calculate the experimental electrophoretic mobility values ......... 184

Equation 5.3-3: Expression of electro-osmotic flow (6) ..................................................................... 190

Equation 5.3-4: Stokes law governing electrophoretic mobility (6) ................................................... 190

Equation 5.3-5: Calculation of viscosity of KOH, NaOH and LiOH. (5) ................................................ 191

Equation 5.3-6: Calculation for resolution of symmetric peaks ......................................................... 192

Equation 5.3-7: Formula used to calculate the experimental electrophoretic mobility values with an internal standard................................................................................................................................. 200

James Oliver CE for bioethanol research xx

List of abbreviations

°C Degree Celsius δ Chemical shift η Viscosity of the solution µL Microliter µm Micrometre (v/v) Volume to volume (w/v) Weight to volume 13C NMR 13C nuclear magnetic resonance 1H NMR 1H (proton) NMR ACROSS Australian centre of research on separation science AFEX Ammonia fiber explosion ACN Acetonitrile BGE Background electrolyte C4D or CCD Contactless conductivity detection CE Capillary electrophoresis (refers to free solution capillary

electrophoresis or capillary zone electrophoresis in this thesis) DAD Diode array detector DMSO Dimethylsulfoxide DSS 4,4-dimethyl-4-silapentane-1-sulfonic acid DP Degree of polymerization E Electric field EOF Electroosmotic flow ESR Electron spin resonance EtOH Ethanol FID Flame ionization detection g Gram GC Gas chromatography GLC Gas-Liquid Chromatography GMO Genetically modified organism GRAS Generally recognized as safe h Hour HILIC Hydrophilic interaction liquid chromatography HMF hydroxymethyl furfural HPAEC High performance anion exchange chromatography HPLC High performance liquid chromatography Hz Hertz i.d. Internal diameter IF Impact factor L Litre Ld Length of capillary to the detector/effective length Lt Total capillary length LOD Limit of detection LOQ Limit of quantification M Mole per litre m Meter mapp Apparent mobility meof Electroosmotic mobility µep Electrophoretic mobility

James Oliver CE for bioethanol research xxi

mbar Millibar mg Milligram min Minute mL Millilitre mM Millimole per litre mm Millimetre mol Mole MS Mass spectrometry MW Molecular weight nm Nanometre NMR Nuclear magnetic resonance o.d Outer diameter PAD Pulsed amperometric detection pH Potential hydrogen pKa Negative log of acidity constant Ka ppm Parts per million PPP Pentose phosphate pathway q Effective charge r Ionic radius rf Radio frequency RP-HPLC Reverse phase high performance liquid chromatography RSD Relative standard deviation s Second SD Standard deviation SDS sodium dodecyl sulfate SNR Signal-to-noise ratio SPE Solid phase extraction t Time teof Migration time of the neutral species tm Migration time of the analyte UNE University of New England UTas University of Tasmania UV Ultraviolet UWS University of Western Sydney V Voltage v Velocity veof Velocity of the neutral species vapp Apparent velocity vep Electrophoretic velocity

James Oliver CE for bioethanol research xxii

1. Introduction

1.1 Background

Liquid biofuels have the potential to provide a carbon neutral liquid energy source. Since

the realization of a limited coal and oil supply, alternative sources of renewable energy have been

investigated worldwide. It is believed that fossil fuels will be depleted at some point over the next

century. They could last for the next 20 to 100 years if they were progressively replaced by

alternative fuels [1]. Biofuels are an attractive alternative for transport fuels as they require

minimal change to infrastructure and also have the potential to be carbon neutral. In Brazil

between 1975 and 2005, ethanol fuel substituted for 240 billion liters of gasoline, saving $56

billion in direct importation costs over the 30 year period [2]. Both the sucrose from sugar cane

and starch from corn are easy to release and ferment to ethanol. Although there are clear

benefits of the use of biofuels, especially in Brazil, there have been a number of studies that have

continually reviewed its economic and environmental sustainability [1, 3]. First generation

biofuels (or conventional biofuels) are prepared from food crops, including sugar cane from Brazil

and corn from the USA and Mexico. In Australia, high grain prices in 2007 forced plans for a

number of bioethanol production plants to be cancelled [4]. A major shortfall of this approach is

that supply is limited by food demand, which increases with a growing population.

Second generation biofuels (or advanced biofuels) are prepared from lignocellulosic

materials. These are sourced from non-food crops, such as switch grass [5] and various woods [6,

7], or food crop wastes, such as sugar cane bagasse [8], agave bagasse [9] and corn stover [10].

They are considered to be more sustainable (in relation to food security) as they can be obtained

from any plant material not only food crops. A drawback to these sources is that the

carbohydrates of these plants are more difficult to access, requiring more complex treatment to

release the carbohydrates which makes economic sustainability an issue. Unlike first generation

fuels, lignocellulosic materials also contain other hexose sugars, such as galactose, rhamnose and

mannose, and pentose sugars, such as arabinose and xylose (discussed later in 1.2.1). These

complex mixtures of carbohydrates require innovative fermentation strategies to ferment the

larger variety of sugars (discussed later in 1.2.3) and analysis techniques (discussed later in 1.3) to

monitor the process.

James Oliver CE for bioethanol research 1

As the field moves further into second generation biofuels, the performance of the

methods to analyze these substrates are not improving accordingly. These samples have complex

matrices (large variation of molecules in the sample) therefore a robust separation is required to

obtain accurate results. As there is no IUPAC definition [11] robust in this study is to mean “a

method that can be applied to analytes in a wide variety of matrices” [12].

1.2 The structure, hydrolysis and fermentation of lignocellulosic material

The hydrolysis and fermentation of lignocellulose substrates is more complex than that of

first generation substrates. Lignocellulose contains a variety of sugars bound by lignin, which

makes its breakdown and subsequent analysis more difficult.

1.2.1 The structure of lignocellulosic plant fiber

Lignocellulosic plant fiber is comprised of cellulose microfibrils, a variety of hemicellulose

and pectin polysaccharides, as well as the chemical compound lignin. Together these polymers

make up the primary and secondary cell wall that account for the majority of dry mass of the

plant as well as its fermentable carbohydrates. The ratio and composition of each will vary greatly

depending on the plant.

1.2.1.1 Cellulose

Cellulose ([C6H10O5]n) is the most abundant naturally occurring compound. It is a

reproducible organic polysaccharide, comprising at least a third of advanced plants [13]. Cellulose

is a polymer of glucose (C6H12O6) units joined by β(1→4) bonds [14]. The β(1→4) of cellulose

differs from the glucose α(1→4) of starch in that every second β(1→4) force the glucose unit to

bend back 180° on itself creating tightly bound subunits. The intermolecular hydrogen bonds

formed between glucose subunits by this bend (Figure 1.2-1), produces a strong secondary

ribbon structure [15]. The length of the polymer chain varies greatly with the type of plant and is

measured as Degrees of Polymerization (DP) that is the number of glucose units in a chain. The

chains of cellulose are bound with each other by hydrogen bonds to form a water impermeable

crystalline microfibril structure, stronger than starch, which makes the sugars more difficult to

hydrolyze [16, 17].


Figure 1.2-1: (A) Glucose with numbered carbons and (B) cellulose polymer with a DP of 2n+2

adapted from [14].

1.2.1.2 Lignin

Lignin ([C9H10O2]n [C10H12O3]n [C11H14O4]n) is the second most abundant terrestrial polymer

and accounts for 30 % of organic carbon in the biosphere [18]. This complex phenolic polymer

gives plants structure and strength by cross-linking with cellulose in the secondary cell wall [19,

20]. It is comprised of three structural units (Figure 1.2-2) that vary in ratio depending on the

plant [21].

Figure 1.2-2: The three structural units of lignin polymers (adapted from [21]).

The hydrolysis of lignin is needed to access the cellulose polymers, however the products

may inhibit the carbohydrate fermentation to ethanol [22] and increase difficulty in analysis (see

1.3.2.2) of both the hydrolysis plant fiber and subsequent fermentation.


1.2.1.3 Hemicellulose and pectin

Hemicelluloses are a family of polysaccharides that link to cellulose microfibrils by

hydrogen bonds in the primary cell wall [21] and have other functions throughout the plant [23].

The structure and composition of hemicellulose varies greatly with plant type. Hemicellulose

consists of varying amounts and concentrations of xyloglucans (β(1→4)-linked glucose backbone

with α(1→6)-linked single D-xylose unit side chains; Figure 1.2-3). Some D-xylose units have

β(1→2)-linked D-galactose or D-fucose units or L-arabinose residues (Figure 1.2-3). Arabinose

may also be directly linked to the glucose backbone (C-2) [19, 24, 25] (Figure 1.2-3).

Figure 1.2-3: Hemicellulose polymer (adapted from [25]).

Pectin is present in both the primary cell wall and the spaces between cells (middle

lamella). Pectin is a group of polysaccharides that contain a significant amount of galacturonic

acid and some smaller amounts of arabinose, galactose and rhamnose. Galacturonic acids are

bound together by α(1→4) bonds [26] (Figure 1.2-4). Like hemicellulose, its structure varies

greatly with plant type. In addition, the distributions of composition for pectin varies between

plants, as well as within one plant [27, 28]. Pectin contributes structure to the cell wall by forming

intermolecular bonds with free carboxyl groups.


Figure 1.2-4: Structure of pectin polysaccharide rhamnogalacturonan II (adapted from [29]).

The determination of carbohydrates during hydrolysis and fermentation of lignocellulose

(discussed in 1.2.2 and 1.2.3) requires a method capable of determining not only the amount of

glucose from cellulose but the variety of sugars found in hemicellulose and pectin resisting

interference of other compounds, such as lignin monomers.

1.2.1.4 Structure of lignocellulose summary

The structure of lignocellulose is complex which leads to difficulty in its hydrolysis

(discussed in 1.2.2), fermentation (discussed in 1.2.3) as well as its analysis (discussed in 1.3).

1.2.2 Hydrolysis of lignocellulosic material

Due to intermolecular bonding between lignin, pectin, cellulose and hemicellulose within

cell walls, the plant material must be pre-treated and hydrolyzed into fermentable

monosaccharides and disaccharides before their fermentation into ethanol. There are two

primary methods of pre-treatment; acid or alkaline hydrolysis and steam explosion (Figure 1.2-5).

When pre-treating lignocellulose, the aims are to hydrolyze the hemicelluloses completely to

monomers without degradation, to remove the lignin and to reduce the size of the cellulose

semi-crystalline structure for enzyme hydrolysis [30, 31]. According to Kumur et al. (2009) the

pre-treatment must improve the release of sugars or the ability to subsequently release sugars by

hydrolysis, avoid the degradation or loss of carbohydrate, avoid the formation of by-products

that are inhibitory to the subsequent hydrolysis and fermentation processes and be cost-

effective. After pre-treatment, the cellulose polymers are exposed for enzymatic hydrolysis

(Figure 1.2-5). Alternatively acid hydrolysis or pyrolysis maybe used (Figure 1.2-5).


Figure 1.2-5: Summary flowchart of substrate hydrolysis.

1.2.2.1 Physical pre-treatment (milling)

The substrates are milled to a smaller particle size for hydrolysis via chipping and milling.

It has been shown, in relation to woody plant species, that the particle size has a direct impact on

the efficacy of the pre-treatment step [32]. Woody substrates are generally ‘chipped’ down to a

size of 10-30 mm and/or milled down to a size of 0.2-2 mm [33]. The overall effect of milling is

that cellulose loses some of its semi-crystalline structure.

1.2.2.2 Physicochemical pre-treatment

Physicochemical pre-treatment uses a combination of physical pre-treatment such as

mild pyrolysis, which exploits the molecular alteration and decomposition of biomass under heat,

with chemical decomposition. The three primary types are:

• Steam Explosion: chipped biomass is exposed to high pressure steam between 160-260

°C followed by a swift pressure reduction, which forces the biomass to undergo explosive

decompression [33].

• Ammonia Fiber Explosion (AFEX): biomass is exposed to liquid ammonia at high-pressure

and temperature followed by a swift pressure reduction. This pre-treatment does not

significantly solubilize the hemicelluloses compared to steam explosion, acid catalyzed

steam explosion and acid pre-treatment in studied substrates [33].


• CO2 Explosion: CO2 explosion is similar to steam and ammonia fiber explosion wherein

the biomass is treated under high pressure and temperature with CO2. This is followed by

a rapid lowering in pressure. Theoretically, CO2 forms carbonic acid which then speeds up

hydrolysis [33]. For recycled paper mix and bagasse, CO2 explosion is found to be more

cost effective than ammonia explosion and, unlike steam explosion, does not form

inhibitory compounds [34]. However, this method with alfalfa only yielded 75% of the

theoretical glucose in the following hydrolysis, which is a lower yield compared to steam

and ammonia explosions [35].

1.2.2.3 Chemical pre-treatment

The popular chemical pre-treatments, an alternative to physicochemical pre-treatment,

usually involve hydrolytic techniques with acids, alkalis and to a small extent, oxidizing agents.

Acids such as sulfuric, hydrochloric, nitric or phosphoric acid are used individually or in

combination with a physicochemical pre-treatment such as steam explosion to break down

hemicelluloses. Peroxides or alkalis such as sodium hydroxide, ammonia and calcium hydroxide

are used for delignification (removal of lignin; see 1.2.2.2) and hemicellulose removal. Solvents

such as methanol, ethanol and acetone are also used for delignification [1] by extraction.

1.2.2.4 Enzymatic hydrolysis

Three major hydrolysis processes are typically used in ethanol production: dilute acid,

concentrated acid (both discussed next) and enzymatic hydrolysis [31]. Enzymatic hydrolysis has

3 main advantages. Firstly, production of by-products can be controlled and thereby increase

yield efficiency. Secondly, they require milder conditions (e.g. pH, temperature and pressure) and

thirdly they require low energy inputs [36]. There are two distinct disadvantages of enzymatic

hydrolysis. First, the production of enzymes adds to the cost of the overall process. Second, the

sample requires neutralization (to reach the enzymes optimum pH range) after acid/alkaline pre-

treatments which can produce inhibitory salts or add to the sample preparation time (if an ion

exchange resin is used) before analysis and fermentation.

Enzymes are commercially available for cellulose, hemicellulose and pectin polymers. A

class of enzymes called cellulases breaks down cellulose into glucose (Figure 1.2-6). The three

main cellulases are [37]:


• endoglucanase, which binds to, and cleaves the most accessible glycosidic bonds of the

cellulose polymer chain to create smaller cellulose chains (oligomers) and thus increase

the amount of binding ends of points for the other cellulase enzymes (Figure 1.2-6).

• exoglucanase, which binds to the chain ends and breaks off the disaccharide cellobiose

(Figure 1.2-6).

• β-glucosidase, which breaks the cellobiose into glucose monosaccharides (Figure 1.2-6).

Figure 1.2-6: Action of cellulose enzymes [38].

1.2.2.5 Acid hydrolysis

Acid hydrolysis, which predates enzymatic hydrolysis, has the advantage of not requiring

feedstock to produce enzymes [1] and is simple and relatively inexpensive to carry out on a small

scale. High strength acids (30-70 %) are used close to room temperature, while much lower

concentrations (≈1 %) can be used when combined with high temperatures (190 °C-220 °C). The

distinct disadvantage is that microbial inhibitors, such as furfural and hydroxymethyl furfural

(HMF), are formed. Ions from certain acids, such as sulfates from sulfuric acid, are also inhibitory

to the fermentation [33, 39]. Microbial inhibitors need to be removed before the fermentation

process. Pyrolysis is another form of physical treatment in which biomass is thermally

decomposed in the absence of oxygen to yield solid, liquid and gas by-products [40].


1.2.2.6 Hydrolysis summary

Hydrolysis is able to liberate monosaccharides and disaccharides out of the fibers but

with difficulty and thus combining different methods is advised. Each hydrolysis technique has

the ability to increase the complexity of the sample matrix. The acid pre-treatment is simple to

carry out on a small scale and it is typically used in combination with enzymatic hydrolysis to

maximize the carbohydrate available for fermentation. The presence of by-products formed by

the acid pre-treatment as well as the presence of enzymes increases difficulty in analysis and thus

a robust analysis technique is required.

1.2.3 Microbial ethanol fermentation

Once the polysaccharides are broken down into monosaccharides and disaccharides,

fermentation can take place. The biochemical pathways of microorganisms produce many

different end products that are desirable to industries. These include pharmaceutical active

ingredients, antibiotics, flavors and enzymes [41]. The aim of the fermentation is to gain

accumulation of the end product (in this case ethanol) which is achieved by altering growth

conditions and/or available substrates. Fermentation of carbohydrates into ethanol is achieved

through ethanol producing microorganisms called ethanologens. The biochemical pathway used

to produce ethanol varies depending on the organism and influences the growth requirements

and speed of ethanol production. For the fermentation of glucose to ethanol, the yeast

Saccharomyces cerevisiae uses the Embden-Meyerhof-Parnas pathway, [42] whereas the

bacterium Zymomonas mobilis uses the Entner-Doudoroff pathway [43].

1.2.3.1 Theory of ethanol fermentation

For most microorganisms, the initial catabolism of glucose in the presence of oxygen is

shown in Equation 1.2-1.

C6H12O6 + 6O2 → 6CO2 + 6H2O

Equation 1.2-1: Aerobic respiration of glucose [44].

The equation for anaerobic respiration of glucose by an ethanologen, such as the

bacterium Zymomonas mobilis, can be summarized by Equation 1.2-2.

C6H12O6 → 2CO2 + 2C2H5OH

Equation 1.2-2: Anaerobic respiration of glucose by an ethanologen [44].


Every 1 mol of glucose metabolized by the organism produces 2 mol of ethanol, so for

every 100 g of glucose metabolized, 51 g of ethanol is accumulated as a by-product giving a 51 %

theoretical maximum (g ethanol/g glucose). The theoretical maximum would only be reached if

all of the carbon source was utilized for ATP production and if there was no utilization of carbon

for cell replication [45]. In a practical fermentation situation, glucose represents not only the

energy source to drive the cellular endothermic reactions, but also the carbon source that is

converted to the cells’ organic materials for cellular division and growth. Therefore, the more

cellular division occurs, the further from theoretical maximum of conversion the end products fall

[46]. For production of ethanol from a microorganism, the fermentation is flooded with an

excessive amount of substrate, in this case glucose, that forces the organism to exceed its

maximum uptake of oxygen required for the oxidative process and thereby producing an

overflow of other metabolites, such as ethanol, from other biochemical pathways that do not

require oxygen [1]. Therefore, once the growth rate of an ethanologen is minimal, glucose is

channeled to ethanol production and not cell growth. When growth rate is significant, or other

metabolic side products are formed, the theoretical maximum of ethanol yield cannot be

achieved [44].

Pichia stipitis is an ethanologen that has shown great promise, as it has the ability to

ferment hexose sugars as well as the pentose sugars found in hemicellulose (see 1.2.1.3) of

lignocellulosic materials [47, 48]. Xylose, one of the most predominate pentose sugars in

hemicellulose, can be fermented microaerobically into ethanol. The microaerobic fermentation

of xylose by Pichia stipitis is summarized by Equation 1.2-3.

2C5H10O5 + O2→ 4CO2 + 3C2H5OH + H2O

Equation 1.2-3: Microaerobic respiration of xylose by Pichia stipitis [49].

Only 1.5 mol of ethanol is produced per mol of xylose (compared to 2 mol of ethanol per

mol of glucose) with the theoretical mass conversion of 0.46 g of ethanol per g of xylose [50],

although experimentally this has been as high as 0.48 g of ethanol per g of xylose [51]. Organisms

that use microaerobic fermentation, such as Pichia stipitis [52] are theoretically less efficient for

the fermentation of xylose to ethanol by the Pentose Phosphate Pathway (PPP) in comparison to

anaerobic fermentation of xylose which yields 1.67 mol of ethanol per mol of xylose. This gives a

theoretical maximum of 0.51 g of ethanol per g of xylose. This highlights the influence of the type

of substrate on the selection of ethanologen needed to ferment all sugars and the resulting

yields.


1.2.3.2 Ethanologens

Many ethanol producing microorganisms have been discovered and these have been

reviewed by numerous authors [1, 53, 54]. The yeast Saccharomyces cerevisiae is the

microorganism of choice for industrial use [55]. The bacterium Zymomonas mobilis has been

researched in great depth but is yet to be used industrially [53]. The yeast Pichia stipitis

(previously mentioned) has the ability to ferment pentose sugars found in hemicellulose [47, 48].

Clostridium acetobutylicum is another ethanologen that ferments carbohydrates to butanol and

acetate as well as ethanol [56].

1.2.3.3 Saccharomyces cerevisiae

Currently yeasts are the major industrial ethanol-producing microorganisms [55].

Fermentation is achieved via the Embden-Meyerhof-Parnas pathway. When fermenting biomass,

the yeast S. cerevisiae has many distinct advantages over most organisms hence its application in

the last few decades. It ferments glucose to ethanol with virtually no other by-products, other

than CO2 and has a high ethanol tolerance (ability to withstand the solvent effects of ethanol) in

comparison to other yeasts [44]. Like most other yeasts, S. cerevisiae is host to a dual metabolism

for the utilization of glucose. If an adequate supply of oxygen (O2) is present then it can

completely metabolize glucose into CO2 and H2O via aerobic respiration (Equation 1.2-1). When

submerged in a flask with a limited supply of oxygen, S. cerevisiae accumulates ethanol as the

end product as well as CO2 [57] (Equation 1.2-2). The other distinct advantages are rapid

fermentation rates that can be achieved under acidic conditions and its resistance to acetic acid

found in some lignocellulosic hydrolysates. It can rapidly metabolize glucose, fructose, sucrose,

galactose, mannose and maltose, and more gradually metabolize trehalose, isomaltose, raffinose,

maltotriose, ribose and glucuronic acid [1]. However, the most distinctive disadvantage of

Saccharomyces is the narrow substrate range in comparison to other yeasts. It cannot directly

ferment xylose, which is one of the main sugars in lignocellulosic substrate sources [44]. Other

disadvantages include low ethanol tolerance and high biomass production in comparison to

Zymomonas mobilis [58].


1.2.3.4 Zymomonas mobilis

Z. mobilis is a bacterium that achieves fermentation through the Entner-Doudoroff

pathway and produces one mol of ATP per mol of glucose in comparison to yeasts which produce

two mol of ATP per mol glucose [59]. Z. mobilis has many advantages over yeasts including

growth at glucose concentrations above 25 % (w/v) and the ability to produce and tolerate

ethanol up to 13 % (v/v) [60]. In addition, ethanol yields close to the theoretical maximum have

been reported from glucose [53]. The increased yield of ethanol from glucose is due to less

biomass being produced in comparison to yeast [44]. Similar to most yeasts, Zymomonas sp.

cannot utilize pentose sugars for ethanol production. Z. mobilis has many desirable

characteristics of an ethanol producer including being classified as a GRAS organism (Generally

Recognized As Safe), the ability to produce 5-10 % higher ethanol yield per unit of glucose, 2.5

fold higher specific productivity than S. cerevisiae [61], the absence of the Pasteur effect

(presence of oxygen does not inhibit the fermentation) on the glucose consumption rate [62] and

the ability to channel more glucose to ethanol production than to growth of the organism [61].

The use of Z. mobilis as an industrial ethanologen does have some disadvantages, the

most significant being that its substrate range for ethanol fermentation is limited to three sugars:

glucose, fructose, and sucrose [63, 64]. Also, during the fermentation of ethanol, a less acidic

medium is produced by Z. mobilis than by the yeast S. cerevisiae [65]. The more acidic medium

aids in minimizing contamination and the need for sterilization [65]. The expression of the

desirable traits is the cause for extensive research into genetic manipulation of Z. mobilis in order

to increase the substrate utilization. It has been reported that Z. mobilis can be engineered to

utilize pentose sugars by transferring genes from other organisms [64]. The pentose sugars xylose

and arabinose are the main components of hemicellulose material which is derived from plant

waste.

1.2.3.5 Pichia stipitis

Pichia stipitis is a strain of yeast that is in the same family as Saccharomyces, however it

ferments alcohol from xylose utilizing the PPP followed by the glycolytic pathway. It has been

successfully used in the fermentation of a number of hydrolysates from eucalyptus wood [6], red

oak wood [66], wheat straw [47] and sugar bagasse [67]. However limitations of Pichia stipitis

prevent it from being used as an industrial ethanologen. In the presence of hexoses, the

metabolism of xylose is repressed and its ethanol tolerance is much lower than that of Z. mobilis

and S. cerevisiae.


1.2.3.6 Constructed ethanologens

Genetic modification opened the door to the possibility of constructing ethanologens

with all of the desired traits with minimal drawbacks. The most successful approach to creating

these genetically modified organisms (GMOs) was by modifying Z. mobilis and S. cerevisiae to

ferment pentose sugars. One of the first successful recombinants of Z. mobilis was produced [68]

after previous attempts of insertion of single genes failed to produce stable mutations without

selection pressure and low levels of gene expression [53]. Insertion of gene sequences to express

enzymes for the PPP and xylose assimilation produced a Z. mobilis capable of utilizing xylose for

ethanol production [68]. S. cerevisiae has also been successfully genetically modified for xylose

fermentation after numerous failed attempts [69]. This was achieved by inserting genes for the

PPP from P. stipitis. Although in the case of yeast, modification can occur by breeding, it was

shown that these strains accumulated a large amount of xylitol and low amount of ethanol in

comparison to laboratory strains [70]. E. coli has a large substrate range which was also

genetically modified for ethanol production by the insertion of genes from Z. mobilis. Although

successful it still had the disadvantage, shared by some other bacterial ethanologens, of

generating acid by-products[71].

1.2.3.7 Microbial fermentation summary

There are a number of different ethanologens that could ferment the lignocellulosic

substrates. In this work, Zymomonas mobilis was chosen for simple fermentations due to its

higher ethanol yield and specific productivity. Due to the complexity of the fiber substrates Pichia

stipitis was also chosen. Previous work on the fermentation of complex mixtures to ethanol with

these organisms has been successful [48, 72].

1.3 Determination of carbohydrates in complex matrices

Analysis of carbohydrates is essential for determining the substrate composition and

monitoring of the fermentation to ethanol and other end-products. Monitoring of individual

carbohydrates during fermentation also provides an understanding of the bioconversion process,

e.g. when an ethanologen has switched carbohydrates. Analysis of carbohydrates can either be

by chemical analysis or by separation and detection.


1.3.1 Chemical assays

Chemical methods have the advantage of being fast and inexpensive methods of

carbohydrate analysis.

1.3.1.1 Dinitrosalicylic acid (DNS) assay

The DNS assay detects reducing sugars by oxidizing the aldehyde carbohydrate to a

carboxylic acid with 3,5-dinitrosalicylic acid which in turn is reduced to 3-amino-5-nitrosalicylic

acid.

Figure 1.3-1: (A) Isomerization of glucose between open chain form and cyclic form (D-

Glucopyranose) and (B) reaction of D-Glucose in open form and 3,5-dinitrosalicylic acid to

gluconic acid (2,3,4,5,6-pentahydroxyhexanoic acid) and 3-amino-5-nitrosalicylic acid.

The carbohydrate concentration is determined by the concentration of 3-amino-5-

nitrosalicylic acid which is detected spectrophotometrically at a absorption of 640 nm [73]. The

reducing power of different carbohydrates vary [74] and a non-reducing carbohydrate such as

sucrose cannot be detected by this method. As the reducing power of each saccharide varies, a

different color intensity is observed between both pure solution and mixtures of different

carbohydrates [74, 75]. Additionally any unhydrolyzed hemicellulose may also cause a

discrepancy [76]. The Fehling method also detects carbohydrates based on the aldehyde

functional group [77].

1.3.1.2 Phenol-sulfuric assay

The phenol-sulfuric assay was developed to determine the end-group of polysaccharides

[78] or to be used in conjunction with paper chromatography to study the composition of

polysaccharides [79]. The method uses a reaction of sulfuric acid to dehydrate the carbohydrate

to its furfural derivative (furfural for pentoses, hydroxymethylfurfural for hexoses) which forms a


colored complex with phenol [80]. The absorption of the complex varies for each sugar between

460 and 490 nm [79, 81] which creates an error of up to 25 % even if no pentose or hexuronic

acids are present [82].

1.3.1.3 Chemical assays summary

Although chemical assays are inexpensive and simple, they cannot distinguish between

individual types of carbohydrates. As highlighted in 1.2.3.1, the composition of the lignocellulosic

fiber is required to select the appropriate ethanologen and conditions. Thus, these methods were

not appropriate for this PhD work. A method that can identify and quantify at least the different

hexoses and pentoses, which were previously mentioned (section 1.2.1), was required.

1.3.2 Separation methods

Separation coupled to detection provides more accuracy for carbohydrate analysis than

the traditional chemical methods. Carbohydrates can be separated by a variety of methods either

in their natural state or after derivatization or complexation. Chromatography is the most

common type of separation technique for mixtures. In chromatography, the sample is immersed

in the mobile phase which brings it through a stationary phase and analytes are generally

separated by their interaction with the stationary phase. The detection of carbohydrates is much

more challenging than the chemical methods as carbohydrates do not naturally absorb UV light

above 190 nm. A detection that is selective (only detecting analytes of interest) for carbohydrates

is desirable. The repeatability (precision, ability to yield a consistent value on one system) and

reproducibility (precision, the ability to yield a consistent value between systems or operators

[83]) depend on the separation method.

1.3.2.1 Gas Chromatography (GC)

GC separates analytes by the interaction with a stationary phase, or with a liquid phase

on an inert solid support (the latter also called Gas-Liquid Chromatography or GLC) [84]. The

analytes are carried by a gaseous mobile phase (or carrier gas). GC requires analytes to be

volatile, which is ideal for ethanol quantification. However carbohydrates need to be derivatized

to more volatile forms such as their alditol acetate derivatives [60, 85, 86], trifluroacetate

derivatives [87], or others [88, 89]. The goal of derivatization is to reduce the boiling point by

reducing intermolecular hydrogen bonding. Once derivatized, carbohydrates can be vaporized

without degradation. One example, the derivatization of glucose to its alditol derivative has 3

main steps [90]. First, the open form of glucose is reduced to its alditol (sorbitol) by sodium


borohydride. Second, the resulting borate is then removed to avoid interference with the

derivatization and third, the alditol is acetylated with acetic anhydride to the alditol acetate

derivative. This reduces hydrogen bonding thus decreasing the boiling point and interactions with

other components of the sample [90]. After separation the analytes are detected by Flame

Ionization Detection (FID) or mass spectrometry (MS).

Figure 1.3-2: Derivatization of glucose to its alditol acetate by acetic anhydride adapted from

[90].

1.3.2.2 High Performance Liquid Chromatography (HPLC)

HPLC is one of the most popular techniques for the analysis of carbohydrates. Unlike GC,

HPLC can separate carbohydrates without derivatization (no need to volatilize the sugar). Reverse

Phase (RP-HPLC) is one of the most common HPLC methods as it separates analytes based on

their hydrophobicity. The stationary phase is non-polar and inert, commonly an octadecyl carbon

chain (C18) bound to silica. The mobile phase is a polar organic solvent or buffer with water. RP-

HPLC requires derivatization for both the separation and selective detection [91] of

carbohydrates, as they are hydrophilic and thus do not interact with the hydrophobic stationary

phase. Underivatized carbohydrates can be separated by a number of modes other than RP-HPLC

[92], however these modes require a universal detector such as a refractive index (RI) detector

that will also detect other compounds such as those from the hydrolysis of lignin or other by-

products.

Hydrophilic Interaction Liquid Chromatography (HILIC) separates carbohydrates by their

interaction with an amino-abundant stationary phase [93]. A hydrophobic mobile phase, such as

acetonitrile, promotes interaction with the stationary phase. Smaller carbohydrates, such as

pentoses, have less interaction with the stationary phase and thus elute first, followed by

hexoses, disaccharides and oligosaccharides, with the retention time increasing with the degree

of polymerization. Samples require dilution in the polar organic mobile phase which can cause

precipitation of polysaccharides and proteins in the sample before injection. Sample pre-

treatment, such as Solid Phase Extraction (SPE), increases recovery and resolution on this column

[94]. HILIC and the DNS assay were compared on different lignocellulosic hydrolysates [76]. The

results showed a discrepancy between the two methods with the amount of sugars quantified by


HILIC up to 73 % lower than the one quantified with the DNS assay. The authors suggested that

oligomers of 2 to 3 units, which were separated and detected by HPLC (and not originally

counted), may have caused the over-estimation with the DNS assay. The difference in reducing

power of different sugars was not considered.

Calcium and lead form resins can separate carbohydrates by ligand exchange with a

contribution of size exclusion [95-97]. The saccharide forms a complex with the metal in the

stationary phase slowing down its migration. The separation can be improved by altering the

metal, however this also influences the pore size, the size and strength of the metal-saccharide

complex as well as the mutarotation of the saccharide. The benefit of these columns is that water

is the mobile phase. The sample preparation however, requires some clean up to remove

compounds (such as salts produced by the neutralization before enzymatic hydrolysis; see

1.2.2.4) that can interact and alter the stationary phase resulting in the loss of separation.

Samples of non-starch polysaccharides were analyzed by GC after their derivatization to alditol

acetates as well as HPLC with a lead based ligand exchange column [98]. Although the

determination of individual sugars was significantly different between techniques, with arabinose

being higher on HPLC, the determination of total sugars was not.

Hydrogen form resin can also separate carbohydrates as well as organic acids and

alcohols [99] without any sample pre-treatment. The mobile phase of sulfuric acid ensures the

stationary phase is replenished during analysis. The drawback of this column is that, contrary to

separation on the lead form resins, the common fiber sugars, galactose and xylose, are not

resolved [100]. There have been studies on sample recovery with this column, with one study

claiming recovery as low as 80.3 %, 85.7 % and 90.1 % for lactose, galactose and glucose

respectively [101].

1.3.2.3 High Performance Anion Exchange Chromatography (HPAEC)

HPAEC is a mode of ion chromatography that separates negatively charged analytes

based on their affinity with the positively charged stationary phase. Carbohydrates are negatively

charged by a basic mobile phase such as a solution of sodium hydroxide and thus compete with

hydroxide for the positively charged binding sites on the stationary phase (Figure 1.3-3). The

carbohydrate’s affinity for the stationary phase increases with the charge which is determined by

the pKa of the carbohydrate: the lower the pKa the longer the retention time. Weakly charged

carbohydrates (high pKa) such as sugar alcohols, elute first while the most acidic carbohydrates

(low pKa) such as mannose elute later. Between separations, the column is cleared of all residual


compounds from the matrix by increasing the hydroxide concentration. The excess hydroxide

increases competition with the residual compounds for the cationic sites of the stationary phase.

Residual compounds may be peptides, oligosaccharides and polysaccharides, which may are

present in lignocellulosic fiber samples.

Figure 1.3-3: Movement of sodium hydroxide and glucose along the pellicular anion exchange

resin (adapted from [102, 103]).

The detection commonly used for the determination of carbohydrates with HPAEC is

Pulsed Amperometric Detection (PAD). It employs an oxidation reaction between an electrode,

usually of gold (Au), and the oxidizing groups of the carbohydrate in alkaline media [104]. PAD is

considered to be superior to RI detection as it has an increased sensitivity and selectivity.

Sensitivity is increased as only electron-donor functional groups at the set voltage are detected

(carbohydrates have numerous electron-donor functional groups) [105]. Detection selectivity is

increased as neutral and cationic species are not detected [105]. A drawback for complex

samples is that amino acids, peptides and organic acids all give a positive reading, therefore the

detection of carbohydrates with PAD is not selective of amino acids, peptides and organic acids

also present in lignocellulosic fiber samples [106]. HPAEC, after SPE, and GC have been compared

for the analysis of acid hydrolyzed lignocellulose from wood and the methods were considered to

be in excellent agreement [107].


1.3.2.4 Capillary Electrophoresis (CE) of carbohydrates

1.3.2.4.1 Theory of CE

Electrophoresis is a method of separating charged analytes based on their movement

though a solution under the influence of an electric field [108, 109]. The analyte can be passed

through a gel immersed in an electrolyte (gel electrophoresis) or passed through a free solution

in a tube. Capillary Electrophoresis (CE) is carried out in a capillary (typically with a 25-75 µm

internal diameter) with either gel (capillary gel electrophoresis) or free solution (free solution

capillary electrophoresis/capillary zone electrophoresis). Working with a smaller volume

increases the efficacy of cooling thus limiting Joule heating (or the Joule effect) in CE. The

solution moves with the flow of ions (explained in the next paragraph) and analytes are

separated and identified based on the difference of migration though the solution. Additionally

the separation can occur in the presence of micelles (Micellar Electrokinetic

Chromatography/MEKC).

Typically, fused-silica capillaries are used in free solution capillary electrophoresis. The

surface of the fused silica capillary is typically negatively charged (conditioned) by flushing with a

strong alkaline solution such as 1 M sodium hydroxide. This also aids in the cleaning of the

capillary. The capillary is filled with a BackGround Electrolyte (BGE). Due to the negative charge of

the capillary surface, the cations adsorb along its surface. For example, if the BGE was NaOH then

the hydrated sodium ions would adsorb along the capillary surface. When the electric field is

applied, the cations migrate along the capillary wall towards the cathode. The flow of water

hydrating the ions creates an electro-osmotic flow (EOF) (also known as the electro-osmotic

velocity), marked by any uncharged molecule. The velocity of the analyte (know as it

electrophoretic velocity) is affected by the analytes’ charge and the strength of that charge. The

analyte migrates faster than the EOF if the charge is the opposite to the charge of the capillary

surface and slower than the EOF if the charge is the same. The latter is named a counter-EOF

separation. The sum of the EOF and the analytes’ electrophoretic velocity give its apparent

velocity.


Figure 1.3-4: Counter-EOF separation in free solution capillary electrophoresis.

In Figure 1.3-4, the analyte (glucose) has an electrophoretic velocity towards the anode,

opposite to the EOF, giving a counter-EOF separation. The difference between the EOF and the

electrophoretic velocity of glucose gives an apparent velocity slower than the EOF (Equation 1.3-

1).

vep = vapp − veof

Equation 1.3-1: Relationship between apparent velocity (vapp), electroosmotic velocity (veof) and

electrophoretic velocity (vep).

The analytes’ apparent velocity and the velocity of the EOF are calculated by Equation

1.3-2.

v = 𝐿𝐿d𝑡𝑡

Equation 1.3-2: Calculation of the velocity of the EOF (veof) and an analytes’ apparent velocity

(vapp), where ‘v’ stands for either ‘vapp’ or ‘veof’. Where ‘Ld’ is the length to the detection window

(or effective length) and ‘t’ is the time the analyte or EOF marker is detected.

The electrophoretic mobility, the fundamental parameter of capillary electrophoresis, is

the proportionality constant between electric field and velocity (Equation 1.3-3).

µep �m2

V ∙ s�=

vep (m/s)𝐸𝐸 (V/m)

Equation 1.3-3: Relationship between an analytes’ electrophoretic mobility ‘µep’, its ionic velocity

‘v’ and the electric field ‘E’.


The field strength is proportional to the length of the capillary and the voltage (Equation

1.3-4), if either is altered, the field strength alters.

𝐸𝐸 =𝑉𝑉𝐿𝐿t

Equation 1.3-4: Calculation of the electric field strength where ‘Lt’ is the total length of the

capillary and ‘V’ is the voltage.

In Equation 1.3-3, substituting the value for ‘E’ by Equation 1.3-4 and the value for ‘vep’ by

Equation 1.3-1 with the values for vapp’ and ‘veof’ by Equation 1.3-2, the following is obtained

[110]:

µep =𝐿𝐿d ∙ 𝐿𝐿t𝑉𝑉

�1𝑡𝑡m

−1𝑡𝑡eo

�

Equation 1.3-5: Formula used to calculate electrophoretic mobility ‘µep’ of an analyte.

In this equation, ‘tm’ is the migration time of the analyte and ‘teo’ is the migration time of

the EOF marker. The electrophoretic mobility is proportional to the charge-to-friction ratio; the

friction is assumed to be hydrodynamic (Equation 1.3-6) in the case of small molecules and

therefore the separation is based on charge-to-size ratio. Thus electrophoretic mobility is

governed by Stokes law where ‘q’ is the effective charge, ‘r’ is the ionic radius and ‘η’ represents

the viscosity of the solution [111].

𝑚𝑚ep =𝑞𝑞

6π𝜂𝜂𝜂𝜂

Equation 1.3-6: Stokes law governing electrophoretic mobility.

1.3.2.4.2 Application of CE to carbohydrate analysis

The analyte must be charged for separation to take place. The simplest way to charge a

carbohydrate is to use a BGE with a pH above the pKa of the carbohydrates. The pKa of the most

common carbohydrates and sugar alcohols is 12 to 12.5 and 13 to 14 respectively [112-114]

(Table 1.3-1). Thus the most commonly used BGE have a pH of 12-13.1 [112-117]. The least

charged carbohydrates (sugar alcohols) migrate just after the EOF due to the weaker charge,

followed by disaccharides, then monosaccharides. Hexoses migrate faster than pentoses [113].

Carboxylates can also be separated from samples in this high pH BGE, however due to the

relatively low pKa compared to the carbohydrates, their migration is much slower [115]. CE has


also shown to be useful for the separation of polysaccharides such as gellan gums [118], chitosan

[119] and pectin [28].

Table 1.3-1: The structure and pKa of some monosaccharides, disaccharides and sugar alcohols.

Molecule Structure pKa

(25°C)

Molecule Structure pKa

(25°C)

Galactose

OOH

OH

OHOH

HO

12.35

[120]

Sucrose

O

OH

OH

OH

HO

OHO OH

OHO

OHHO

12.51

[120]

Glucose

O

OH

OH

OH

HO

OH

12.35

[120]

Xylitol HO OH

OHOH

OH

13.7

[112]

Rhamnose O

OHOH

OH OH

CH3

NA Arabitol HO OH

OHOH

OH

NA

Mannose

OOHOH

OH

HO

OH

12.08

[120]

Lactose O

OH

OH

O

HO

OHO

OH

OHOH

HO

11.98

[120]

Fructose OOH

OHOH OH

OH

12.03

[120]

Xylose O

OH

OH

OH

OH

12.29

[120]

Arabinose OOHOH

OHOH

12.43

[120]


Separation of carbohydrates can also be achieved via complexation with a metal

compound such as borate leading to separation and quantification with good repeatability and

recovery [121]. The sugar-borate complex has a much lower pKa and can be charged in a BGE

with a pH of 9.2 [122, 123]. The electrophoretic mobility of each complex varies based on the

isomer and the position of the vicinal hydroxyl groups. Unlike the separation of native

carbohydrates, the complex has an UV absorption around 195 nm [122]. The Limit of Detection

(LOD) for carbohydrate-borate complexes, to our knowledge, has not been published, however it

is considered to suffer from poor sensitivity [112]. LOD is improved by derivatization of the

carbohydrates before separation of their borate complexes (Table 1.3-2).

MEKC is a modification of the classical CE method, where the analytes are separated by

their interaction with micelles (pseudo-stationary phase). Micelles migrate much slower than the

EOF. Analytes that do not interact with the micelles migrate with the EOF, followed by the

analytes that have some interaction. Analytes that strongly interact and any analytes that

completely interact with the micelles migrate at the same time as the micelles. As the separation

is based on interaction with micelles instead of size-to-charge ratio, the pH of the buffer is close

to neutral. MEKC of carbohydrates after derivatization can be achieved in less than 6 min [124].

The drawback for MEKC is that it requires the use of surfactants such as sodium dodecyl sulfate

(SDS) which interact with proteins and lipids present in complex matrices.

The detection of underivatized or complexed carbohydrates can be achieved by a

number of different methods. Indirect UV detection of carbohydrates (without derivatization or

complexation) is achieved by the addition of a UV absorbing molecule to the background, such as

sorbate (sorbic acid) [115] or 2,6-pyridinedicarboxylic acid [114]. The UV absorbing molecule

takes on the role of the co-ion in the BGE and is displaced in the presence of the analyte [125]

(Figure 1.3-5A). The displacement is detected as a negative peak (1.3-4B). Indirect detection is

considered to be one of the least sensitive detection methods for carbohydrates (Table 1.3-2).


Figure 1.3-5: The theory of indirect detection represented in the capillary (A) where lowering of

the concentration of a UV-absorbing co-ion (Black circle) by the analyte (Purple circle) leads to a

negative peak (B).

PAD can be hyphenated to CE, [126] although this is not commercially available. As

mentioned in 1.3.2.3, PAD is considered to be sensitive and selective. More recently, contactless

conductivity detection (C4D or CCD) was also developed for carbohydrate analysis. The detector

measures the conductivity of the BGE with and without the presence of the analyte. A BGE with

an ion that has a large difference in charge to the analyte is desired to give the best sensitivity

[127]. Although C4D has one of the lowest published LOD values in the case of carbohydrates

(Table 1.3-2), the use of the C4D detector limits the concentration of the BGE [128]. Only a

maximum NaOH concentration of 100 mM can be used, when much higher concentrations are

considered to be optimal for separation [113, 116].

Recently, direct UV absorption of carbohydrates at 270 nm has been reported [113]. This

novel detection method, although not well understood, requires no derivatization or

complexation. The method is not as sensitive as the derivatization methods or than PAD or C4D,

however detection is achieved with a commercially available diode array detector (DAD).


Table 1.3-2: Comparison of various detection methods for carbohydrates in CE.

Separation Method Derivatization Detection LOD of glucose (mg·L-1)

Reference

MEKC 4-aminobenzonitrile UV-284 nm 0.054

[124]

Borate complexation 1-phenyl-3-methyl-5-pyrazolone

UV-245 nm [129]

0.14 (Mannose)*

[121]

Borate complexation None UV-200 nm NA** [122, 123] High pH None Contactless

conductivity 0.11 5.6

[128] [127]

High pH None Indirect 13

[114]

High pH None PAD 0.36

[130]

High pH – Sodium phosphate buffer

None Direct UV 3.6

[131]

High pH – Sodium hydroxide

None Direct UV 7.2

[112]

* LOD of glucose not given **not stated to the best of our knowledge but “suffers from poor sensitivity” [112].

The direct UV detection was originally theorized to be the result of enediolate formation

[113] (Figure 1.3-6). It was later noted that such as a reaction scheme would not be possible with

the non-reducing carbohydrate sucrose and that a photo-oxidation reaction that takes place in

the detection window [112] (Figure 1.3-7) was more likely.

Figure 1.3-6: Possible mechanism for direct UV detection of carbohydrates in CE by enediolate

formation (adapted from [113]).


Figure 1.3-7: Possible mechanism for direct UV detection of carbohydrates in CE by UV initiated

photo-oxidation (adapted from [112]).

CE with direct UV detection has been applied to the analysis of wood hydrolysates [132]

as well as forensic, pharmaceutical and beverage samples [133]. CE with direct UV detection was

compared to HPAEC for the analysis of wood hydrolysates [132]. Although the values for different

hydrolysates were in good agreement between the two methods, the precision of quantification

was much lower with CE. Similar results were found in a comparison between CE and ligand

exchange chromatography on similar samples [131]. It is worth noting that neither CE methods

included an internal standard. The potential of direct UV detection as a simple alternative for

determination of carbohydrates at non-trace concentrations is yet to be fully realized.

1.3.3 Determination of carbohydrates in complex matrices summary

Methods based on separation are more suited to analysis of carbohydrates in complex

samples than chemical methods. Determination of individual carbohydrates in a mixture provides

more detail of lignocellulose carbohydrate composition than chemical methods. This detail is

required to determine the optimum ethanologen (see 1.2.3.1). From the separation methods

reviewed, free solution CE has the ability to separate all the carbohydrates of interest with a

simple detection technique. CE with direct UV detection has the advantage of neither requiring

derivatization unlike GC and RP-HPLC nor sample clean-up or filtration unlike HPAEC and HPLC-

RID. Thus it has potential to efficiently analyze lignocellulosic fibers and their fermentation. The

drawback to this method was the lack of understanding of the direct UV detection of

carbohydrates. For continued development and application of this detection, an adequate level

of understanding must be reached.


1.4 Determination of ethanol

Determination of ethanol as well as carbohydrates by the same method would be

advantageous. Ethanol can be determined by a number of the separation methods already listed

above. The typical method for the determination of volatile compounds such as ethanol is GC-FID

[134]. However the analysis of carbohydrates by GC requires derivatization (see 1.3.2.1). HPLC on

a hydrogen form resin can separate carbohydrates without derivatization and alcohols [99] hence

it is a popular choice among fermentation scientists. However this column does not resolve

galactose and xylose which are both present in lignocellulosic fiber. HPAEC can determine

ethanol and carbohydrates in standards with a MicrobeadTM pellicular resin [135, 136] however

other compounds present in the matrix of the fermentation sample co-elute with the ethanol.

Determination of ethanol is possible with some modes of CE. Ethanol as well as other

solvents have been determined by MEKC with indirect detection [137] however it requires the

use of the surfactant SDS that may interact with proteins and lipids that are present in

lignocellulosic fermentations. CE with PAD [126] or indirect UV detection [138] can also detect

ethanol however no quantification was carried out in this study. No single method can determine

all the fiber sugars of interest as well as ethanol. This is an area that was explored further in this

PhD.

1.5 PhD project aim and objectives

Based on the literature review, the field of bioethanol research lacked simple and robust

analytical methods for carbohydrate and ethanol analysis in lignocellulosic fiber and fermentation

samples. A method was required that was able to separate the various saccharides found in

lignocellulose as well as robust enough to not be affected by the various by-products of the

lignocellulosic hydrolysis and fermentation media. There were two aims of this PhD work, each

with their own research questions:

1) To determine the potential of free solution capillary electrophoresis with direct UV

detection to analyze samples with complex matrices (lignocellulosic hydrolysates and

bioethanol fermentation products)

o Is CE with direct UV detection an adequate method for fiber analysis in

comparison to HPLC methods? This was investigated by comparing the

separation (by resolution) of common fiber sugars with CE to popular HPLC

modes and by comparing the quantification of an acid treated fiber sample

between CE and HPLC with a hydrogen form resin.


o Can CE be used for monitoring of lignocellulosic fermentations? This was

investigated by analyzing the influence of the BGE on the separation (by

electrophoretic mobility, resolution and time of separation) and by comparing

the quantification of carbohydrates in fermentation samples to HPAEC and HPLC.

2) To improve the understanding of the direct UV detection.

o Is the underlying cause of the direct UV detection enediolate formation or a

photo-oxidation reaction? This was investigated by studying the detection with

novel 1H NMR and CE experiments.

o What is the reaction pathway that makes the photo-oxidation detection

possible? This was investigated by studying the mechanism by quantum

mechanics calculations and the end products by novel 13C NMR experiments.

o Can the photo-oxidation detection be used to quantify ethanol in fermentation

samples? This was investigated by novel CE experiments with pressure

mobilization and 13C NMR experiments with samples of vodka and fermentation

broth.

The long term goal was to provide the field with a method that is simple to implement,

with sufficient sensitivity (not trace analysis) and robust with a range of complex lignocellulosic

samples after hydrolysis and during fermentation. This method was sought to facilitate future

research into the use of non-food based plants for the production of bioethanol.


2. Publication “Simple and robust determination of monosaccharides in plant fibers in

complex mixtures by capillary electrophoresis and high performance liquid chromatography”

2

2.1 Contribution to PhD work, field, and candidates personal and professional development

2.1.1 Advantages and limitations of CE with direct UV detection and HPLC for carbohydrate

determination in lignocellulosic plant fiber

This work initially began with an investigation of the carbohydrate content of non-food

plants, adaptable to Australian marginal lands, for bioethanol production. These included Atriplex

nummularia, Aloe vera, Agave attenuate and Opuntia ficus-indica. The plants were chosen due to

their low lignin content and ability to grow in harsh environments. The lignocellulosic fiber of

these plants was acid hydrolyzed before carbohydrate determination. The acid treatment was

used as it was simple to carry out on a small scale. The presence of by-products formed by the

acid treatment increases difficulty in analysis and thus a robust analysis technique is required.

Chemical assays are fast and inexpensive for carbohydrate analysis. The phenol-sulfuric assay was

attempted for the analysis of carbohydrates (not published). The assay could detect but not

accurately quantify (Section 1.3.1.2) the carbohydrates. Following the research trends, HPLC was

attempted. Two key issues arose after utilizing HPLC to analyze complex hydrolysis samples:

inadequate resolution and/or robustness. Alternative methods were reviewed in the literature

(as discussed in 1.3) and CE with direct UV detection seemed a viable analysis technique. CE has

many benefits over HPLC for the analysis of carbohydrates in plant fiber including being more

robust, and based on previous work [132], resolving all main fiber monosaccharides. The 1st

publication compared the separation of underivatized carbohydrates in CE to the common HPLC

modes. When the CE separation was found to be superior, a quantitative study was undertaken.

CE gave consistently higher quantification values than HPLC leading to an investigation of the

mechanism of detection. The lower quantification values of individual sugars may indicate a

recovery or accuracy issue on the hydrogen form HPLC column. This has only once, to the best of

our knowledge, been noted in the literature [101]. While it is established that carbohydrates do

not absorb UV above 200 nm, carbohydrates were unexpectedly observed by direct UV detection

at ≈270 nm. The main limitation of the CE separation method in this PhD project was the minimal

understanding of the detection technique. At the time of this publication there were two

competing theories by Rovio et al. (2007) [113] and Sarazin et al. (2011) [112] (as discussed in


1.3.2.4) explaining the mechanism of the detection. To determine which theory was correct, 1H

NMR (Nuclear Magnetic Resonance) and CE experiments were designed and carried out. 1H NMR

was used to identify the changes in chemical structures in a solution of glucose in 130 mM NaOH

D2O.

2.1.2 Theory of NMR spectroscopy

NMR spectroscopy is a type of absorption spectroscopy where an atom’s nucleus absorbs

electromagnetic radiation in the radio frequency (rf) range under appropriate conditions in a

magnetic field [139]. NMR can be used for either investigating molecular dynamics or for

structure elucidation. It is used in this work for the latter. For 1H NMR (proton NMR) spectroscopy

signals are observed at chemical shifts (δ) between 0 and 10 ppm. The signal frequencies are

acquired in Hz and then the chemical shifts are calculated as their relative difference to the

Larmor frequency of the investigated nucleus in the main magnetic field. The values for the

chemical shifts are independent from the magnetic field. The chemical shift relates to the

hydrogen position in the molecule relative to carbon, oxygen, hydrogen and other atoms (Figure

2.1-1). The chemical shift is also influenced by the solvent, hence in this study chemical shifts of

standard compounds were experimentally measured in the presence of NaOH. The intensity of

the observed peak is proportional to the concentration of the hydrogen of the corresponding

functional group in the sample, provided the delay between scans is sufficient to ensure

complete relaxation (complete return to the equilibrium state). Several scans are accumulated in

order to average out noise and acquire enough signal with a sufficient signal-to-noise ratio.

Figure 2.1-1: Ranges of 1H chemical shifts for different functional groups, adapted from [139].


13C NMR spectroscopy is similar to 1H NMR spectroscopy except the isotope 13C is

measured [140]. Since the 13C isotope represents only ≈ 1.1 % of all carbons in natural

abundance, the sensitivity of 13C NMR spectroscopy is limited. By using fully labeled 13C glucose

(as done in the 2nd and 3rd publication) sensitivity can be increased by a factor of 91. Signals are

observed at chemical shifts (δ) between 0 and 200 ppm in 13C NMR spectroscopy (Figure 2.1-2)

resulting in a higher resolution than in 1H NMR spectroscopy. The higher resolution allows for

easier structure elucidation. For example, chemical shifts of carboxylates are in the 160-180 ppm

range whereas chemical shifts of aldehydes are in the 180-200 ppm range (Figure 2.1-1). As with 1H NMR spectroscopy, chemical shift relates to the carbon’s position with respect to other atoms

in the molecule and is affected by the solvent. The intensity of the observed signal is proportional

to the concentration of the corresponding functional groups, provided the delay between scans is

sufficient to ensure complete relaxation. A sequence of different electromagnetic radiation

pulses can manipulate the magnetization to change the signal output. In this study a 135° DEPT

(Distortionless Enhancement by Polarization Transfer) sequence was used where primary and

tertiary carbons have positive signals, secondary carbons have negative signals and quaternary

carbons are not observed [140]. Electron Spin Resonance (ESR) spectroscopy is a technique very

similar to NMR spectroscopy however unpaired electrons are observed, rather than nuclei [141].

The main limitation being that only radicals can be observed by ESR.

Figure 2.1-2: Ranges of 13C chemical shifts for different functional groups, adapted from [140].


2.1.3 Investigation of the direct UV detection

1H NMR was used to investigate the chemical structure of end products from glucose in

130 mM NaOH after UV irradiation due to NMR’s sensitivity and resolution. Consistent with

Sarazin et al. [112] , an in-situ photo-oxidation reaction was the reason behind the UV absorption

of carbohydrates observed at ≈270 nm. This led to an investigation of the reaction pathway

causing this detection. After an in-depth literature search, the pathway could be linked to past

ESR spectroscopy experiments performed by Gilbert et al. [142, 143] on a radical initiated

oxidation pathway. The studies were performed in similar conditions (pH >9.0) as in the CE. The

2nd publication looked at the potential UV absorbing intermediates by comparing each

carbohydrate’s experimental wavelength and absorption intensity to their calculated ones. It

strongly indicated a link between the absorption seen in CE and the free radical pathway

suggested by Gilbert et al. [143]. Due to the success of the 1H NMR spectroscopy experiments in

analyzing end products, NMR spectroscopy was used in the 2nd publication. The end products of

the reaction pathway were investigated by 13C NMR spectroscopy. A sample was generated by

continuously injecting a 13C glucose in D2O with NaOH for 95 hours. It was discovered that the

end products contain carboxylates contrary to the theory of Sarazin et al. [112] which predicted

aldehydes. The successful use of the 13C NMR spectroscopy experiments resulted in its repeated

use in the 3rd publication. Based on this new understanding, sensitivity of the detection was

improved by the use of radical photo-initiators.

The 2 research questions of the 1st publication were: “Is CE with direct UV detection an

adequate method for fiber analysis in comparison to HPLC methods?” and “Is the underlying

cause of the direct UV detection enediolate formation or a photo-oxidation reaction?”

2.1.4 Contribution to my personal development

This publication contributed to my personal development in a number of ways. Aside

from being my first publication in a peer reviewed journal, this work gave me the opportunity to

give an oral presentation at an international conference, the 33rd Australasian Polymer

Symposium (33APS, see “Conference and seminar presentations”). Also I was provided training

on the important analytical techniques of HPLC, CE and 1H NMR (solution state). Professional

development was achieved through my collaboration with Professor Emily Hilder from the

Australian Center for Research On Separation Science (ACROSS) at the University of Tasmania,

Australia (UTas). A part of this collaboration included an invited seminar at ACROSS (see


“Conference and seminar presentations”) chaired by Professor Paul Haddad, fellow of both the

Australian Academy of Science and the Academy of Technological Sciences and Engineering.

This publication had 3 co-authors. The last author, Dr Patrice Castignolles provided the

direction of the publication as well as training and understanding of CE. He also assisted in

forming the collaboration with UTas via the ACROSS network. Dr Marianne Gaborieau provided

assistance with performing 1H NMR as well as discussion to understand the results of the 1H NMR

experiments. Prof. Emily Hilder provided the idea and equipment for irradiating a sample of

glucose external to the CE and analyzing by 1H NMR. In addition Prof. Emily Hilder also organized

the invited seminar at ACROSS in UTas.

I performed all background research, experiments, data acquisition and analysis as well

as writing the first draft of the publication. Initially I had the idea to use HPLC to characterize the

various fiber samples due to its use popularity in the field. However it proved to be inadequate

for the complex fiber sample I was working with. After a literature search and a discussion of

different possibilities, I proposed CE with direct UV detection as potentially the most robust

separation with a simple yet selective detection. I proposed comparing the separations HPLC

mode and CE as well as comparing the quantification. I also developed the experiments in this

publication that determined that enediolate formation was not the cause of the detection

mechanism and that the electric field played a role in enhancing the detection. I selected the

plant Opuntia ficus-indica as it flourishes in Australian semi-arid climates.


2.2 Publication

Simple and robust determination of monosaccharides in plant fibers in complex mixtures

by capillary electrophoresis and high performance liquid chromatography

James D. Oliver a, Marianne Gaborieau b, Emily F. Hilder c, Patrice Castignolles a,*

1 University of Western Sydney (UWS), Australian Centre for Research on Separation Science (ACROSS), School of Science and Health, Locked Bag 1797, Penrith NSW 2751, Australia, [email protected], [email protected]

2 University of Western Sydney (UWS), School of Science and Health, Nanoscale Organisation and Dynamics group, Locked Bag 1797, Penrith NSW 2751, Australia, [email protected]

3 Australian Centre for Research on Separation Science (ACROSS), School of Chemistry, University of Tasmania, Hobart TAS 7001, Australia, [email protected]

* Corresponding author: [email protected]

Abstract:

Carbohydrates partially liberated by acid hydrolysis of plant fiber can be separated by

Hydrophilic Interaction Liquid Chromatography (HILIC), ligand-exchange liquid chromatography or

other forms of LC with ion-exchange columns. However, the robust hydrogen-exchange columns

show co-elution of galactose, xylose and mannose. Free solution capillary electrophoresis (CE)

can be used without derivatization at pH 12.6 and was found to provide a higher resolution of

galactose and xylose than common LC with no sample pre-treatment required, other than

dilution, within 26 min. CE was able to provide resolution higher than 0.79 for all separated

carbohydrates, and the RSDs of determined concentrations lower than 10% for concentrations

above 1.3 g·L-1. A quantitative comparison between CE and HPLC revealed that up to 22% more

carbohydrates are quantified with CE. Direct UV detection in CE of mono- and disaccharides is

unexpectedly possible at 270 nm. NMR analysis shows that alkaline degradation is too slow to

explain this detection. This CE detection sensitivity is increased by the electric field and our CE

and NMR analyses are consistent with a photo-oxidation process.

Keywords: Monosaccharide, Plant fiber, Capillary electrophoresis, Ion-exchange

chromatography, Photo-oxidation


mailto:[email protected]

1. Introduction

Carbohydrates make up most of the living world around us. Their identification and

quantification is generally sought after in many fields such as food and beverage analysis, plant

analysis, fermentation studies and metabolism studies. Different separation techniques for

mono- and disaccharides have been previously compared, mainly on model samples or diluted

samples such as fruit juices or wine. However there is a need for a separation technique that can

characterize “complex” samples with little sample preparation. These complex samples have a

significant variation of carbohydrate type and concentration as well as significant levels of acids,

bases, salts, amino acids and other cell debris. Dilute acid treatment of fiber is routinely used in

biotechnology to break down hemicellulose fiber and exposes cellulose to enzymatic breakdown

[1-7]. The characterization of these important samples is the focus of this manuscript.

Preparation of these samples for analysis, i.e. matrix removal, is tedious and lowers the accuracy.

Carbohydrates can be separated by high performance liquid chromatography (HPLC) using a

number of modes [8]. Ion exchange resins in the calcium or lead form were first shown to

separate carbohydrates without clear mention of the separation mechanism [9,10] which was

later proven to be ligand exchange with a contribution of size exclusion [11]. Separation with

ligand-exchange on ion exchange resins containing cations such as lead or calcium provide the

same order of separation; however, in the case of lead, an improved resolution is observed for

the common fiber sugars xylose and galactose. Separation on a cation-exchange resin in the

hydrogen form with a sulfuric acid mobile phase elutes disaccharides first, followed by hexoses,

pentoses then alcohols. The mechanism of separation for carbohydrates on this column has been

shown by several groups but has not been definitively proven [12]. Separation with amino

columns, as hydrophilic interaction liquid chromatography (HILIC), utilizes hydrophilic

interactions between the sample and the amino-rich resin with pentoses eluting first, followed by

hexoses, disaccharides then oligosaccharides in order of increasing oligomer units. HILIC is better

adapted to small oligomers than monosaccharides [13]. HPLC can also be used in reverse phase

mode (RP-HPLC) after multistep derivatization [14]. Each method has its own issues relating to

co-elution, tedious sample preparation, salt intolerance or acid intolerance leading to incomplete

separation and short column life [15]. Gas chromatography (GC) is also commonly used; however,

it requires multistep derivatization of the carbohydrates [16-18]. RP-HPLC and GC were recently

compared for plant fiber analysis [19], and although the determination of a number of

monosaccharides was accurate within 10 %, rhamnose and galactose were not resolved via HPLC.


CE is used and recognized in both research and industry as a viable technique for the

separation of carbohydrates [20-22]. GC and CE were recently compared for the separation of

hydrolyzed wood samples [23]. Although these methods required derivatization, both

carbohydrates and uronic acids could be determined in the same run with CE. HILIC and CE were

also compared for fruit juice samples for the detection of sucrose, glucose and fructose [24]. Both

methods were not significantly different and showed good repeatability. Pre-column

derivatization is avoided in this work to keep the method simple and robust and ensure no side-

reaction occurs with proteins and lipids of complex samples (we follow as definition of a robust

method “a method that can be applied to analytes in a wide variety of matrices” [25]). GC of

carbohydrates is not possible without derivatization and this may be why it is not currently a

common method to analyze plant fiber degradation products [16-18]. Without derivatization or

complex formation CE cannot separate carbohydrates below pH 9, as expected. Monosaccharides

can be charged, separated and detected in borate buffer since they complex the borate [26,27].

The detection suffers however from a poor sensitivity of the borate complex. Separation can also

be achieved at pH 7.5 with micellar electrokinetic chromatography (MEKC) [27]; however, the

method requires the addition of sodium dodecyl sulfate (SDS) surfactant that also interacts with

proteins and lipids present in complex mixtures. Separation of underivatized sugars in CE is

possible using an electrolyte with a pH above the pKa of the sugars, which is generally above pH

12. On this basis earlier methods demonstrated separations in strongly alkaline electrolytes with

indirect or pulsed amperometric detection [28-31]. CE with indirect UV detection was shown to

be a rapid, repeatable and sensitive method for carbohydrates, and has also shown quantitative

recovery of carbohydrates in similar samples [30,32]. However, the limitations and possible

artifacts of indirect detection or pre-derivatization for complex samples containing complex

carbohydrate mixtures in conjunction with other compounds have never been investigated. More

recently CE has been shown to separate up to 12 different mono-, disaccharides and sugar

alcohols in direct detection without derivatization using a high pH buffer of sodium hydroxide

and sodium phosphate. The direct detection was found to be unexpectedly possible through UV

absorption at 270 nm by Rovio et al. [33] and the method was applied to plant fiber samples with

a maximum concentration of 400 mg⋅L-1 for single sugars [34]. An adapted version of the method

has been applied to forensic, pharmaceutical and beverage samples) [35,36]. While maximizing

the detection sensitivity is an objective for some applications, plant degradation leads to complex

mixtures but with relatively large quantities of monosaccharides, thus detection does not require

the highest sensitivity. Rather for this application we have focused on the development and

critical comparison of approaches to achieve the most robust and simple separation of


carbohydrate mixtures from complex plant samples. The specific objectives were to adapt the

separation of Rovio et al. to achieve high resolution separations at higher concentrations and to

compare the optimized CE method to existing HPLC methods, specifically in terms of

quantification.

2. Materials and methods

2.1. Materials

Water was of MilliQ quality (Millipore, Bedford, MA, USA). Fused-silica capillaries (50 µm

i.d., 360 µm o.d.) were obtained from Polymicro (Phoenix, AZ, USA). Xylose ≥99% was obtained

from Alfa Asear (Ward Hill, MA, USA). Dimethyl sulfoxide (DMSO), D+glucose ≥99.5%,

D+galactose ≥99%, L-rhamnose monohydrate ≥99%, L-arabinose ≥99% and D+cellobiose ≥99%

and acetonitrile (ACN) were obtained from Sigma-Aldrich (Castle Hill, NSW, Australia). Sodium

hydroxide pellets (NaOH), disodium hydrogen phosphate powder (Na2HPO4), lactose, glacial

acetic acid and sulfuric acid were obtained from Univar (Ingleburn, NSW, Australia).

2.2. Plant sample and standard preparation

Three cladodes (flattened paddle shaped stems) of the plant of Opuntia fiscus-indicia

were obtained from the wild in Richmond, NSW, Australia in November 2010. They were

immediately homogenized with water and then centrifuged at 3000 rpm for 30 min to isolate the

fiber. The insoluble fraction was then dried to a constant weight at 75 °C and milled to fit through

a 1 mm sieve and stored in an airtight container until sample preparation. A 5 mL solution of 0 to

4 % (v/v) sulfuric acid was loaded with 5 % (w/v) of dried fiber in a sealed glass tube and heated

to 134 °C for 1 h as done in [1]. The sample was then filtered through a nylon 0.45 µm filter

before analysis. The fiber standard was prepared by measuring 200 mg of each sugar (glucose,

galactose, rhamnose, arabinose, mannose, xylose and cellobiose) in a 200 mL volumetric flask

then filling to the mark with MilliQ water.

2.3. High performance liquid chromatography

All separations were performed on a Shimadzu 10A Series System with a RID-10A

refractive index detector and SPD-M10Avp PDA detector (Shimadzu Scientific Instruments,

Rydalmere, NSW, Australia). The injector was equipped with a 20 µL injection loop and rinsed

with 200 µL of samples before injection. The different column sets were purchased from Bio-Rad

(HPX-87H, HPX-87P and HPX-87C) (Hercules, California, USA) and Supelco (LC-NH2) (Sigma-Aldrich


Castle Hill, NSW, Australia). Experimental conditions are given in Table 2.2-1. Each mobile phase

was vacuum filtered through a 0.45 µm filter before use. Results were integrated via VP class 5.0

software from Shimadzu.

Table 2.2-1: Experimental conditions used in HPLC for the different columns.

Column Resin form Mobile phase Temperature Flow rate

Column RID

HPX-87P Lead Water 80 °C 60 °C 0.6 mL⋅min-1

HPX-87C Calcium Water 80 °C 60 °C 0.6 mL⋅min-1

HPX-87H Hydrogen 0.005 M H2SO4 60 °C 60 °C 0.6 mL⋅min-1

LC-NH2 Amino 75:25 ACN:water 25 °C 40 °C 1.0 mL⋅min-1

2.4. Capillary electrophoresis

Separations were performed on Agilent 7100 or 3D Capillary Electrophoresis systems

(Agilent Technologies, Santa Clara, CA, USA) with a Diode Array Detector monitoring at 200 nm

and 270 nm with a 10 nm bandwidth. The buffer preparation and separation was carried out as

described by Rovio et al. [33]. Typically a capillary with a 60 cm total length (51.5 cm effective

length), was filled with 130 mmol NaOH and 36 mmol of Na2HPO4 and a voltage of 16 kV ramped

up over 2 min. The capillary was pre-treated prior to use by flushing with 1 M NaOH, 0.1 M NaOH

and water for 20 min each. The sample was injected hydrodynamically by applying 34 mbar of

pressure for 4 s (≈55.6 nL according to the Poiseuille law) followed by buffer in the same manner.

Between each run, the capillary was flushed with 10 % (v/v) acetic acid for 5 min followed by

water and then running buffer. After the last injection, the capillary was flushed 1 min with NaOH

1M, 10 min with water and 10 min with air. Dimethyl sulfoxide (DMSO, 1 µL/500 µL) was added

to each sample to mark the electro-osmotic flow (EOF) and 1 g·L-1 of lactose was added as an

internal standard. The EOF was determined at 200 nm. Calibration curves (Figure 2.3-4) were

calculated from 5 concentrations between 0.125 g⋅L-1 and 1 g⋅L-1; each concentration level was

determined from an average of 5 injections with lactose used as an internal standard. All

electropherograms were corrected for the EOF by plotting the intensity against the

electrophoretic mobility (µep) (Equation 2.3-1). Integration was performed on signals at 270 nm

with Origin Pro 8.5 (Northampton, MA, USA).


2.5. NMR spectroscopy

1H nuclear magnetic resonance (NMR) spectra of 1 g⋅L-1 glucose and sucrose in water

with 130 mmol⋅L-1 NaOH were recorded at 25 °C on a Bruker Avance 400 spectrometer (Bruker,

Alexandria, NSW, Australia) operating at 400 MHz for 1H, with a BBO probe, using WATERGATE

water suppression. A 14 µs 90° pulse was used for the first 1H irradiation and 18 µs 180° pulses

were used for WATERGATE; 16 scans were recorded with a 5 s relaxation delay. The chemical

shift scale was externally calibrated with the resonance of 4,4-dimethyl-4-silapentane-1-sulfonic

acid (DSS) at 0 ppm.

1H NMR spectra of glucose samples (1 g⋅L-1 with 130 mmol NaOH in D2O) irradiated with a

HP3DCE deuterium lamp (Agilent Technologies, Part number: 2140-0585) were recorded at room

temperature on a Varian Mercury 2000 spectrometer (Palo Alto, CA, USA) operating at a 1H

Larmor frequency of 300 MHz. A 11 µs 90°pulse was used for 1H irradiation; a 4 s repetition delay

was used and 2 to 64 scans were recorded. The chemical shift scale was externally calibrated with

the resonance of 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS) at 0 ppm.

3. Results and discussion

Plant fiber was partially hydrolyzed and the composition was determined for the first time by

both common liquid chromatography methods as well as free solution capillary electrophoresis

with direct UV detection.

3.1. Comparison of common HPLC separations of monosaccharides and application to fiber

analysis

HPLC is the dominant method for carbohydrate identification and quantification owing to

its relatively high throughput (separation in typically 10-18 min) and ease of use. Ligand exchange

with lead and calcium form resins, LC with hydrogen exchange resins as well as HILIC have

different advantages and drawbacks, but none has led to a clear, robust separation. Fig. 2.3-1

demonstrates the separation of different standards containing common fiber sugars with three

commonly used columns for carbohydrate analysis, with the resolution and sensitivity listed in

Table 2.2-2.

Ligand-exchange separation against calcium (Fig. 2.3-1A and Table 2.2-2) led to the

lowest number of resolved peaks for standards with complete co-elution of galactose, xylose and

rhamnose, as documented previously [37]. The stronger complexation of the carbohydrates with

lead provides higher resolution between all fiber sugars, in particular xylose and galactose (Fig.


2.3-1B and Table 2.2-2), which is of high importance in fiber analysis and fermentation studies.

Although rhamnose still co-elutes with galactose, it is generally only found in trace amounts in

the woody biomass that is typically investigated. The weakness of both columns is their inability

to tolerate salt and acids. Fiber samples, after being pre-treated with sulfuric acid at high

temperatures, require neutralization [38]. This can be achieved with calcium hydroxide or barium

hydroxide due to the low solubility of barium sulfate and calcium sulfate; however, trace

amounts of salts still rapidly displace calcium or lead. The use of hydroxide additives or hydroxide

form resins also adds to the sample preparation. Deashing systems are also available; however,

they significantly add to the cost and the cartridges have short lives. HILIC does provide

resolution between the sugars of interest (Fig. 2.3-1C and Table 2.2-2), however, galactose is still

not baseline resolved from the glucose. In this case ACN needs to be added to the sample prior to

injection to decrease the eluent’s strength which can lead to precipitation of compounds

insoluble in ACN such as polysaccharides.

Table 2.2-2: Resolution values for consecutive peaks on each column and in CE.

Carbohydrate peaks HPLC

CE HPX-87C (Calcium form)

HPX-87P (Lead form)

HPX-87H (Hydrogen form)

LC-NH2 (Amino form)

Glucose galactose 1.19 2.48 0.88 <0.5 1.67 Glucose rhamnose 1.37 2.48 1.66 3.95 0.79 Glucose xylose 0.81 1.35 0.88 2.92 6.83 Glucose arabinose 3.22 4.14 1.89 1.62 3.39 Glucose mannose NA 4.32 NA NA 1.71 Xylose galactose <0.5 1.04 <0.5 2.61 8.05 Xylose rhamnose <0.5 1.04 0.69 1.07 6.05 Xylose arabinose 1.27 2.56 1.01 0.56 3.86 Xylose mannose NA 2.95 NA NA 5.76 Arabinose galactose 1.92 1.53 1.01 1.63 4.92 Arabinose rhamnose 1.56 1.53 <0.5 1.35 2.55 Arabinose mannose NA 0.74 NA NA 1.93 Rhamnose mannose NA 2.06 NA NA 0.83 Rhamnose galactose <0.5 <0.5 0.69 3.39 2.40 Galactose mannose NA 2.06 NA NA 3.42 Sensitivity SNRa 11,000 1,500 10,000 2,100 1,800

‘NA’ denotes that one of the carbohydrate was not injected a Signal-to-noise ratios (SNR) are indicated for glucose with 2 significant digits.


One of the most widely used columns in the literature is the hydrogen exchange HPX-

87H, due to its tolerance to low pH samples and its ability to detect acids, alcohols, as well as

mono- and disaccharides [12]. Investigations of wood acid hydrolysates have been carried out on

this column as the acidity of the sample can be tolerated by the column negating the need for a

tedious neutralization step [38]. The issues with this approach are the co-elution of galactose and

mannose, which are only trace amounts in wood, with xylose, the acidic mobile phase degrading

some disaccharides such as sucrose and the co-elution of acids, ketones and aldoses from the

plant degradation with the carbohydrates [12]. In the fiber sample (Fig. 2.2-1-A), peak 9 elutes

close to cellobiose, however its UV spectrum reveals it is not cellobiose. In the case of more

complex carbohydrate mixtures the HPX-87H column gives robust separations, but still co-elution

(Fig. 2.2-1 and Table 2.2-2).

Figure 2.2-1: Separation of a fiber sample (A) and mixture of standard (B) and using HPX-87H

column 1: cellobiose, 2: glucose, 3: galactose, 4: xylose, 5: rhamnose, 6: arabinose, 7: void

volume, 8: galacturonic acid, 9: unknown.


3.2. Separation of monosaccharides in fiber samples using capillary electrophoresis

The use of CE provides an alternative separation technique with a lower running cost. To

run 50 samples via HPLC, the cost of the column, guard column with holder is larger than that of

the running cost of capillary electrophoresis for the same purpose for a higher throughput of the

latter (see Table 2.3-3). The same standard and sample shown in Fig. 2.2-1 were separated via CE

with direct detection and without any pre-derivatization (Fig. 2.2-2). The sample did not need

neutralization before injection saving preparation time and cost. All the common fiber sugars are

resolved with baseline resolution maintained for standard concentrations up to 250 mg.L-1, after

which glucose and rhamnose start to lose baseline resolution. The peaks can be identified by the

electrophoretic mobility and the precision of the mobility is greatly improved by the use of an

electro-osmotic flow marker as well as an internal standard (see Table 2.3-2). The addition of an

internal standard improved also the repeatability of the peak area which this study showed to be

better than published values [34]. The calibration curves obtained with different capillaries,

reprepared standard solutions and a different CE machine exhibit good reproducibility (Fig. 2.3-4

and Table 2.2-3). One of the most significant benefits is the flexibility of the separation in

comparison to all HPLC modes compared. Capillary length, buffer concentration and type can be

optimized with minimal cost and time in CE compared to optimization of the eluent

concentration and nature, and stationary phase in HPLC.


Figure 2.2-2: Fiber standard 250 m·gL-1 (A) and sample (B) plotted with electrophoretic mobility

and migration time (C-i). Separation by CE via Rovio et al.’s method [33]. 1: Cellobiose, 3:

galactose, 2: glucose, 5: rhamnose, 4: arabinose, 6: xylose and corresponding UV absorption

spectra (C-ii) for glucose (dashed line), xylose (solid line) and arabinose (dotted line).


The electrophoretic mobility is not only a parameter to enable the quantitative

determination of the sugars but it also characterizes the molecule and relates to its structure [39-

41]. The electrophoretic mobility of each sugar measured in this work (Table 2.2-3 as well as

reproduced data in Table 2.3-1) was lower than that published by Rovio et al. [33] and by Gürel et

al. [30] (Table 2.3-2). The latter used methanol (direct detection) and water (indirect detection)

as electro-osmotic flow markers, respectively. Water and methanol have a low but significant

effective charge at pH 12 and should not be used as electro-osmotic flow markers in these

conditions. Consistent with this we found that when DMSO was used to mark the electro-osmotic

flow, methanol had a non-zero electrophoretic mobility (Fig. 2.3-3). The ramping of the

separation voltage in the first 2 min from 0 to 16 kV of the run was taken into account in this

calculation.

Table 2.2-3: Electrophoretic mobility (µep) with its relative standard deviation (RSD) and

calibration of response at 270 nm with its correlation coefficient R2, for the sugars in our fiber

standard (capillary of 66 cm total length).

Sugar µep (10-8 m2 V-1s -1) RSD (%) Calibration (270 nm)b R2

Lactose a -1.260

Cellobiose -1.343 0.39 0.8191 x + 0.0672 0.9969

Galactose -1.439 0.41 1.2634 x + 0.0367 0.9958

Glucose -1.518 0.45 0.9458 x + 0.0327 0.9954

Rhamnose -1.577 0.63 0.4936 x + 0.0418 0.9981

Mannose -1.604 0.40 0.8049 x + 0.0169 0.9996

Arabinose -1.621 0.57 0.7186 x + 0.0095 0.9984

Xylose -1.754 0.40 0.4391 x + 0.0101 0.9996

a Lactose was used as an internal standard b When the internal standard is equal to 1 and ‘x’ is the sugar concentration


The aim of this work was to quantify carbohydrates in the fiber sample by CE and HPLC

with the determined sugar concentrations compared in Table 2.2-4 (and Table 2.3-4). Separation

and quantification of three sugars had been compared for HILIC and CE with indirect detection

[24]. The repeatability showed a higher relative standard deviation (RSD) than our own results

since no internal standard and limited flushes between injections were used in CE and the

refractive index detection used in HPLC suffered from poor temperature control. The RSD of the

HPLC system shows it to be a more precise system. However, mannose, galactose and xylose co-

elute in HPLC while they can be quantified separately in CE (Table 2.2-2). Taking this into account

the total concentration of these three sugars determined by HPLC is compared with the sum of

the peaks determined in CE and the concentrations determined by HPLC are systematically lower

than those determined by CE: 17-22 % lower on the total sugar content and up to 44.14 % lower

on glucose. These separations have been fully reproduced with fresh solution and new capillaries

and led to similar results shown on Table 2.3-4. Lower sugar concentrations determined in HPLC

may be attributed to non-quantitative recovery due to absorption of the sugars onto the

stationary phase. Such incomplete recovery has been observed previously on this HPLC column

once [42]. The incomplete recovery cannot be explained by limited sensitivity since the signal-to-

noise ratio is above 1000 for all peaks quantified in Table 2.2-2 on the fiber sample. It is to be

noted that the capillary electrophoresis instrument we used leads to a more than sufficient

sensitivity for fiber samples, but in other cases, such as trace detection, other instruments lead to

higher sensitivity [43]. Fractions were collected after LC separation and injected in CE (Table 2.3-

5) but the results only indicate a possible additional influence of column bleeding in LC.


Table 2.2-4: Comparison of the determined sugar concentration C (g·L-1), with their relative standard deviation RSD (%), by CE (capillary of 66 cm total

length) and HPLC with HPX-87H column.

a CA is the original acid concentration of the sample before dilution (% v/v) b galactose, mannose and xylose co-elute in HPLC. Values based on the refractive index of xylose (max) and galactose (min) carabinose and rhamnose co-elute in HPLC. Values based on the refractive index of arabinose (min) and rhamnose (max) d The relative difference Diff. is in % and is calculated as the difference of the concentrations determined by CE and HPLC divided by their average, for recovery only the higher values were used e Loss in HPLC is a comparison to a recovery study previously carried out with this column under these conditions [42]. The study found a loss of recovery for different sugars; the relevant ones are listed in this row. f As mentioned previously, repeatability (as measured by RSD) is improved with the use of an internal standard, the values in this row show the RSD as measured previously by [34] for comparison.

System CAa Glucose Rhamnose Arabinose A+Rc Mannose Galactose Xylose G+M+Xb Recovery Total (g⋅L-1)

C RSD C RSD C RSD C RSD C RSD C RSD C RSD C RSD CE 1 3.58 2.4 1.21 8.61 3.95 1.13 5.16 0.59 8.90 2.76 1.65 2.10 4.27 5.45 14.19 HPLC 2.82 0.84 4.18 to

4.40 2.06 4.17 to

4.53 0.91 11.75

Diff.d 24% 21% to 16%

27% to 18%

19%

CE 3 3.67 5.38 1.28 11.15 3.19 6.21 4.47 0.58 8.58 2.41 3.98 1.59 3.36 4.58 12.72 HPLC 2.58 0.49 3.92 to

4.13 2.22 3.63 to

3.98 0.88 10.69

Diff.d 35% 13% to 8%

23% to 14%

17%

CE 4 3.54 2.98 1.05 4.98 2.39 9.67 3.44 0.54 15.29 2.03 3.21 1.06 4.05 3.63 10.61 HPLC 2.26 0.91 2.89 to

3.05 2.70 2.82 to

3.14 0.73 8.45

Diff.d 44% 17% to 12%

25% to 14%

22%

Loss in HPLCe [42] 9.9 14.3 Sample quantification without internal standard f [34]

4.3 to 16

20 to 30

8.1 to 19

2.3 to 19

3.7 to 11


3.3. Investigation of the detection in capillary electrophoresis

Monosaccharides do not normally absorb UV at 270 nm however Rovio et al. proved that

sensitive detection at this wavelength is obtained at pH 12.6 [33] with the mechanism for this

detection of the sugars at 270 nm still under debate. The complex mechanism of CE direct detection

at 270 nm, which has recently been pointed out by Sarazin et al.[25], was further investigated in this

study. Rovio et al. hypothesized an alkaline degradation of the sugars with the formation of an

enediolate absorbing at 270 nm that is unable to proceed to a carboxylic acid due to complexation

with the sodium ion [33]. Sarazin et al. subsequently argued that such a mechanism would not be as

universal as observed experimentally since it is impossible for sucrose. They proposed instead a

photo-oxidation of the saccharides in the detection window [43]. This mechanism is consistent with

the mechanism proposed by Gilbert et al. after monitoring the degradation of carbohydrates by

hydroxyl radicals using Electron Spin Resonance spectroscopy (ESR) [44] . The reaction pathway is

shown in Figure 2.3-7. The difference we observe between the UV spectra of glucose and that of

xylose and arabinose (Fig. 2.2-2 C-ii) confirms that there is a difference in the structure of the

absorbing molecule between five and six carbon sugars.

In elucidating the actual detection mechanism a possible electrochemical reaction should be

considered. Since a sucrose solution can be detected by CE in the absence of electric field as

previously shown by Sarazin et al. [43], the role of the electric field in the detection of sugars at 270

nm was further investigated. Glucose (1 g·L-1 in water) was injected into a 51.5/60 cm capillary with

130 mmol NaOH as the background electrolyte. The migration of glucose was obtained first by

applying voltage; it was independently obtained applying the same voltage for 2 min to allow for

adequate mixing of the glucose with the electrolyte shown by the separation of a water peak, then

applying a 42 mbar pressure to the capillary inlet for migration. Both types of migrations lead to

detection at 270 nm (Fig. 2.2-3). However, the peak area is close to 4 times larger in the presence of

electric field, demonstrating that the electric field plays a part in the detection (as well as the photo-

irradiation).


Figure 2.2-3: Migration of 1 g·L-1 of glucose into 130 mmol NaOH electrolyte by 16 kV electric field

(solid line) and with voltage for 2 min followed by 42 mbar pressure (dashed line).

Detection via enediolate formation as suggested by Rovio et al. [33] was further investigated

in this study. A glucose sample (1 g·L-1 in 130 mmol NaOH) was injected into the CE with 130 mmol

NaOH as the back ground electrolyte at regular intervals and migrated alternatively by voltage (Fig.

2.2-4, as well as pressure Fig. 2.3-6). Over a period of 46 h the intensity of the glucose peak

decreased and some negatively charged products formed (Table 2.3-6). The products do not have UV

spectra distinctive from that of glucose, but a higher mobility (possibly explained by a lower friction

through a smaller size).


Figure 2.2-4: Degradation of glucose in 130 mmol NaOH monitored by migration with voltage. The

arrows indicate the evolution with increasing time (0 h: bold solid line, 1.5 h: bold dashed line, 4 h:

solid line, 7 h: dotted line, 27 h: dashed line, 46 h: bold dotted line).

Migration by pressure reveals a decrease in the amount of compounds detected at 270 nm

after more than 4 h. Migration by voltage reveals that the usual glucose peak reduces in intensity

decreasing in an apparent first order reaction with respect to glucose (Fig. 2.3-5). After 4 h,

degradation products with higher electrophoretic mobility than glucose are separated and they are

also detected at 270 nm. Alkaline degradation of glucose is documented in the literature [45-47] but

it is important to note that the degradation is very slow compared to the residence time of the

monosaccharides in alkaline conditions before the detection (30-40 min) even in the presence of

electric field.

The alkaline degradation of glucose, as well as sucrose, in alkaline solution, was

characterized by 1H NMR using water suppression. No structural change was observed with glucose

within the first 2 h confirming the monitoring of this alkaline degradation by CE as described above.

No structural change was observed with sucrose even after a week (Fig. 2.3-9B), as predicted by

Sarazin et al. [43]. The potential formation of enediolates is definitely too slow to explain the


detection in CE at 270 nm, thus the hypothesis presented in Rovio’s pioneering work cannot explain

the detection at 270 nm: the photo-irradiation is necessary for these species to be detected. We

further investigated the mechanism of the photoreaction by attempting the identification of the

potential products by NMR.

NMR analysis can be quantitative in a deuterated solvent, since the water peak does not

have to be suppressed. We first tested if the photochemical reaction still takes place by replacing

water with deuterated water. When injected into the CE with 130 mmol NaOH in D2O, the

electrophoretic mobility is 20 % lower than in H2O, consistent with the difference in viscosity of D2O

and H2O [48], leading to difference in hydrodynamic friction. In both cases the peak absorbance was

still at 270 nm (Fig. 2.3-8). The detection mechanism is still possible in the absence of water;

however, the peak area observed was five times smaller than that in H2O. The difference in injection

volumes can only be a minor contribution to this difference, since the difference of viscosities for our

hydrodynamic injections is only of the order of 20 % and the difference might rather be due to the

NaOD in D2O being significantly less alkaline than sodium hydroxide in H2O. The sample of glucose (1

g⋅L-1) in 130 mmol NaOH in D2O was then exposed directly to the HP3D deuterium lamp (Scheme S-1)

for a time significantly longer than the residence time in the CE detection window and then

characterized by 1H NMR with no water suppression (Fig. 2.2-5). After 60 min of irradiation a minor

structural change was seen by 1H NMR at 4 ppm. It is thus clear than on the detection timescale,

only a minimal fraction of glucose is photo-oxidized (and the decomposition product absorbing at

270 nm has to be in minimal amount and thus highly UV-absorbing at 270 nm).


Figure 2.2-5: 1H NMR of glucose (1 g·L-1 with 130 mmol NaOH in D2O) before (A) and after irradiation

with CE deuterium lamp for 5 min (B), 30 min (C) and 60 min (D). The arrows indicate the region in

which new signals appear.

Sarazin et al. [43] and our results suggest that detection in CE is made possible by a photo-

oxidation similar to the one observed in sodium borate solutions at pH 9 [49] but the extent and rate

of the reaction is not clear. In Sarazin et al.’s experiments the sugar was moved into the window

with pressure and made stationary for the photo-reaction to proceed [43]. However our results have

shown that the electric field plays an essential role in the detection. To investigate the photo-

oxidation reaction in the presence of the electric field, glucose (1 g·L-1 in water) was injected into 130

mmol NaOH as for the usual separation. After the peak was detected, the voltage was inverted and

the glucose band passed through the detection window a second time. The voltage was inverted

again. This was repeated 15 times in total. The peak retained the same electrophoretic mobility;

however, its area was not constant, increasing during the first 6 passes and then decreasing (Fig. 2.2-

6). This variation in peak intensity with the residence time is qualitatively similar to the one observed

by Sarazin et al. [43] in the absence of electric field. In our experiment, the band is in the dark for

several minutes between each pass/photo-irradiation. The putative intermediates and mechanism

proposed by Gilbert et al. and then Sarazin et al. cannot solely explain our results since all the


proposed intermediates are unstable radicals with lifetimes significantly shorter than one minute. It

is possible that some intermediates might accumulate under photo-irradiation and be stable enough

to remain in the capillary for the next pass, consistent with the detection of minimal degradation

products detected by NMR. The decrease of the signal intensity after the 6th pass might be due to

build-up of oxygen arising from side reaction from the UV degradation of water and consistent with

a photo-oxidation process enhanced by the electric field, however could not be definitely confirmed

from these experiments.

Figure 2.2-6: Detection of glucose (1 g·L-1) in 130 mM NaOH with 16 kV separation. Each peak

represents a pass of the sugar though the lamp, after which the voltage was inverted.

4. Conclusions

HILIC, ligand exchange and other LC methods conventionally used for carbohydrates provide

an inadequate separation of all sugars in acid-hydrolyzed plant fiber and none of the columns

examined provide both a robust and clear separation. In order to maintain separation on the calcium

and lead form resins a tedious neutralizing step must be carried out. This study showed free solution

CE to be a superior quantitative method to HPLC in terms of robustness and resolution, as well as

recovery. In comparison to HPLC, CE has a significantly lower running cost, a higher throughput and a


greater flexibility. The direct detection of deprotonated saccharides is unexpectedly possible at 270

nm but it is neither due enediolate formation nor to alkaline degradation, which is slow compared to

the residence time in the detection window. The detection is likely due to an intermediate in a

photo-oxidation process that we showed to be enhanced by the electric field. Despite a still

controversial mechanism, the detection is robust (possible for example in deuterated solvent

although with lower sensitivity), although the presence of oxygen might be of importance. CE is thus

a robust and simple method to study polysaccharides degradation in general.

Acknowledgements

PC and MG thank the College of Health and Science of the University of Western Sydney for

a College Equipment Grant for the purchase of the Agilent Capillary Electrophoresis and the School

of Natural Sciences for Small Equipment grant. Support from the Australian Research Council is

gratefully acknowledged: EFH is recipient of an ARC Future Fellowship (FT0990521). We thank Dr

Michael Phillips, Dr Paul Peiris, Dr Mark Williams and Julie Markham (UWS), Mark Thomas, Dr

Artaches (Tom) Kazarian, A/Prof Michael Breadmore, Dr James Horne (University of Tasmania), Dr

Yohann Guillaneuf and Dr Jean-Louis Clement (Aix-Marseille University) for fruitful discussions.

5. References

[1] Y.J. Jeon, Z. Xun, P.L. Rogers, Lett. Appl. Microbiol. 51 (2010) 518. [2] T.A. Lloyd, C.E. Wyman, Bioresour. Technol. 96 (2005) 1967. [3] A. Esteghlalian, A.G. Hashimoto, J.J. Fenske, M.H. Penner, Bioresour. Technol. 59 (1997) 129. [4] Y. Sun, J.J. Cheng, Bioresour. Technol. 96 (2005) 1599. [5] R. Torget, P. Walter, M. Himmel, K. Grohmann, Appl. Biochem. Biotechnol. 28-29 (1991) 75. [6] B.S. Dien, H.-J.G. Jung, K.P. Vogel, M.D. Casler, J.F.S. Lamb, L. Iten, R.B. Mitchell, G. Sarath,

Biomass Bioenergy 30 (2006) 880. [7] L. da Costa Sousa, S.P.S. Chundawat, V. Balan, B.E. Dale, Curr. Opin. Biotechnol. 20 (2009)

339. [8] A.A. Ben-bassat, E. Grushka, J. Liq. Chromatogr. 14 (1991) 1051. [9] G. Bonn, J. Chromatogr. 322 (1985) 411. [10] G. Bonn, J. Chromatogr. 350 (1985) 381. [11] H. Caruel, L. Rigal, A. Gaset, J. Chromatogr. 558 (1991) 89. [12] R. Pecina, G. Bonn, E. Burtscher, O. Bobleter, J. Chromatogr. A 287 (1984) 245. [13] M. D'Amboise, D. Noēl, T. Hanai, Carbohydr. Res. 79 (1980) 1. [14] H. Alwael, D. Connolly, B. Paull, Anal. Methods 3 (2011) 62. [15] M. Verzele, G. Simoens, F. Van Damme, Chromatographia 23 (1987) 292. [16] J.B. Sluiter, R.O. Ruiz, C.J. Scarlata, A.D. Sluiter, D.W. Templeton, J. Agric. Food Chem. 58

(2010) 9043. [17] A.I. Ruiz-Matute, O. Hernández-Hernández, S. Rodríguez-Sánchez, M.L. Sanz, I. Martínez-

Castro, J. Chromatogr. B 879 (2011) 1226. [18] D.R. Knapp, Handbook of Analytical Derivatization Reactions. , Wiley, New York, 1979. [19] M.J. Villanueva-Suárez, A. Redondo-Cuenca, M.D. Rodríguez-Sevilla, M. de las Heras

Martínez, J. Agric. Food Chem. 51 (2003) 5950.


[20] Z. El Rassi, Electrophoresis 20 (1999) 3134. [21] G. Hanrahan, Chemometric Methods in Capillary Electrophoresis, Wiley, Hoboken, NJ, USA,

2009. [22] C.W. Klampfl, M. Himmelsbach, W. Buchberger, in N. Volpi (Editor), Capillary Electrophoresis

of Carbohydrates, Humana Press, 2011, p. 1. [23] O. Dahlman, A. Jacobs, A. Liljenberg, A.I. Olsson, J. Chromatogr. A 891 (2000) 157. [24] J. Cabálková, J. Žídková, L. Přibyla, J. Chmelík, Electrophoresis 25 (2004) 487. [25] D. Harvey, Modern Analytical Chemistry, McGraw-Hill, Boston, 2000. [26] P. Schmitt-Kopplin, K. Fischer, D. Freitag, A. Kettrup, J. Chromatogr. A 807 (1998) 89. [27] H. Schwaiger, P.J. Oefner, C. Huber, E. Grill, G.K. Bonn, Electrophoresis 15 (1994) 941. [28] A. Vorndran, P. Oefner, H. Scherz, G. Bonn, Chromatographia 33 (1992) 163. [29] T. Soga, D.N. Heiger, Anal. Biochem. 261 (1998) 73. [30] A. Gürel, J. Hızal, N. Öztekin, F. Erim, Chromatographia 64 (2006) 321. [31] T.J. O'Shea, S.M. Lunte, W.R. LaCourse, Anal. Chem. 65 (1993) 948. [32] T. Soga, M. Serwe, Food Chem. 69 (2000) 339. [33] S. Rovio, J. Yli-Kauhaluoma, H. Siren, Electrophoresis 28 (2007) 3129. [34] S. Rovio, H. Simolin, K. Koljonen, H. Siren, J. Chromatogr. A 1185 (2008) 139. [35] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, P. Gareil, Talanta 99 (2012) 202. [36] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, P. Gareil, Talanta 103 (2013) 301. [37] T. Foyle, L. Jennings, P. Mulcahy, Bioresour. Technol. 98 (2007) 3026. [38] T. Irick, K. West, H. Brownell, W. Schwald, J. Saddler, Appl. Biochem. Biotechnol. 17 (1988)

137. [39] M.E. Starkweather, D.A. Hoagland, M. Muthukumar, Macromolecules 33 (2000) 1245. [40] H. Cottet, P. Gareil, Electrophoresis 21 (2000) 1493. [41] H. Cottet, P. Gareil, O. Theodoly, C.E. Williams, Electrophoresis 21 (2000) 3529. [42] G. Zeppa, L. Conterno, V. Gerbi, J. Agric. Food Chem. 49 (2001) 2722. [43] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, J.M. Mallet, P. Gareil, Anal. Chem. 83 (2011)

7381. [44] B.C. Gilbert, D.M. King, C.B. Thomas, J. Chem. Soc., Perkin Trans. 2 (1982) 169. [45] J.C. Sowden, R. Schaffer, J. Am. Chem. Soc. 74 (1952) 499. [46] E.R. Garrett, J.F. Young, J. Org. Chem. 35 (1970) 3502. [47] G. De Wit, A.P.G. Kieboom, H. van Bekkum, Carbohydr. Res. 74 (1979) 157. [48] J. Kestin, N. Imaishi, S.H. Nott, J.C. Nieuwoudt, J.V. Sengers, Physica A 134 (1985) 38. [49] B. Roig, O. Thomas, Anal. Chim. Acta 477 (2003) 325.


2.3 Publication supporting information

Supporting Information for

Simple and robust separation of monosaccharides in complex mixtures by capillary electrophoresis

and high performance liquid chromatography

James D. Oliver,1 Marianne Gaborieau,2 Emily F. Hilder,3 Patrice Castignolles*1


2 University of Western Sydney (UWS), School of Science and Health, Nanoscale Organisation and Dynamics group, Locked Bag 1797, Penrith NSW 2751, Australia, [email protected]

3 Australian Centre for Research on Separation Science (ACROSS), School of Chemistry, University of Tasmania, Hobart TAS 7001, Australia, [email protected]


1. High Performance Liquid Chromatography (HPLC):

The common HPLC separations discussed and quantified in the manuscript are shown on Figure S-1.



Figure 2.3-1: HPLC Separation of sugars on HPX-87C with water mobile phase (A), HPX-87P with

water mobile phase (B) and LC-NH2 with 75:25 ACN:water mobile phase (C). Sol: Solvent peak. 1:

Cellobiose, 2: Glucose 3: Galactose 4: Xylose 5: Rhamnose 6: Arabinose 7: Mannose


Figure 2.3-2: Calibration curve of response with RID for the sugars in our fiber standard on the HPX-

87H column.

Table 2.3-1: Calibration of response with RID with its relative standard deviation (RSD) of response at

270 nm with its correlation coefficient R2, for the sugars in our fiber standard on the HPX-87H

column

Sugar Calibration (RID) R2 Glucose 263019x + 3526.5 1 Mannose 268449x + 14431 0.9999 Galactose 254065x + 120085 0.9994 Xylose 256985x + 6663.3 0.9999 Rhamnose 239718x + 18703 0.9999 Arabinose 250476x + 26141 1


2. Capillary Electrophoresis:

The electrophoretic mobility µep was determined using the following equation [1]:

𝜇𝜇𝑒𝑒𝑒𝑒 =𝑙𝑙 ∙ 𝐿𝐿𝑉𝑉 �

1𝑡𝑡m

−1𝑡𝑡𝑒𝑒𝑒𝑒�

where l is the capillary length to the detection window, L is the total length of the capillary, V is the

voltage, tm is the migration time of the analyte, teo is the migration time of the neutral species. The

ramping was taken into account by averaging the voltage at tm.

The electrophoretic mobility values are then given on Table 3 and on Table S-1 (the latter is based on

the same method as for Table 4 but with new injections of new solutions in a new capillary) and the

difference with values published by Rovio et al. is explained by Figure S-3.

Table 2.3-2: Electrophoretic mobility (10-8 m2 V-1s-1) of common fiber sugars determined in this study

(35 injections), before and after correction using lactose as the internal standard. RSD is the relative

standard deviation (%). The values are compared with published values.

Sugar

Our work Literature

Mobility RSD (before) RSD (after) Rovioa Diff.b Gürelc Diff.b Lactose 1.259 1.179

Cellobiose 1.343 1.497 0.394 1.039 25.525 1.05 24.601 Galactose 1.439 1.326 1.542 1.119 25.020 1.19 19.468 Glucose 1.518 1.425 0.448 1.176 25.390 1.16 26.578 Rhamnose 1.577 3.439 2.634 1.2 27.152 1.26 22.830 Arabinose 1.621 1.469 0.57 1.222 28.069 1.16 32.431 Xylose 1.745 1.224 0.401 1.365 24.437 1.4 22.186

a S. Rovio, et al.[2] b relative difference calculated as the difference of our corrected mobility and the literature mobility

divided by the average of both mobilities. c A. Gürel, et al. [3]


Figure 2.3-3: Comparison of electrophoretic mobility of DMSO (1) and methanol (2) in 130 mmol

NaOH and 36 mmol Na2HPO4 , detected at 200 nm.

The calibration curves used for the quantification of the carbohydrates using CE are given on Figure

S-4.

Figure 2.3-4: Calibration curves with standards, showing R2 values (capillary of 60 cm total length).

Equations are given in Table 2.


3. Quantitative Comparison of HPLC and CE:

The comparison of CE and common HPLC is given on Table S-3 in terms of cost and in Table S-4 in

terms of carbohydrates quantification. It is important to note that Table S-4 give the same

comparison as in Table 4 but from different experiments reproducing Table 4 ones with a different

capillary and solutions. The results of the analysis of HPLC fractions by CE are given on Table S-5.

Table 2.3-3: Estimate of the cost of the typical CE and HPLC separations ($ is for Australian dollar and

prices are as for 2012).

HPLC System CE System

HPX-87P HPX-87H LC-NH2

Column $2,096 $2,096 $731 Capillary $7.2 Guard Column $537 $517

$355

Guard Column Holder $848 $848 Total $3,481 $3,461 $1,086 Total $7.2

Table 2.3-4: Comparison of the determined sugar concentration C (g•L-1), with their relative

standard deviation RSD (%), by CE (capillary of 66 cm total length) and HPLC with HPX-87H column.

Compared to Table 4, these separations have been fully reproduced with fresh solution and new

capillaries.

System CAa Glucose Rhamnose Arabinose Mannose Galactose Xylose M+G+Xb

C RSD C RSD C RSD C RSD C RSD C RSD C RSD CE 1 6.12 0.33 0.76 13.38 4.77 6.3 1.35 3.01 5.1 0.33 2.25 5.2 8.7

HPLC 4.84 0.20 1.00 0.20 4.26 0.2 6.57 to 7.51

0.17

Diff.c 23 27 11 15

CE 2 6.48 2.69 1.40 7.97 4.01 5.59 1.24 9.67 5.36 1.02 3.34 3.98 9.94 HPLC 5.12 0.80 1.26 1.40 4.12 0.5 6.86 to

7.83 0.66

Diff.c 23 11 2.7 27 CE 3 7.98 2.10 1.98 6.66 4.78 0.2 1.71 5.27 6.48 2.77 3.74 6.9 11.93

HPLC 5.92 0.60 1.43 0.70 4.26 0.3 7.75 to 8.82

1.53

Diffc 30 32 11 43 a CA is the acid concentration of the sample (% v/v) b galactose, mannose and xylose co-elute in HPLC. Values based on the refractive index of Xylose and Galactose c The relative difference Diff. is in % and is calculated as the difference of the concentrations determined by CE and HPLC divided by their average.


To investigate a potential loss in the LC system, fractions were collected after LC separation and

analyzed by capillary electrophoresis. 20 µL of glucose standards (10 or 20 g·L-1) and fiber samples

(equivalent to the 4% (v/v) acid sample) were injected on a HPX-87H column and the glucose peak

was collected as one fraction after the RID detector. The fractions were then injected in CE for

quantification of the glucose (as it had the highest difference between CE and HPLC). The fiber

sample (same 4% (v/v) acid treated sample) was also diluted to the same concentration, injected

directly into CE for quantification. The glucose fractions led to quantification of glucose by the CE 24

% lower than the original amount for the 20 g·L-1 standard while it is 15 % higher for the 15 g·L-

1standard. This is not consistent with the repeatability of both LC and CE quantifications. One

explanation might be column bleeding. Common LC columns for sugars are a relatively old

technology (prior to online light-scattering detection) and column bleeding is expected. The limited

results we have are consistent with bleeding, with an overestimate of the glucose content as the

injected amount decreases. On the fiber samples, RID detection on the HPLC quantified the samples

at 2.49 g·L-1, however the CE quantified the fractions at 3.3 g·L-1 (see table S-5). Incomplete

separation in LC could lead to overestimate of the sugar amounts. The total peak area in LC treated

with the different possible calibration curves lead to 3.9, 6.9 and 7.8 % variation for the 1%, 3% and

4% acid samples respectively in the total sugar amount quantified by LC. Deconvolution of the peak

is thus not necessary to discard the hypothesis that the limited resolution could explain the lower

recovery observed in LC than in CE: deconvolution would not significantly change the total amount

of quantified sugars. Finally, our investigation of the detection mechanism in CE does not indicate

any likelihood of overestimating of the sugar content in fiber samples. We conclude that a loss of

carbohydrates in the LC system likely explains the difference between our CE and LC quantification.

The fact that the difference is larger than the loss observed in LC in the literature might be explained

by column leaching or to a lesser extent the incomplete separation in LC.


Table 2.3-5: Fraction experiments to determine the loss in HPLC in comparison to CE: comparison of

the concentrations of glucose injected in HPLC, Cinj, eluted from HPLC according to RID detection,

CRID, and as determined from CE, CCE.

Sample Cinj (g·L-1) CCE (g·L-1) CRID (g·L-1)

Glucose pure in water

(Fraction)

20.00 15.53 NA 20.00 15.76 NA 10.00 12.65 NA 10.00 13.12 NA

Fibre sample (Fraction)

NA 3.13 2.49 NA 3.11 2.49 NA 3.43 2.49 NA 3.81 2.49

Fibre sample Direct Injection

NA 3.22 NA NA 3.35 NA

4. Investigation the CE detection mechanism:

Additional information about the investigation of the detection in CE is given below. The set-

up used to produce the samples for Figure 5 is given in Scheme S-1. Monitoring of glucose

degradation is illustrated on Table S-2 and Figure S-4 and S-5. The assumed reaction scheme for the

photo-oxidation is given on Figure S-6. The difference in separation in D2O and H2O is shown on

Figure S-7, while the NMR monitoring of alkaline degradation of glucose is shown on Figure S-8.

Scheme 2.3-1: Set-up of CE photo-oxidation experiment.


Table 2.3-6: Peak areas of glucose degrading in 130 mM sodium hydroxide, separated by CE with 16

kV.

Time (h) Peak 1 Peak 2 Peak 3 0 8.10E-08 - - 1.5 7.10E-08 - - 4 4.80E-08 4.00E-09 - 7 4.90E-08 6.60E-09 6.80E-10 27 2.30E-08 8.90E-09 3.70E-09 46 6.60E-09 3.70E-09 4.10E-09

Figure 2.3-5: Evolution of the area of the glucose peak monitored by CE for a solution of glucose 1

g.L-1 in 130 mM NaOH.


Figure 2.3-6: Degradation of glucose in 130 mmol NaOH monitored by migration with pressure. The

arrows indicate the evolution with increasing time (0 h: bold solid line, 1.5 h: bold dashed line, 4 h:

solid line, 7 h: dotted line, 27 h: dashed line, 46 h: bold dotted line).


O

H

HO

H

HO

H

H

OHHOH

OH

OHO

H

HO

H

HOH

H

OHHO

OH

H OH

H2OO-

HO

HH

O

OH

HO

H

OH

H

H

HOH H

O

H

OH

H

OH

HH

OH++

OH

--H2O

HOCH2

OHH

O

+ HO

H H

H

O

+ OH-

OH-

C C

O

CH2 O

H

HO

C C

O

CH2 H

O

HO+

O O

HOCH2

Figure 2.3-7: Photo-oxidation of glucose in CE. Adapted from Gilbert et al. (1982) [5].


Figure 2.3-8: Separation and detection of glucose (1 g·L-1) in 130 mmol NaOH in water (dotted line)

and in D2O (solid line).


Figure 2.3-9: 1H NMR of 1 g·L-1 glucose (A) in 130 mmol of NaOH after 2 hours (A-I) and 5 days (A-II)

and of sucrose (B) in water (B-I), 130 mmol NaOH after 2 hours (B-II) and after 5 days (B-III)

References:

[1] H. Susumu, Journal of Chromatography A 720 (1996) 337. [2] S. Rovio, J. Yli-Kauhaluoma, H. Sirén, Electrophoresis 28 (2007) 3129. [3] A. Gürel, J. Hızal, N. Öztekin, F. Erim, Chromatographia 64 (2006) 321. [4] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, J.-M. Mallet, P. Gareil, Analytical Chemistry 83

(2011) 7381. [5] B.C. Gilbert, D.M. King, C.B. Thomas, J. Chem. Soc., Perkin Trans. 2 (1982) 169.


3. Publication “Understanding and improving direct UV detection of monosaccharides and

disaccharides in free solution capillary electrophoresis”

3


3.1.1 Investigation of the photo-oxidation reaction

CE with direct UV detection has the ability to separate all the fiber sugars of interest and

has, so far, shown to be robust against acid treated fiber samples from our last publication. The

previous publication also showed that a photo-oxidation reaction was the cause of the detection.

The lack of understanding behind the photo-oxidation reaction still remained a drawback of the

detection. This understanding is required if the method is going to continue being utilized. Before

proceeding to apply the method to fermentation samples a study of the photo-oxidation reaction

was undertaken. Mass Spectrometry (MS) was considered and CE-MS would have allowed us to

study the products of the photo-reaction however a BGE compatible with MS detection (volatile)

could not be found. MS is incompatible with the BGE’s high salt concentration. A MS compatible

organic solvent, methyl amine, was tested as a BGE for CE-MS however the photo-oxidation

detection was no longer observed suggesting an inhibition of the reaction. The organic solvent also

stripped the polyimide coating off the outside of the capillary. In discussions with Prof. Wolfgang

Buchberger of Johannes-Kepler-University it was suggested that a compatible BGE would be difficult

to find, however it is still a subject of interest to his group [144].

3.1.2 Theory of radical chemistry in relation to carbohydrate photo-oxidation

Sarazin et al. (2011) proposed that the radical photo-oxidation pathway was initiated by a

hydroxide radical (OH•). Such radicals can be formed by the decomposition of water under hv <190

nm irradiation where H2O can be decomposed to OH• and H• (see Equation 3.2-1 next subchapter).

Superoxide radicals (O2-•) can also be formed following the same irradiation (see Equation 3.2-2 next

subchapter). Radicals only exist in low concentration, since increases of the radical concentration

lead to increased termination reactions. Irradiation is suggested to originate from the CE’s DAD lamp

where wavelengths below 190 nm initiate the irradiation and wavelengths ≈ 270 nm detect the UV

absorbing intermediate. In studies by Gilbert et al. (1982) on the radical reaction of carbohydrate,

the radicals were produced chemically instead of being photo-initiated. Gilbert et al. (1982) used ESR

to study formation of radicals. In this work, however, the lamp did not provide sufficient radicals in


the ESR cavity. The pathway that was proposed in this publication also involved the presence of

oxygen biradicals that form carboxylates. Oxygen biradicals are a mesomer of oxygen molecules

(Equation 3.1-1).

Equation 3.1-1: Formation of oxygen biradicals.

The research question of the 2nd publication was “What is the reaction pathway that makes

the photo-oxidation detection possible?”


This publication contributed to the field of study by providing a reaction pathway explaining

the direct UV detection seen in CE. This new understanding ensured the method is quantitative for

all foreseen applications to fibers and fermentations and gave the potential to increase the

sensitivity of the detection though the use of radical photo-initiators.

For my personal development, this work gave me the opportunity to give an oral

presentation at the 6th International Symposium on the Separation and Characterization of Natural

and Synthetic Macromolecules in Dresden (SCM-6; see “Conference and seminar presentations”).

While attending this conference I was able to meet with Prof Pierre Gareil and Dr Nathaline

Delaunay (Chimie ParisTech, France) to discuss this publication and the 4th publication. I was

provided training on the important analytical techniques of 13C NMR (at UWS) and ESR

spectroscopies (at Aix-Marseille Université). I was also exposed to quantum mechanics calculations

when studying the detection mechanism.

Professional development was achieved through my collaboration with Dr Yohann

Guillaneuf and Dr Jean-Louis Clement from Aix-Marseille Université, France where ESR experiments

were carried out and radical pathways (work of Gilbert et al. 1982) was discussed. Collaboration with

Dr Christopher Fellows and Adam Rosser from University of New England, Australia (UNE) yielded

the quantum mechanics calculations.


direction of the paper and help formulate the ideas behind the experiments. He also facilitated the

collaboration with UNE and Aix-Marseille Université. Dr Marianne Gaborieau provided assistance

with performing 13C NMR spectroscopy experiments. Dr Christopher Fellows help formulate the idea


behind predicting the intermediate’s absorption and his student Adam Rosser carried out the

calculations. Dr Yohann Guillaneuf and Dr Jean-Louis Clement gave feedback on the reaction

schemes as well as training on ESR spectroscopy.

Although the method seemed promising, I felt that the biggest drawback was the lack of

understanding of the detection. I considered different methods to study the detection mechanism.

ESR was chosen based on its use in previous research studied in the literature however it proved to

be limited. I selected 13C NMR for its potential to identify key chemical groups in the end products of

the photo-oxidation reaction. The idea of simulating the UV spectra was developed in discussion

between Dr Chris Fellows, Patrice and myself. The theoretical calculations were performed by Adam

Rosser, a PhD student supervised by Dr Chris Fellows. I performed all background research,

experiments and data acquisition except for the quantum mechanics calculations. I performed all

data analysis and wrote the first draft of the publication. I developed the reaction mechanism, the

key finding of this paper, based on the data I had analyzed from the NMR results. I designed and

carry-out the experiments that linked the previous research of Gilbert et al (1982) with the NMR

data and the theoretical calculations carried out at UNE.


3.2 Publication

Understanding and improving direct UV detection of monosaccharides and disaccharides in free

solution capillary electrophoresis

James D. Oliver1, Adam A. Rosser3, Christopher M. Fellows3, Yohann Guillaneuf4, Jean-Louis

Clement4, Marianne Gaborieau2, Patrice Castignolles1*

1) University of Western Sydney, Australian Centre for Research On Separation Sciences (ACROSS), School of Science and Health, Parramatta Campus, Locked Bag 1797, Penrith NSW 2751, Australia

2) University of Western Sydney, Molecular Medicine Research Group (MMRG), School of Science and Health, Parramatta Campus, Locked Bag 1797, Penrith NSW 2751, Australia

3) University of New England, School of Science and Technology, Armidale NSW 2351, Australia

4) Aix-Marseille Université, CNRS, Institut de Chimie Radicalaire UMR 7273, Avenue Escadrille Normandie-Niemen, 13397 Marseille Cedex 20, France

Graphical abstract

Figure 3.2-1: Proposed sequence of events leading to UV-absorbing intermediates and carboxylated

end-products.


Abstract

Direct UV detection of carbohydrates in free solution capillary electrophoresis at 270 nm is

made possible by a photo-oxidation reaction. Glucose, rhamnose and xylose were shown to have

unique UV absorption spectra hypothesizing different UV absorbing intermediates for their

respective photo-oxidation. NMR spectroscopy of the photo-oxidation end products proved they

consisted of carboxylates and not malondialdehyde as previously theorized and that oxygen thus

plays a key role in the photo-oxidation pathway. Adding the photo-initiator Irgacure® 2959 in the

background electrolyte increased sensitivity by 40 % at an optimum concentration of 1 x 10-4 mM

and 1 x 10-8 mM for conventional 50 µm i.d. capillaries and for the corresponding extended light path

capillaries, respectively.

Keywords: Free solution capillary electrophoresis, Direct UV detection, Radical photo-oxidation,

Nuclear magnetic resonance spectroscopy, Saccharide, Photo-initiator

1 Introduction

Carbohydrate analysis is required for a variety of purposes such as food and beverage analysis,

plant characterization and metabolic studies. Gas Chromatography (GC) or GC with mass

spectrometry (GC-MS) is a common technique for carbohydrate analysis, however multi-step

derivatization is essential for the sugars to become volatile [1, 2]. High performance liquid

chromatography (HPLC) can separate carbohydrates without derivatization using ligand exchange [3,

4], hydrophilic interaction liquid chromatography (HILIC) [5, 6], or on a cation exchange resin [7, 8].

However, complex mixtures of common carbohydrates cannot be fully separated utilizing these

separation modes [9]. Direct detection of carbohydrates in HPLC requires the use of a Refractive

Index Detector (RID), a universal detector than can also detect interfering compounds with similar

elution times. High performance anion exchange chromatography (HPAEC) coupled to pulsed

amperometric detection (PAD) is a sensitive alternative technique for trace analysis of carbohydrates

[10, 11]. Although the technique is flexible, some sample pre-treatment may be required to remove

interfering compounds present in some complex matrices that can affect detection [12, 13].

Capillary electrophoresis (CE) has become a popular technology for carbohydrate analysis [14]. CE

has two distinct advantages over HPLC for sample analysis: undesirable sample components can be

flushed out after analysis, and a new capillary is less costly than a new HPLC column [14]. Previous

CE methods for carbohydrate analysis have used either indirect UV detection [15], contactless

conductivity detection (C4D) [16, 17] or complexation with ions such as borate [18] or copper (II)

[19]. The borate complex is detected only at wavelengths close to 190 nm, which is not


discriminating from other compounds, while copper may induce the formation of

supramacromolecular structures with other compounds in biological matrices and leads to poor

sensitivity. PAD can also be coupled to CE [20, 21]; however this is not currently commercially

available. Capillary electrophoresis with direct UV detection has been shown to be a reliable and

robust technique for the analysis of carbohydrates in analysis of plant fiber [9], juice [22] as well as

forensic, pharmaceutical and other beverage samples [23]. Direct UV detection of carbohydrates at

270 nm was initially proposed to be due to an enediolate formation [22]. This mechanism was

disproved in later studies [9, 24] and also does not explain the detection of sucrose. The UV

absorption is now believed to arise from an intermediate generated during the photo-oxidation [24]

of the carbohydrate. This photo-oxidation reaction is enhanced by the electric field [9]. The

efficiency of the photo-oxidation detection varies between different CE diode array detectors (DAD)

with only detectors capable of irradiation at low wavelength giving trace detection [9, 24]. While

higher sensitivity is preferred, it requires stronger irradiation at low wavelength and this might lead

to chemical degradation; however, this potential degradation has never been investigated.

Sensitivity of the carbohydrate is also dependent on the residence time in the detection window and

the structure of the carbohydrate, in particular the number of free hydroxyl groups [24]. The aim of

this study was to shed light onto the photo-oxidation reaction taking place in the detection window.

Despite direct detection being available, other indirect methods are still utilized [25], one reason

may be the limited understanding of the direct detection mechanism and the limitations of this

detection mode. The direct detection mechanism was studied in this work by analysis and modeling

of possible photo-oxidation products, with the long-term aim of increasing the sensitivity of

detection while retaining the flexibility and robustness of the CE method.

2 Materials and methods

2.1 Materials and reagents

Sodium hydroxide pellets ≥98 %, glycerol ≥99 %, malondialdehyde tetrabutylammonium salt

≥96%, gluconolactone (USP testing specifications), L+arabinose ≥99 %, and rhamnose monohydrate

≥99 % were sourced from Sigma-Aldrich (Castle Hill, NSW, Australia). Sucrose ≥99 %, glucose ≥99 %,

L-arabitol ≥98 % and D-xylose ≥99 % were sourced from Alfa Aesar (Ward Hill, MA, USA). Potassium

gluconate 98 % and fructose 99 % were sourced from BDH (Poole, Dorset UK). Deuterium oxide

(D,99.9 %) and fully 13C-labelled glucose ≥99 % were sourced from Cambridge Isotope Laboratories

(Tewksbury, MA, USA). Hydrogen peroxide 30 % (v/v) was sourced from Chem-Supply (Gillman, SA,

Australia). Formic acid ≥99 % and oxalic acid ≥99.5 % were from Univar (Ingleburn, NSW, Australia).


Glycolic acid 70 % (w/w) was from Ajax chemicals (Australia). The photo-initiator Irgacure® 2959 was

from Ciba (Switzerland). Water was of Milli-Q quality (Millipore, Bedford, MA, USA). Fused-silica

capillaries (50 µm i.d., 360 µm o.d.) were obtained from Polymicro (Phoenix, AZ, USA). High

sensitivity capillaries (50 µm i.d., 360 µm o.d with extended light path at the window) were from

Agilent Technologies (Agilent Technologies, Waldbronn, Germany).

2.2 Capillary electrophoresis

Free solution capillary electrophoresis was carried out on an Agilent 7100 instrument (or a HP3D

instrument where specified) (Agilent Technologies, Waldbronn, Germany) equipped with a Diode

Array Detector. A capillary with a 35 cm total length (26.5 cm effective length), was filled with 130

mM NaOH as the background electrolyte (BGE) which was prepared daily. The capillary was pre-

treated prior to use by flushing for 20 min in turn with 1 M NaOH, 0.1 M NaOH and water. The

sample was injected hydrodynamically by applying 34 mbar of pressure for 4 s followed by BGE at 34

mbar for 5 s. A voltage of 9.6 kV was ramped up over 1 min and signals were monitored at 200 nm

and 266 nm with a 10 nm bandwidth. Between consecutive runs, the capillary was flushed with fresh

BGE. After the final injection, the capillary was flushed for 1 min with NaOH 1M, 10 min with water

and 10 min with air. Dimethyl sulfoxide (DMSO, 1 µL/500 µL) was added to each sample to mark the

electro-osmotic flow (EOF). The EOF was determined at 200 nm. Integration was performed on

signals at 266 nm with Origin Pro 8.5 (Northampton, MA, USA).

2.3 Nuclear magnetic resonance (NMR)

The photo-oxidized sample was prepared by dissolving 13C labeled glucose in 130 mM NaOH

in D2O at 1 g⋅L-1. The sample was pressure injected continuously at 10 mbar into a 35 cm capillary

(26.5 cm effective length) with the lamp on. The sample (200 µL) was collected, made up to 600 µL

with 130 mM NaOH in D2O, and analyzed by both 1H and 13C nuclear magnetic resonance (NMR).

Standards of sodium gluconate, gluconolactone, malondialdehyde and glycerol were prepared at

100 g⋅L-1 in 130 mM NaOH in D2O. Standards of sodium methanoate, sodium glycolate and sodium

oxalate were prepared from the acids dissolved at 100 g⋅L-1 in NaOH in D2O; the NaOH concentration

was chosen to yield a final calculated pD (-log[D+]) of 13.1 (2.30, 0.63 and 2.25 M respectively).

1H NMR,13C NMR and DEPT-135 NMR spectra were recorded at room temperature on a

Bruker DRX300 spectrometer (Bruker, Alexandria, NSW, Australia) operating at 300 MHz for 1H,

equipped with a 5 mm 1H-13C dual probe. 1H NMR spectra were recorded with a 5.3 µs 30°pulse, a 5

s repetition delay and 8 to 6,000 scans. 13C NMR spectra were recorded with a 7 µs 90°pulse, a 3.3 s


repetition delay and 40 to 122,880 scans. The DEPT-135 NMR spectrum was recorded with a 8.7 µs 1H 90°pulse, a 9.7 µs 13C 90°pulse, a 145 Hz 1H-13C coupling constant, a 3 s repetition delay and

61,440 scans. 1H and 13C chemical shift scales were externally calibrated with the resonance of the

methyl signal of ethanol in D2O at 1.17 and 17.47 ppm, respectively [26].

2.4 UV-Vis spectra prediction

GaussView 4.1.2 was used to construct and visualize all species investigated. Energy

calculations and spectra predictions were executed using Gaussian ‘03W[27]. Molecules were

structurally optimized at the B3LYP/6-31++G(d) level, followed by an energy calculation at the TD-

B3LYP/6-31++G(2d, 2p) level (where TD is Time Dependent). UV-Vis spectra were extracted from the

latter calculation approximating 20 excitations for each spectrum.

3 Results and discussion

Previous CE separations of carbohydrates with direct UV detection have been made faster

with a polymer coating [24, 28]. The reversal of the EOF also decreases the residence time in the

detection window and when used with a CE DAD with limited emission below 190 nm, the limit of

detection is reached (see Figure 3.3-1). The sensitivity of the detection also depends on the life time

of the lamp as well as on the design of the DAD optics, residence time of the carbohydrate in the CE

window, and the carbohydrate structure. To increase the sensitivity of the detection, an

understanding of the electric field assisted photo-oxidation is required.

3.1 Understanding the electric field assisted photo-oxidation reaction.

Direct UV detection of carbohydrates is made possible through a photo-oxidation reaction in

the detection window [24] where a hydroxyl radical (OH•) or superoxide (O2•-) is assumed to oxidize

the carbohydrate to malondialdehyde [29] or dihydroxyacetone [24] while some of the intermediate

species absorb UV at about 270 nm. Hydroxyl or superoxide radicals can be formed by the splitting

of water at wavelengths lower than 190 nm [30], although the extent is expected to be limited with

the deuterium lamp used in CE detectors [31]. The pathway of monosaccharide oxidation by

hydroxyl radicals has been studied previously by Electron Spin Resonance (ESR) spectroscopy at pH

5-10 under inert atmosphere [29]. In that study, the hydroxyl radicals were formed by a continuous

reaction of titanium (TiIII) and hydrogen peroxide in the ESR cavity. Two types of semidiones (A and

B) were found (Schemes 1 and 2 respectively), stemming from two different reaction pathways. Type


A semidiones (Scheme 1) were detectable from pH 6 and above as both cis and trans-isomers. Type

A semidiones were detected for glucose, mannose, glucuronic acid, galactose, galacturonic acid,

rhamnose, xylose, arabinose, ribose, fructose, sorbose and maltose. However no type A semidiones

were detected for sucrose. Type B semidiones (Scheme 2) were formed in basic media, and

increased in concentration as the pH increases, but not at the expense of semidione type A, proving

two different reaction mechanisms were taking place. Type B semidiones were detected for glucose,

mannose, galactose, rhamnose, xylose, arabinose, ribose, fructose, sorbose and sucrose. No type B

semidiones were detected for maltose; however colorimetric tests for malondialdehyde, an end

product of the type B semidione pathway, showed a positive result for maltose as well suggesting a

third route [29].

O

H

HO

H

HO

HOHH

OH

OH

H O

H

HO

H

HO

HOHH

OH

OH

OH H2OH2OOH O

H

HO

H

HO

HO

HOH

OH

O

H

HO

H

HO

HO

HOH

OH

O

H

HO

H

HO

HOH

OH

OH

OHH2OO

H

HO

H

HO

HOH

O

OHOH H2OOH

H

HO

H

HO

HOH

O

OH

Scheme 3.2-1: Formation of semidione A from β-D-glucose adapted from Gilbert et al. [29].

O

H

HO

H

HO

HOHH

OH

OH

HOH H2OH2OOH

OHH2O

O

H

HO

H

HO

HOH

OH

OH

HO

H

HO

H

HO

HOH

O

OH

HO

H

HO

H

HO

HOH

O

OH

H

O

H

OH

H

OH

H

H

O

OHH

HHO

H

O

H

OH

H

OH

H

H

O

OHH

HHOO

H

HH

HO H

O

OHHO

O

HHO

HO

OH H2O O H

OHO

O H

OHOO O

OH

H2O

OH

Scheme 3.2-2: Formation of semidione B from β-D-glucose adapted (and corrected to place missing

radical in 1st and 2nd molecule), from Gilbert et al. [29]. It is noted that between the 4th and 5th stage,

protonation followed by de-protonation of the alcohol on the 4th carbon is not necessary.


In this work, ESR was attempted for the purpose of identifying the resulting UV-absorbing

intermediate(s) as they have been shown previously to have lifetime of less than 2 min [9, 24]. Direct

observation of photo-oxidized radicals in an ESR cavity was attempted by irradiating (with a Lumatec

lamp) either a pure sucrose solution at pH 13 or a sucrose solution in the presence of hydrogen

peroxide, both purged under argon gas, however no radicals were observed (see Supporting

information). Our previous NMR study demonstrated that only a small fraction of the carbohydrate

is oxidized during the detection process even after relatively long exposure to UV irradiation [9]. We

also showed that the presence of electric field increased the sensitivity of the detection thus

potentially explaining the absence of detected radicals in our ESR experiment by the absence of

electric field. To further investigate the UV absorbing product, carbohydrates were injected and the

UV absorption spectra and peak area per mM of the mobility based electropherogram at 266 nm of

carbohydrate were determined (Table 3.2-1).


Table 3.2-1: Relationship between carbohydrate, UV absorbance and possible UV absorbing

intermediate.

Carbohydrate

pKa

(25 °C)

Wavelength at

peak apex on the U

V absorption spectrum

Peak Area per mM

of carbohydrate

(10-11) **

Possible photo-oxidation pathw

ay*

Structure of type B sem

idiones[29] *

ArabitolHO OH

OH

OH

OH

n.a. 266 54

Unknown

Not recorded MaltoseOH

OHO

OHHO

HO OOH

OHO

OH

11.94 [41]

266 23

A,B

Sucrose

HOOH

OHO

HO

O

OHOH

HO

HOO

12.51 [41]

266 55

B+B

O

O

H

O

H

O

HO

HO

Glucose

OH

OHOH

HO

OHO

12.35 [41]

266 23

A,B

Rhamnose

OH

OHOH

HO

O

n.a

262 7.1

A,B H3C

O

O

H

H3C

O

H

O

Xylose

OH

OHOH

HO

O

12.29 [41]

250 6.0

A,B H

O

H

OH

O

O

H *Pathway for formation of semidiones is shown in Scheme 1 and 2. ** measured on the mobility plot monitored at 266 nm The separation and thus the photo-oxidation pathway take place at a pH above the pKa of

the sugars. The absorbance at the maximum of the UV absorption spectrum and the normalized

peak area on the related electrophoretic mobility plots were compared for each carbohydrate: Table

3.2-1 lists these values obtained in our work and compares them with the type A or B predicted by

Gilbert. Gilbert predicted three main different type B semidiones could be formed and this is

consistent with the three different values measured for wavelength at the maximum of the UV


spectra for glucose, rhamnose and xylose. Although type B semidiones were not originally detected

for maltose [29], the wavelength at the maximum of absorbance would suggest that the same UV

absorbing intermediate would be produced as for glucose in similar relative amounts. The highest

normalized peak area is obtained for sucrose, which would be consistent with photo-oxidation of

both fructose and glucose moieties or possibly an increase in reaction rate of sucrose in comparison

to glucose/fructose. Although arabitol might not be fully ionized at this pH, the UV absorption

suggests that the same intermediate as for glucose might be formed.

3.2 Simulation of the UV absorption spectra of the potential intermediates in the photo-oxidation

reactions

In order to determine if the UV absorbing intermediates relevant for CE are linked to

semidiones A or B and the pathway proposed by Gilbert et al. [29], the UV-Vis absorption spectra of

the latter were predicted (Figure 3.3-2 and Table 3.2-2). Most intermediates give theoretical peak

UV absorptions in the same range where maximum absorptions are found experimentally, with

relative experimental absorption values in reasonable agreement with the relative oscillator field

strengths calculated. Peak absorption positions for the type B semidiones and relative intensities

(assuming a predominantly transoid conformation) of absorption fitting the results for glucose and

sucrose photo-oxidation reasonably well, while the fit is poorer for the other carbohydrates. While

the predicted position of the type B semidione absorption maxima derived from xylose are similar to

the experimental values (Table 3.2-2), the relative intensities are much greater than observed

experimentally (Fig 3.3-2C).


Table 3.2-2: Simulated spectral properties of possible UV absorbing intermediates.

Structure of sem

idiones[29]

Carbohydrate Postulated

Calculated w

avelength at m

aximum

Experimental

wavelength at m

aximum

Calculated oscillator strength at m

aximum

(non-radical)

Possible photo-oxidation pathw

ay

Sucrose, Glucose

261 266

0.039 (0.022)

B

Sucrose, Glucose

261 266

0.015 (0.070)

B

Rhamnose

283 (233) 262

0.018 (0.015)

B

Rhamnose

283 262

0.027 (0.032)

Xylose 230

250 0.15 (0.045)

B

Xylose

242 250

0.057 (0.06)

Glucose All monosaccharides

314, 260 (238)

0.015 (0.09)

A

Glucose All monosaccharides

280 (247)

0.0075 (0.07)

A


3.3 Characterization of the products of the photo-oxidation reaction by 13C and 1H NMR.

The end products of the photo-oxidation reaction were characterized by NMR spectroscopy.

A sample of 13C-labelled glucose (1 g⋅L-1) in 130 mM NaOH in D2O was continuously injected into the

CE at 10 mbar with no electric field as done previously [24] and proved to give the same UV

absorption although four times less intense [9]. 600 µL sample was collected and 13C and 1H NMR

spectra were recorded. Controls consisting of 13C-labelled glucose in D2O and of an untreated 13C-

labelled glucose (1 g⋅L-1) in 130 mM NaOH in D2O were also measured. Figure 3.2-2 shows the 13C

NMR spectrum of the degradation products only: the control spectrum (in 130 mM NaOH in D2O)

was subtracted from the one of the irradiated solution following normalization on the maxima of the

two peaks between 90 and 100 ppm that are only present in glucose.

Figure 3.2-2: 13C NMR spectrum of 1 g⋅L-1 13C-labelled glucose continuously and hydrodynamically

injected into CE, after subtraction of the spectrum of the control glucose. Both original spectra are

shown in supporting information (Figure 3.3-4). Corresponding molecules taken from [40] where ‘R’

refers to a saturated alkyl group.


The highest 13C chemical shift experimentally observed in the photo-oxidized 13C glucose was

below 181 ppm (Figure 3.2-2). The mechanism proposed by Gilbert et al. [29] leads to

malondialdehyde as an end product that produced one signal with a 13C chemical shift above 193.2

ppm under our conditions (Table 3.2-3 and Figure 3.3-5A). Sarazin et al. adapted a mechanism from

Bucknall et al. [32] predicting dihydroxyacetone as an end product of the photo-oxidation. One of

dihydroxyacetone’s 13C chemical shifts is predicted to be around 201 ppm (Figure 3.3-5B). If either

mechanism were present, the corresponding concentration would be negligible, as no signal is

detected above 181 ppm which could correspond to either malondialdehyde or dihydroxyacetone.

13C chemical shifts observed below 181 ppm with no corresponding signal between 160 and

80 ppm are consistent with sodium carboxylate functional groups and possibly esters, and disproves

the presence of alkenes. Carboxylates have been observed previously in the degradation of glucose

in an alkaline solution catalyzed by an electric field in the presence of oxygen forming sodium

gluconolactone, sodium gluconate and sodium oxalate as end products [33]. 1H and 13C NMR spectra

of sodium oxalate, sodium gluconate and gluconolactone were recorded in 130 mM NaOH in D2O to

facilitate the identification of the observed 1H and 13C NMR signals (See Figure 3.3-7 and 3.3-8 and

Table 3.2-3). Note that some 13C NMR signals of the degradation products of 13C-labelled glucose are

split into multiplets due to coupling with neighboring 13C nuclei, which is not the case of the

measured standard compounds; for that reason multiple signals of the degradation products of 13C-

labelled glucose are sometimes assigned to a single signal of a measured standard compound.

Sodium oxalate was not detected in the 13C NMR spectrum of the photo-oxidized 13C glucose sample,

as shown by the absence of 13C NMR signal around 174.8 ppm. 1H and 13C chemical shifts are

however consistent with the presence of sodium gluconate as a product of the photo-oxidation.

Gluconolactone was however not observed through its signal at 174.5 ppm (Table 3.2-3 and Figure

3.3-8) despite the suggested presence of sodium gluconate supporting that the first step in the

photo-oxidation reaction is the opening of the ring structure as suggested previously (Scheme 3.2-1

and Scheme 3.2-2).

The 13C and 1H signal assignment in Table 3.2-3 shows that the products of the photo-

oxidation contain carboxylates consistent with sodium gluconate but also sodium methanoate,

sodium glycolate, and possibly glycerol. The presence of sodium methanoate and sodium glycolate

was confirmed by DEPT-135 NMR through the detection of a positive CH signal for sodium

methanoate at 172 ppm and the absence of COOH signal for sodium glycolate at 180 ppm (Figure

3.3-6). The presence of sodium glycolate and sodium methanoate might be explained by adding

oxygen as reactant in the 6th step of Scheme 3.2-2 as hypothesized on Scheme 3.2-3.


H

O

OHHO H

O

OHHO

O OOO OR

H

O

OHHO

OO

H

O

OHHO

O

HO

O

OH

O

H

O OO

HO O

O

HHO O

OHO

HO

O

HHO

OH

O OOO2

R

O R

H

O

OHHO

OHOR OH

orR H

R RHR RH O

H

or

O

HO

O

RO OO

ORO2OR

or

Scheme 3.2-3: Photo-oxidation of glucose in the presence of oxygen: possible reaction pathway

leading to sodium methanoate and sodium glycolate (a second possible reaction pathway is shown

in Scheme 3.3-2).

Gamma irradiation of glucose in the presence of oxygen showed the presence of methanoic

acid and glycolic acid as well as various others such as gluconic acid, D-arabino-hexulosonic acid and

various compounds with aldehyde functions [34]. Molecules containing aldehyde functional groups

are however not detected by 13C NMR in this study (no peak is present in the region above 190 ppm

as expected for aldehyde functional groups). The NMR spectrum of D-arabino-hexulosonic acid is

known [35] and include a peak at 104 ppm that our photo-oxidized glucose does not contain. Only

gluconic acid is present in our work. This can either be due to the high pH used in our study

restricting the pathway, or the use of UV radiation and not 60Co gamma rays [34] or even the use of

D2O in the sample.

Several 13C NMR signals are still unidentified: a doublet at 61.4 and 60.9 ppm, as well as less

intense signals at 167.9, 59.1, 40.0, and 20.0 ppm. This shows the complexity of the photo-oxidation

reaction. Additionally, the area of the NMR spectrum of the irradiated glucose and of the scaled

NMR spectrum of the initial glucose might allow us to estimate the fraction of glucose photooxidized

for the NMR experiment (Equation 3.3-1).


Table 3.2-3: Possible identification of some products from photo-oxidation of 13C glucose according

to their 13C and 1H NMR chemical shifts δ. The individual 1H and 13C NMR spectra are shown in

supporting information. All compounds listed except sodium oxalate, malondialdehyde and sodium

gluconolactone are potentially present in the sample.

Sample Sodium Methanoate Glycerol Sodium

Glycolate Sodium Gluconate

Sodium Oxalate

Malondialdehyde

Gluconolactone

δ( 13C), ppm 193.1 180.0 180.1

179.4 179.5

178.9

174.8 174.5 171.5 172.1

167.9

110.0 81.0

82.5 76.3, 75.8, 74.4

75.3, 74.0, 73.0, 72.5

74.5, 73.5

73.3, 72.8, 72.4 72.8

71.9, 70.6, 69.6

71.5, 68

64.8, 64.3 64.2

63.8, 63.2 63.3

63.6, 63.4, 63.2

62.6, 62.1 62.6

61.4, 60.9

61.0

59.1, 40.0, 20.0

δ (1H), ppm 8.8

8.5 8.5 8.5

8.6, 8.7 8.2, 7.1

4.5-3.2 (massif)

3.8-3.3 (massif) 4.1 4.1-3.6

(massif) 5.2, 5.3, 5.4

4.4-3.6 (massif)

2.6, 2.1


3.4 Increasing sensitivity utilizing photo-initiators

If the formation of hydroxyl or superoxide radical was the limiting step of the photo-

oxidation, then it would be possible to increase the amount of radical intermediates in the detection

window and therefore the sensitivity of the detection by increasing the radical formation. Radical

photo-initiators are molecules that form free radicals under UV-Vis light irradiation. In this work,

water produces some hydroxyl radicals by Eq. (1) and superoxide radicals can be formed by Eq. (2):

hv < 190

nmOH• +

H•H2O

Equation 3.2-1

hv < 190

nmO2

-•eaq +

O2

Equation 3.2-2

The efficiency of the production of hydroxyl radicals depends on the CE lamp intensity

(determined by lamp life) as well as the CE detector optics and the intensity of the lowest

wavelength (below 190 nm) irradiation reaching the solution. Strong UV irradiation around 190 nm

may however not be suitable for most application in CE since it could lead to unwanted degradation

reactions. The photo (and thermal) radical initiator hydrogen peroxide (H2O2) also generates

hydroxyl radicals under UV light (Eq. 3).

hv < 190

nm2OH•H2O2

Equation 3.2-3

Hydrogen peroxide (H2O2) was tested as a photo-oxidant for detection in CE with varying

concentrations of hydrogen peroxide (2.9 × 10-1 to 2.9 × 10-12 M) present in a BGE of 130 mM NaOH

within the capillary as well as the reservoirs. A sample of 1 g⋅L-1 sucrose was injected and the

increase in peak area in the presence of hydrogen peroxide was determined (Fig 3.2-3).


Figure 3.2-3: effect of hydrogen peroxide in BGE on peak area of 1 g⋅L-1 sucrose in 130 mM NaOH.

The Increase in peak area is relative to 1 g⋅L-1 sucrose injected with 130 mM NaOH BGE (no hydrogen

peroxide). The error bar in this graph indicates the highest and lowest value (n=2) for a given run,

while the different points indicate different runs. Runs were carried out on the HP3D instrument

(n=2) as well as the Agilent 7100.

The peak area increased in the presence of 130 mM NaOH with 1 × 10-8 to 1 × 10-4 H2O2, but

it suffered from poor repeatability. The poor thermal stability of H2O2 and the Joule heating within

the capillary might have caused excessive thermal degradation of the peroxide leading to a variation

in the results (Figure 3.2-2). The capillary is air cooled to 15 °C externally with a calculated internal

temperature increase of only 2 °C (see Equation 3.3-2 [36]), so thermal degradation should be very

limited. Alternatively, the poor repeatability may also be explained by the strong oxidizing potential

of H2O2 leading to significant reduction reaction at the cathode. The highest concentrations (3 × 10-3

to 3 × 10-1 M) led to the poorest repeatability and/or to a peak of inverse polarity, possible due to

the high amount of water produced by peroxide decomposition (water having a very low refractive

index) or the high radical concentration causing side reactions. It was thus decided to rather use a

true photo-initiator (thermally stable and with a poor oxidizing potential). The most characterized

radical photo-initiator, 2,2-dimethoxy-2-phenylacetophenone, DMPA, has very limited aqueous

solubility [37]. Irgacure® 2959 (1-[4-(2-hydroxyethoxy)-phenyl]-2-hydroxy-2-methyl-1-propan-1-one)

was chosen instead as a photo-initiator since it is water-soluble. Irgacure® 2959 is more

thermostable then hydrogen peroxide and should not be reduced at the cathode during

electrophoresis separation. Irgacure® 2959 was added to 130 mM NaOH at varying concentrations

(10-3 to 10-9 M), see Figure 3.2-4.


Figure 3.2-4: The effect of Irgacure® 2959 in BGE on peak area of 1 g⋅L-1 sucrose. (A) The increase in

peak area is shown relative to 1 g⋅L-1 sucrose injected with 130 mM NaOH BGE. Separations were

carried out in a conventional capillary (solid line) and a high sensitivity capillary (dotted line). Error

bar indicates relative standard deviation (n=5) (B) Overlay of sucrose peak in a conventional capillary

without Irgacure® 2959 (dash line) and with 1 × 10-4 M Irgacure® 2959 (solid line), in a high

sensitivity capillary without Irgacure® 2959 (dotted line) and with 1 × 10-8 M Irgacure® 2959 (dash

dotted line).


The addition of Irgacure® 2959 led to a significant increase in peak area at the lowest

Irgacure® 2959 concentrations. This may be due to an enhanced photo-oxidation reaction. The

photolysis of Irgacure® 2959 led to a variety of free radicals [38] (and Figure 3.3-9). These radicals

could lead to hydroxyl radicals as in the pathway previously discussed or some may directly photo-

oxidize the carbohydrate. The maximum increase of 42 % is observed with no significant difference

between 1 × 10-4 M and 1 × 10-6 M in a standard bare fused silica capillary. The RSD up to 1 × 10-4 M

has a maximum of 12 %, that can relate to fluctuations in the injection amount not accounted for by

an internal standard, as observed in previous studies [9, 39]. Using a high-sensitivity capillary the

concentration of Irgacure® 2959 that yielded the highest increase in peak area shifts from 1 × 10-4 M

to 1 × 10-8 M. The shift may be due a more efficient photo-decomposition producing radicals caused

by the extended light path in the capillary window and the fact that the “bubble” shape of the

detection window may focus the irradiation. This leads to a strong reduction in the amount of

required photo-initiator, but no change in the maximum increase in sensitivity. A higher

concentration of radicals likely leads to terminations or other side reactions and do not increase the

sensitivity of the detection in CE. The system can thus be considered as optimized in terms of the

addition of photo-initiator. A lower concentration of photo-initiator is preferred as Irgacure® 2959

absorbs at 270-290 nm and at a concentration of 1 × 10-8 M its absorbance is negligible (Figure 3.3-

10). At 1 × 10-3 M in both capillaries types, there is a large error consistent with a large amount of

free radicals being produced, leading to an uncontrolled reaction with alternative pathways. The

increase in radical concentration also leads to an increase in band broadening. Joule heating from

high salt concentration in the capillary window may contribute to it. These use of the high sensitivity

capillary lead to a change in peak shape (Figure 3.2-4B) however no change in peak shape was

observed due to the addition of the photo-initiator, only an increase in peak area. Additionally no

change in peak shape between conventional and high sensitivity capillaries has been observed in

other separations with direct UV detection and no photo-oxidation reaction (Figure 3.3-11). The

change of peak shape is therefore likely a result of differences in the photo-oxidation detection

between a conventional and a high sensitivity capillary and is not related to the separation, for

example to stacking.

The limit of detection (LOD) of various carbohydrates were determined (Table 3.2-4) in a

high sensitivity capillary with 130 mM NaOH containing 1 × 10-8 M Irgacure® 2959. Direct detection

with the photo-initiator resulted in a lower LOD than the popular HPLC-RID separation detection

modes that are currently used in biotechnology. In comparison the CE-contactless conductivity

detection (C4D) [17] that has recently been developed has a lower (better) LOD. Detection with C4D


requires limiting the ionic strength, thus the BGE concentration to values lower than the ones

leading to optimal resolutions [9, 22, 24].

Table 3.2-4: Comparison of limit of detection (LOD) between different analytical separation and

detection methods. CE separation with direct UV detection (this study) was at 24 kV in 90 cm (81.5

cm effective length) high sensitivity capillary in 130 mM NaOH with 1 × 10-8 M Irgacure® 2959.

System Mode Detector Analyte LOD * (mg·L-1)

Ref

High performance liquid chromatography (HPLC)

HILIC RID Glucose 130 [6] Ligand exchange (Pb3+) Glucose 21 [4] Cation exchange resin Glucose 70 [8]

Ion chromatography (IC) HPAEC PAD Glucose 0.090 [11] Capillary Electrophoresis (CE)

High pH buffer Contactless conductivity

Glucose 0.11 [17]

Direct UV Glucose 1.8 This study Arabitol 0.87 Sucrose 1.6 Maltose 2.8 Xylose 3.4

Limit of detection (LOD) as defined by [17] as the signal to noise ratio equal to 3. Noise level in CE (in

this study) with a BGE of 130 mM NaOH = 0.01 mAU.

4 Conclusions

The photo-oxidation pathway in the direct detection of carbohydrates in CE is different from

the ones previously proposed. The products of the reaction were characterized for the first time

using NMR spectroscopy of photo-oxidized, fully 13C labeled glucose and potential end products. This

discarded the presence of some end products of previously suggested pathways, such as

malondialdhyde and dihydroxyacetone. The end products of the photo-oxidation were shown to

include carboxylates that are consistent with glucose being photo-oxidized into sodium gluconate,

sodium glycolate, sodium methanoate and possible glycerol. Oxygen may play a role in the

formation of these products and might also then play a role in the formation of the intermediate(s)

absorbing UV at about 266 nm.

The sensitivity of the detection was improved by increasing the amount of free radicals

present by the use of photo-oxidants. Hydrogen peroxide is unstable leading to a decrease in

repeatability. Irgacure® 2959 was more repeatable and was able to increase the peak area by up to

42 % at a concentration of 1 × 10-8 M in a high sensitivity capillary and 1 × 10-4 M in a standard fuse

silica capillary. Alternative photo-initiators may further increase sensitivity, but their selection would

first require a more precise identification of the UV-absorbing intermediate. The increase in


sensitivity will enable the use of coated capillaries that will decrease analysis time for all DAD types.

The improved method will be useful for analysis of carbohydrates in plant, food, fermentation and

metabolic studies.

Acknowledgements: The authors would like to acknowledge the Key Centre for Polymer and Colloids

at the University of Sydney for donating photo-initiators. We wish to thank Dr Michael Phillips, Prof

Paul Peiris, Julie Markham, Danielle Taylor, Adam Sutton, David Fania (UWS), and Prof Emily Hilder

(UTas) for fruitful discussions as well Joel Thevarajah (UWS) for the oligoacrylates injection.

References [1] A.I. Ruiz-Matute, O. Hernández-Hernández, S. Rodríguez-Sánchez, M.L. Sanz, I. Martínez-

Castro, J. Chromatogr. B, 879 (2011) 1226. [2] D.R. Knapp, Handbook of analytical derivatization reactions. , Wiley, New York, 1979. [3] H. Caruel, L. Rigal, A. Gaset, J. Chromatogr., 558 (1991) 89. [4] S. Meisen, J. Wingender, U. Telgheder, Anal. Bioanal. Chem., 391 (2008) 993. [5] M. D'Amboise, D. Noēl, T. Hanai, Carbohydr. Res., 79 (1980) 1. [6] J.L. Chávez-Servın, A.I. Castellote, M.C. López-Sabater, J. Chromatogr. A, 1043 (2004) 211. [7] R. Pecina, G. Bonn, E. Burtscher, O. Bobleter, J. Chromatogr., 287 (1984) 245. [8] F. Chinnici, U. Spinabelli, C. Riponi, A. Amati, Journal of Food Composition and Analysis, 18

(2005) 121. [9] J.D. Oliver, M. Gaborieau, E.F. Hilder, P. Castignolles, J. Chromatogr. A, 1291 (2013) 179. [10] R.D. Rocklin, C.A. Pohl, J. Liq. Chromatogr., 6 (1983) 1577. [11] V.P. Hanko, J.S. Rohrer, Anal. Biochem., 283 (2000) 192. [12] T.R.I. Cataldi, C. Campa, G.E. De Benedetto, Fresenius J. Anal. Chem., 368 (2000) 739. [13] H.P. Smits, A. Cohen, T. Buttler, J. Nielsen, L. Olsson, Anal. Biochem., 261 (1998) 36. [14] C. Klampfl, M. Himmelsbach, W. Buchberger, in: N. Volpi (Ed.), Capillary Electrophoresis of

Carbohydrates, Humana Press, 2011, p. 1. [15] A. Gürel, J. Hızal, N. Öztekin, F. Erim, Chromatographia, 64 (2006) 321. [16] A.Z. Carvalho, J.A.F. da Silva, C.L. do Lago, Electrophoresis, 24 (2003) 2138. [17] P. Tůma, K. Málková, E. Samcová, K. Štulík, Anal. Chim. Acta, 698 (2011) 1. [18] S. Hoffstetter-Kuhn, A. Paulus, E. Gassmann, H.M. Widmer, Anal. Chem., 63 (1991) 1541. [19] A. Bazzanella, K. Bächmann, J. Chromatogr. A, 799 (1998) 283. [20] T.J. O'Shea, S.M. Lunte, W.R. LaCourse, Anal. Chem., 65 (1993) 948. [21] L.A. Colon, R. Dadoo, R.N. Zare, Anal. Chem., 65 (1993) 476. [22] S. Rovio, J. Yli-Kauhaluoma, H. Sirén, Electrophoresis, 28 (2007) 3129. [23] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, P. Gareil, Talanta, 99 (2012) 202. [24] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, J.-M. Mallet, P. Gareil, Anal. Chem., 83 (2011)

7381. [25] M. Vaher, M. Koel, J. Kazarjan, M. Kaljurand, Electrophoresis, 32 (2011) 1068. [26] H.E. Gottlieb, V. Kotlyar, A. Nudelman, The Journal of Organic Chemistry, 62 (1997) 7512. [27] R. Dennington, T. Keith, J. Millam, GaussView Version 4.1.2, Semichem Inc., Shawnee

Mission, KS, 2009. [28] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, P. Gareil, Talanta, 103 (2013) 301. [29] B.C. Gilbert, D.M. King, C.B. Thomas, J. Chem. Soc., Perkin Trans. 2 (1982) 169. [30] M.G. Gonzalez, E. Oliveros, M. Wörner, A.M. Braun, J. Photochem. Photobiol. C: Photochem.

Rev., 5 (2004) 225.


[31] Agilent, Personal Communication on Agilent 7100 DAD specifications (2013). [32] T. Bucknall, H.E. Edwards, K.G. Kemsley, J.S. Moore, G.O. Phillips, Carbohydr. Res., 62 (1978)

49. [33] M. Tominaga, T. Shimazoe, M. Nagashima, I. Taniguchi, Electrochem. Commun., 7 (2005)

189. [34] M.N. Schuchmann, C. von Sonntag, J. Chem. Soc., Perkin Trans. 2, 0 (1977) 1958. [35] A.F. Cirelli, E.M. Oliva, R.M. De Lederkremer, Phytochemistry, 28 (1989) 1645. [36] C.J. Evenhuis, R.M. Guijt, M. Macka, P.J. Marriott, P.R. Haddad, Anal. Chem., 78 (2006) 2684. [37] I. Lacik, S. Beuermann, M. Buback, Macromolecules, 34 (2001) 6224. [38] H. Fischer, R. Baer, R. Hany, I. Verhoolen, M. Walbiner, J. Chem. Soc.-Perkin Trans. 2 (1990)

787. [39] S. Rovio, H. Simolin, K. Koljonen, H. Sirén, J. Chromatogr. A, 1185 (2008) 139. [40] J.B. Lambert, Organic Structural Spectroscopy, Prentice Hall PTR, 1998. [41] Y.H. Lee, T.I. Lin, J. Chromatogr. B, 681 (1996) 87.



Supporting information for:

Understanding and improving direct UV detection of monosaccharides and disaccharides in free

solution capillary electrophoresis

James D. Oliver1, Adam A. Rosser3, Christopher M. Fellows3, Yohann Guillaneuf4, Jean-Louis

Clement4, Marianne Gaborieau2, Patrice Castignolles1,2*

1) University of Western Sydney, Australian Centre for Research On Separation Sciences (ACROSS), School of Science and Health, Parramatta Campus, Locked Bag 1797, Penrith NSW 2751, Australia

2) University of Western Sydney, Molecular Medicine Research Group (MMRG), School of Science and Health, Parramatta Campus, Locked Bag 1797, Penrith NSW 2751, Australia

3) University of New England, School of Science and Technology, Armidale NSW 2351, Australia

4) Aix-Marseille Université, CNRS, Institut de Chimie Radicalaire UMR 7273, Avenue Escadrille Normandie-Niemen, 13397 Marseille Cedex 20, France


1 Capillary electrophoresis separation with dynamic coating

A typical separation was performed with a dynamic coating adapted from [1]. The separation was

faster but the sensitivity poor (compare Figure 3.3-1 A and B).

Materials:

L+xylitol 99 % was sourced from Alfa Aesar (Ward Hill, MA, USA).Mannose ≥99 %,

L+arabinose≥99 %, poly(diallyldimethyl ammonium chloride) (polyDADMAC) solution 20 % (w/w),

alginic acid sodium salt and lithium hydroxide monohydrate ≥98 % were sourced from Sigma-Aldrich

(Castle Hill, NSW, Australia). D+galactose ≥99 % was sourced from Scharlau (Barcelona, Spain). Other

materials are as in manuscript.

Method:

Capillary electrophoresis was carried out as in manuscript with the following alterations. The

capillary was 60 cm (51.5 cm effective length) and coated during preparation by flushing with 1 %

w/v polyDADMAC in water for 30 min followed by with 1 % w/v alginic acid in water for 17 min and

polyDADMAC again for a further 30 min. The back ground electrolyte (BGE) was 110 mM lithium

hydroxide. The standard was injected at 34 mbar for 5 sec followed by an injection of BGE in the

same manner. The voltage was ramped to 16 kV over 1 min. Signals were monitored at 200 nm and

270 nm with a 10 nm bandwidth.


Figure 3.3-1: Separation of fibre standard in a fused silica capillary (A) and with the coated capillary

(B) in 110 mM LiOH. Capillary was coated with alginic acid and polyDADMAC (previous page). Fibre

standard of arabitol, xylitol, galactose, glucose, rhamnose, mannose, arabinose and xylose (1 g·L-1

each).


2 Electron Spin Resonance (ESR) spectroscopy

Method

ESR studies were carried out on a Bruker EPR spectrometer. A Lumatec lamp, with a peak

intensity at 350 nm, was used to irradiate the samples just before the ESR cavity. A solution of 100

mM NaOH with 25 mM H2O2 and 2 g·L-1 sucrose, purged under argon, was continuously pumped

through the quartz flow cell at 400 mL·min-1. The H2O2 was varied between 25 mM and 2.78 M with

the highest concentration leading to bubbles formation in the cavity. The NaOH concentration was

varied between 100 and 200 mM. 8 g·L-1 of ethanol was added to the solution to test the system.

Results

It was expected the lamp would break down the H2O2 into hydroxyl radicals which would

then react with sucrose to form radicals that were described by Gilbert et al.[2]. At hydrogen

peroxide concentrations of 2.78 M, bubbles were observed after the cavity, assumingly due to

formation of oxygen. However the absence of signal from both sucrose but also ethanol indicates

that the hydrogen peroxide under this irradiation generates no free radical stable enough to be

detected, contrary to Gilbert’s observations with a different system generating hydroxyl radicals.


3 Prediction of UV-Vis absorption spectra

As the formation of semidione B was similar to the pattern of detection in capillary

electrophoresis (higher the pH, high the amount of UV absorbing species) the UV absorbing species

may be one of the intermediate leading to the formation of semidione B. In order to determine this,

UV-Vis absorption spectra predictions were carried out based on the HOMO-LUMO gap (method in

manuscript).

Scheme 3.3-1: List of potential UV absorbing intermediates based on Gilbert et al.[2] and the

assignments.


Table 3.3-1: Results of TD-B3LYP/6-31++G(2d, 2p) Calculations. Electronic energies (E), zero point

energies (EZPE), thermal energies (U), enthalpies (H) and Gibbs Free Energies (G) in hartrees and

entropies (S) in cal mol–1 K–1.

Molecule E EZPE U H G S TBsfG1 -342 -342 -342 -342 -342 78.7 TBsfG1_nr -343 -343 -343 -343 -343 81.6 TBsfG2 -342 -342 -342 -342 -342 75.1 TBsfG2_nr -343 -343 -343 -343 -343 79.5 TBsfR1 -267 -267 -267 -267 -267 71.5 TBsfR1_nr -268 -268 -268 -268 -268 75.5 TBsfR2 -267 -267 -267 -267 -267 74.0 TBsfR2_nr -268 -268 -268 -268 -268 73.3 TBsfX1 -228 -228 -228 -228 -228 66.2 TBsfX1_nr -228 -228 -228 -228 -228 66.4 TBsfX2 -228 -228 -228 -228 -228 64.8 TBsfX2_nr -228 -228 -228 -228 -228 67.3 TAS1 -686 -686 -686 -686 -686 109 TAS1_nr -687 -686 -686 -686 -686 111 TAS2 -686 -686 -686 -686 -686 112 TAS2_nr -687 -686 -686 -686 -686 112


Table 3.3-2: Principal Features of Predicted Spectra.

Molecule HOMO-LUMO gap (hartrees)

N=3 (3 predicted excitations, major contributor in bold)

λmax

TBsfG1 0.115 515, 505, 446 457 TBsfG1_nr 0.0921 713, 528, 455 527 TBsfG2 0.102 607, 490, 454 460 TBsfG2_nr 0.108 550, 459, 440 466 TBsfR1 0.0869 741, 543, 496 502 TBsfR1_nr 0.0811 848, 604, 493 501 TBsfR2 0.0810 824, 571, 530 570 TBsfR2_nr 0.0753 923, 601, 547 552 TBsfX1 0.101 640, 525, 478 639 TBsfX1_nr 0.0941 700, 530, 489 700 TBsfX2 0.115 534, 524, 485 533 TBsfX2_nr 0.0944 700, 495, 450 479 TAS1 0.122 499, 445, 401 434 TAS1_nr 0.125 466, 387, 361 384 TAS2 0.117 555, 471, 413 443 TAS2_nr 0.114 488, 421, 408 464



Figure 3.3-2: UV absorption spectra of glucose (A) rhamnose (B) and xylose (C) during CE separation

(solid line) overlaid with predicted spectra of type B semidione with radical (dashed line and dotted

line).


4 1H and 13C NMR spectroscopy

Figure 3.3-3 and Figure 3.3-4 show the 1H and 13C NMR spectra, respectively, of 13C labelled

glucose before and after irradiation.

Figure 3.3-3: 1HNMR spectra of 1 g⋅L-1 13C glucose in 130 mM NaOH before (black) and after (red)

continuous and hydrodynamic injection into CE.


Figure 3.3-4:13CNMR spectra of 1 g⋅L-1 13Cglucose in 130 mM NaOH before (black) and after (red)

continuous hydrodynamic injection into a capillary.

The fraction of glucose degraded under irradiation was estimated as follows. The spectrum

of the initial glucose has been scaled to fit the glucose left in the irradiated sample. The area of the

scaled spectrum of the initial glucose, AG thus represents the non-degraded glucose. The difference

of the area of the spectrum of the irradiated glucose, AI, with AG thus represents the degraded

glucose in the irradiated sample. The fraction of degraded glucose, F, is calculated as:

F=( AI - AG) / AI

Equation 3.3-1

F is estimated at 80 % in our pressure mobilization experiment using the 13C NMR spectra.

For this particular experiment, the velocity of glucose in the capillary was 1.23 cm·min-1, while

glucose migrates at 25 min in a 90 cm capillary, at a velocity of 3.6 cm·min-1. Taking the ratio of the

velocities into account, the fraction of glucose photo-oxidised in a typical CE separation can thus be

estimated at 27 % of the glucose in a 90 cm capillary. This large glucose consumption would not be

consistent with the previous observation of the change of absorbance with residence time in the

window [3] or multiple passes through the window [4]. This would not be consistent either with


previous NMR results on irradiated non-labelled glucose [4]. Note that the relaxation times were not

determined and the values given in our conditions with the Equation 3.3-1 above are thus only crude

estimates.

66.4201.366.4

HOO

OH

(B) Figure 3.3-5: Experimental 13C NMR spectrum for malondialdehyde tetrabutylammonium salt in the

same conditions as Figure 3.3-4 (A) and predicted 13C NMR chemical shifts for dihydroxyacetone (B)

(predictions performed with ChemNMR at neutral pH).


Figure 3.3-6: 13C NMR spectrum (black) and DEPT-135 NMR spectrum (red) of 1 g⋅L-1 13C glucose in

130 mM NaOH after continuous hydrodynamic injection into a capillary. A DEPT-135 NMR spectrum

exhibits positive CH and CH3 signals, negative CH2 signals, and no signal for other carbons.


Figure 3.3-7: 1H NMR spectra of A. glycerol (solid black), B. sodium oxalate (solid red), C.sodium

glycolate (dotted black), D. sodium gluconate (dotted red), E. sodium methanoate (dashed black)

and F. gluconolactone (dashed red). The chemical shifts predicted with ChemNMR are shown on the

molecules on the left.


Figure 3.3-8: 13C NMR spectra of A. glycerol (solid black), B. sodium oxalate (solid red), C. sodium

glycolate (dotted black), D. sodium gluconate (dotted red), E. sodium methanoate (dashed black)

and F. gluconolactone (dashed red). The chemical shifts predicted with ChemNMR are shown on the

molecules on the left.


H

O

OHOH

O

OHHO

H

OHO

O

H

OH

OH

HOHO

O

OH

OHHO

O

OHOH

OOH

HO

H

H

(Multiple steps)

Scheme 3.3-2: A second possibility for the oxidation of glucose in the presence of oxygen leading to

sodium methanoate and sodium glycolate as well as sodium glycerate.


5 Temperature increase in the capillary

The Joule heating inside the capillary was measured by the sum of the Equations 3.3-2 to

3.3-4. All equations and nomenclature were from [5]. The radial temperature difference ΔTradial

across the electrolyte was calculated using Equation 3.3-2. The temperature difference ΔTacross wall

across the fuse silica wall as well as the polyimide coating was calculated using Equation 3.3-3. The

temperature difference ΔTair across the air layer surrounding the capillary was calculated using

Equation 3.3-4.

𝜟𝜟𝑻𝑻𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓 = 𝑻𝑻𝒓𝒓𝒂𝒂𝒓𝒓𝒂𝒂 − 𝑻𝑻𝒘𝒘𝒓𝒓𝒓𝒓𝒓𝒓 = 𝑽𝑽𝑽𝑽𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒

= � 𝟏𝟏𝟒𝟒𝟒𝟒𝝀𝝀𝒆𝒆𝒓𝒓𝒆𝒆𝒆𝒆𝒕𝒕𝒓𝒓𝒕𝒕𝒓𝒓𝒕𝒕𝒕𝒕𝒆𝒆

� 𝑷𝑷𝟒𝟒

Equation 3.3-2

𝜟𝜟𝑻𝑻𝒓𝒓𝒆𝒆𝒓𝒓𝒕𝒕𝒂𝒂𝒂𝒂 𝒘𝒘𝒓𝒓𝒓𝒓𝒓𝒓 = 𝟏𝟏𝟐𝟐𝟒𝟒𝟒𝟒𝒘𝒘𝒓𝒓𝒓𝒓𝒓𝒓

𝒓𝒓𝒍𝒍 �𝒓𝒓𝒕𝒕𝒓𝒓𝒓𝒓� 𝑷𝑷𝟒𝟒

𝜟𝜟𝑻𝑻𝒆𝒆𝒕𝒕𝒓𝒓𝒕𝒕𝒓𝒓𝒍𝒍𝒄𝒄 = 𝟏𝟏𝟐𝟐𝟒𝟒𝟒𝟒𝒆𝒆𝒕𝒕𝒓𝒓𝒕𝒕𝒓𝒓𝒍𝒍𝒄𝒄

𝒓𝒓𝒍𝒍 �𝒓𝒓𝒕𝒕𝒓𝒓𝒓𝒓� 𝑷𝑷𝟒𝟒

Equation 3.3-3

𝜟𝜟𝑻𝑻𝒓𝒓𝒓𝒓𝒓𝒓 = 𝒂𝒂𝒓𝒓𝒓𝒓𝒓𝒓𝟒𝟒𝒓𝒓𝟎𝟎𝟒𝟒𝒓𝒓𝒓𝒓𝒓𝒓

𝑷𝑷𝟒𝟒

= 𝟏𝟏𝟒𝟒𝒓𝒓𝟎𝟎𝒉𝒉

𝑷𝑷𝟒𝟒

Equation 3.3-4

In these equations, 𝟒𝟒electrolyte is the thermal conductivity of the electrolyte, d0 is the external

diameter of the capillary, di ids the internal diameter of the capillary, P/L is the power per unit

length, V is the applied voltage, I is the electric current, L is the total length of the capillary, Xair is the

thickness of the stationary layer surrounding the capillary, 𝟒𝟒air is the thermal conductivity of air, and

h is the heat transfer co-efficient. 𝟒𝟒wall and 𝟒𝟒coating are the thermal conductivities of the capillary wall

and polyimide coating respectively. The thermal conductivities values used for the fuse silica wall

and the polyimide coating were 1.40 and 0.155 W.m-1.K-1 respectively [5-7].


6 Increasing sensitivity utilizing photo-initiators

HO

OO

OH

HO

OO

OH

+hv

Figure 3.3-9: First step in photolysis of Irgacure® 2959 adapted from [8]. Products react further to

form a variety of radicals.

Figure 3.3-10: UV absorption spectra of Irgacure® 2959 at 1 × 10-3 M (red) and 1 × 10-8 M (black) in

130 mM NaOH, obtained using pressure mobilization in the 7100 CE instrument using a high

sensitivity capillary and pressure mobilization.


Figure 3.3-11: Separation of oligoacrylate in a high sensitivity capillary (black) and normal fuse silica

capillary (red). The initiated monomer (AA1) peak [9] is identified with the blue box. Separation

conditions: 30 kV, 25 °C, 75 mM sodium borate buffer.

7 References [1] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, P. Gareil, Talanta, 99 (2012) 202. [2] B.C. Gilbert, D.M. King, C.B. Thomas, J. Chem. Soc., Perkin Trans. 2 (1982) 169. [3] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, J.-M. Mallet, P. Gareil, Anal. Chem., 83 (2011)

7381. [4] J.D. Oliver, M. Gaborieau, E.F. Hilder, P. Castignolles, J. Chromatogr. A, 1291 (2013) 179. [5] C.J. Evenhuis, R.M. Guijt, M. Macka, P.J. Marriott, P.R. Haddad, Anal. Chem., 78 (2006) 2684. [6] T. Nishikawa, H. Kambara, Electrophoresis, 17 (1996) 1115. [7] J.H. Knox, Chromatographia, 26 (1988) 329. [8] H. Fischer, R. Baer, R. Hany, I. Verhoolen, M. Walbiner, J. Chem. Soc.-Perkin Trans. 2 (1990)

787. [9] M. Gaborieau, T.J. Causon, Y. Guillaneuf, E.F. Hilder, P. Castignolles, Aust. J. Chem., 63 (2010)

1219.


4. Publication “Ethanol determination using pressure mobilization and free solution capillary

electrophoresis by photo-oxidation assisted ultraviolet detection”

4


4.1.1 Ethanol determination with CE

The limitation of the direct UV detection for monitoring fermentations was that ethanol

produced by fermentation (as discussed in 1.2.3.1) could not be detected. There is no single method

that can determine ethanol and carbohydrates in a complex sample (see 1.4). HPLC on a hydrogen

form resin cannot resolve the common fiber sugars xylose and galactose and HPAEC does not

resolve the sample matrix from the ethanol. In CE, determination had only been achieved with MKCE

which is incompatible with complex samples (see 1.4). Recently headspace CE has been investigated

[145] however ethanol could still not be detected.

In previous work (unpublished), ammonium hydroxide and methyl amine were both used to

raise the pH instead of NaOH in the BGE in an attempt to adapt the method for use the CE-MS (Mass

Spectrometry). It was found that the use of methyl amine in the BGE resulted in a loss of signal,

likely by interfering with the photo-oxidation reaction. Similarly in a different experiment, 1 % (v/v)

methanol was added to the BGE in order to slow the EOF and increase the resolution (4th publication

supporting information; Figure 5.3-4). Methanol was also found to inhibit the signal. Due to these

unexpected results and the understanding gained in the 2nd publication, it was successfully

hypothesized in the 3rd publication that ethanol could be detected by photo-oxidation interference.

The interference was investigated by 13C NMR, as with the previous (2nd) publication. It was found

that the modified reaction did not produce any new end products. In order to determine if this new

photo-oxidation interference detection was quantitative, it was tested with a simple pressure

mobilization experiment and a vodka sample. The method showed excellent recovery for ethanol.

For samples requiring separation, such as fermentation samples, a CE method was developed and

tested with a simple spiked fermentation sample, which had good recovery (110 % without an

internal standard). The method was used to detect ethanol production in fiber fermentation

samples. When combined with an adequate separation of the carbohydrates, both injections could

give an overview of the fermentation process.


The complex samples produced by lignocellulosic fiber hydrolysis (as discussed in section

1.2.1 and 1.2.2) and the following fermentation (as discussed in section 1.2.3) requires a separation

method with high resolution. The number of samples produced requires a method with high

throughput. The 4th publication looked at improving both the resolution and throughput in CE by

determining the influence the BGE has on the separation. As all the resolutions studied were with

CE, and thus all asymmetrical peaks, a resolution equation was chosen that was tailored for

asymmetrical peaks as opposed to the typical resolution equation used in the first publication that

assumes the peaks are symmetrical. The 4th publication provides a list of recommended BGEs that

would provide the best separation depending on the type of fermentation. Fermentation samples

with known sugar amounts were quantitatively compared by CE, HPLC and HPAEC and the results

showed that the three methods were quantitative and the values were in close agreement (less than

7 % difference from the average total detected amount). A fermentation of hydrolyzed plant fiber

from Opuntia ficus-indica was fermented to arabitol and ethanol by the yeast Pichia stipitis. The

hydrolyzed sugars and the end-product arabitol were monitored by CE. Ethanol was monitored by CE

coupled to pressure mobilization which was developed in the 3rd publication. Together these 2

methods gave an overview of the fermentation process.

The research question of the 3rd publication was “Can the photo-oxidation detection be

used to quantify ethanol in fermentation samples?”


This publication contributed to the field of study by providing a new method for the

detection of ethanol and other non-UV absorbing molecules. This new method is quantitative with

both simple pressure mobilization as well as CE coupled to pressure mobilization.

This publication contributed to my personal development by increasing my understanding of

radical chemistry. This publication contributed to my professional development by its presentation

at the 2013 HPLC conference in Hobart, Australia. The talk given by Dr Patrice Castignolles

showcased some of the work and the interest generated led to an invitation to publish the work in a

special issue of the ‘Journal of Chromatography A’ that was related to the HPLC conference.


direction of the paper. Dr Marianne Gaborieau provided assistance with performing 13C NMR

spectroscopy experiments.


I was able to overcome the main weakness of CE with direct UV detection in regards to its

application in ethanol fermentation monitoring, the impossibility to detect and quantify ethanol. I

performed some experiments with head space GC however I decided not to pursue since this meant

having to use two different instruments. I, along with Patrice, developed the theory of detection of

alcohols by photo-oxidation interference after observations made from previous experiments in our

laboratory (now seen in fourth publication supporting information). I developed the method for the

detection using pressure mobilization and using CE. I also selected the standard beverage and

fermentation sample and applied the method to them. I performed all background research,

experiments, data acquisition and analysis as well as writing the first draft of the publication.


4.2 Publication

Ethanol determination using pressure mobilization and free solution capillary electrophoresis by

photo-oxidation assisted UV detection

James D. Oliver,a Marianne Gaborieau,b and Patrice Castignolles*a

a University of Western Sydney (UWS), Australian Centre for Research on Separation Science (ACROSS), School of Science and Health, Locked Bag 1797, Penrith NSW 2751, Australia, [email protected], [email protected]

b University of Western Sydney (UWS), Molecular Medicine Research Group (MMRG), School of Science and Health, Locked Bag 1797, Penrith NSW 2751, Australia, [email protected]


Abstract:

Free solution capillary electrophoresis (CE) can separate and quantify carbohydrates using a

simple direct UV detection based on a photo-oxidation reaction taking place in the detection

window without any labeling. Ethanol interferes with this photo-oxidation reaction. We thus present

the first detection and quantification of ethanol using either a simple pressure mobilization set-up or

CE. Ethanol can be detected down to 34.9 mg·L-1 and quantified in the range 117 mg·L-1 - 1850 mg·L-1

through the interference with photo-oxidization of 2 g·L-1 sucrose. CE can thus separate and quantify

both carbohydrates and ethanol, for example to monitor a lignocellulosic fermentation process. The

method is not limited to ethanol and applies to alkyl amines and other alcohols and likely to most

molecules possessing the ability to react with free radicals assuming they can be sufficiently

separated from each other.

Keywords: ethanol detection; pressure mobilization; capillary electrophoresis; photo-oxidation

detection; nuclear magnetic resonance spectroscopy

1 Introduction

The determination of ethanol is essential for the analysis of ethanol fermentations and

related alcoholic beverages. Bioethanol fermentation of lignocellulosic material is an important

process that will hopefully significantly reduce the global demand on fossil fuels. Available methods

for the detection and quantification of ethanol in complex matrices are few. Typical methods for the

determination of ethanol include Gas Chromatography with Flame Ionization Detection (GC-FID) [1],

High Performance Liquid Chromatography (HPLC) with Refractive Index detection (RI) on a cation

exchange resin [2] or High Performance Anion Exchange Chromatography (HPAEC) with Pulsed


Amperometric Detection (PAD) [3]. The detection of underivatised ethanol is challenging due to its

lack of UV absorption or fluorescence emission. In ethanol fermentation, an analytical method is

more advantageous if it can determine both carbohydrates and ethanol. GC methods require

derivatization of carbohydrates to make them volatile like ethanol [4], while HPLC [5] and HPAEC

have high running costs and, in the case of HPLC, may suffer from poor robustness and recovery [5].

Additionally, no single separation technique can determine simultaneously ethanol and a complex

mixture of carbohydrates in a complex matrix such as that of a lignocellulosic fermentation.

Some modes of Capillary Electrophoresis (CE) have previously been used in the detection of

ethanol. Ethanol and other solvents have been previously quantified by Micellar ElectroKinetic

capillary Chromatography (MEKC) with indirect detection [7] however it requires the use of sodium

dodecyl sulfate (SDS) surfactant that may interact with proteins and lipids present in complex

samples, such as lignocellulosic fermentations. CE with PAD [8] or indirect UV detection [9] was able

to detect ethanol however no quantification was carried out. CE with direct UV detection at high pH

is a simple and robust method developed for carbohydrate analysis [10]. The separation has been

applied to wide variety of complex matrices including forensic, food, beverage and pharmaceutical

samples [6], fruit juices and cognac [10], and complex acid treated plant fiber samples [5,11].

Ethanol was however never determined by this method limiting its application for monitoring

ethanol fermentations. Direct UV detection of carbohydrates in CE at high pH was originally

suggested to be due to enediolate formation [6,10] but later shown to be due to be a photo-

oxidation reaction [5,6]. This photo-oxidation reaction takes place directly in the detection window

[12,13]. Hydroxyl and/or superoxide related radicals may be produced following minimal but

sufficient decomposition of water at the low wavelength UV irradiation in the detection window

[14], then react with the carbohydrates. Alternatively the carbohydrates may be directly photo-

decomposed by the irradiation [15]. In both cases the radicals obtained from the carbohydrates

decompose through a pathway containing UV-absorbing (250-270 nm) intermediates [13,16] to

carboxylate decomposition products [13]. Multiple passes through the detection window in one

experiment (reversing the electric field after each pass) revealed that the electrophoretic mobility of

the carbohydrates is constant but the peak intensity increases for 6 passes before decreasing [5].

The UV absorption at 250-270 nm is observed with a CE Diode Array Detector (DAD) but not with a

classical grate spectrophotometer [12]. Ethanol might also encounter some photo-oxidation under

these conditions [17], but it does not lead to any UV absorption. Ethanol undergoes hydrogen

abstraction in the presence of some free radicals e.g. in the presence of peroxides [18] or in radical

polymerization [19,20]. We hypothesized that ethanol would interfere with the photo-oxidation

reaction and hence the detection of the carbohydrate and this interference would lead to a change


in direct UV detection in carbohydrates proportionally to the amount of ethanol present. The aim of

this study was to investigate a detection method for ethanol with free solution CE equipment

compatible with both pressure mobilization and free solution CE through its interference with the

photo-oxidation reaction, to investigate how ethanol could interfere with the detection of

carbohydrates and to apply the detection method to fermentation samples and alcoholic beverages.

The long term goal is to develop a separation method that can determine both ethanol and

carbohydrates in a complex sample such as the fermented lignocellulosic plant fiber.

2 Materials and methods

2.1 Materials

Water was of MilliQ quality (Millipore, USA). Sodium hydroxide pellets (NaOH) ≥98 %,

absolute ethanol ≥99.5 % and magnesium chloride hexahydrate ≥99 % were obtained from Sigma-

Aldrich (Australia). Xylitol ≥99 %, sucrose ≥99 % and ammonium sulfate 99 % were obtained from

Alfa Aesar (USA). Fused-silica capillaries (50 µm i.d., 360 µm o.d.) were obtained from Polymicro

(USA). Triethylamine ≥99.5 % and tert-butanol ≥99 % was obtained from BDH (UK). Deuterium oxide

(D, 99.9 %) and fully 13C-labeled glucose ≥99 % were sourced from Cambridge Isotope Laboratories

(USA). Yeast extract was obtained from Oxoid (Australia). Monopotassium phosphate ≥99 % was

obtained from Univar (Australia). Vodka (declared alcohol content 37 %) was produced commercially

in Australia and purchased locally. The fermentation sample, after dilution, was comprised of 500

mg·L-1 of each glucose, fructose and yeast extract and of 0.50 mg·L-1 of each MgCl2, (NH4)2SO4 and

KH2PO4.

2.2 Free solution capillary electrophoresis (CE) and pressure mobilization

The instruments were a MDQ P/ACE (Beckman) and a 7100 CE (Agilent) with DADs

monitoring at 200 nm and 266 nm with a 10 nm bandwidth. Samples were injected by applying 14

mbar of pressure for 8 s (≈10 nL) followed by mobile phase or background electrolyte (BGE) injected

in the same manner. At the end of a series of injection, the capillary was flushed 1 min with NaOH 1

M, 10 min with water and 10 min with air. Integration was performed using Karat 32 (Beckman) or

Chemstation (Agilent) software.


2.2.1 Pressure mobilization

The capillary length was 90 cm with an effective length of 10 cm on the MDQ P/ACE

(Beckman) and of 8.5 cm on the 7100 CE (Agilent) instruments. The mobile phase was comprised of

130 mM NaOH unless otherwise specified. Sucrose and xylitol do not reduce in the presence of 130

mM NaOH in water and therefore their solutions were prepared in such medium. The capillary was

pre-treated prior to use and between each run by flushing with the mobile phase for 5 min. Pressure

mobilization was at 50 mbar unless otherwise specified. For NMR spectroscopy, 1 g·L-1 of 13C labeled

glucose and 2 g·L-1 of ethanol in 130 mM NaOH in D2O was pressure injected continuously at 50

mbar into a 35 cm capillary (26.5 cm effective length) on the 7100 CE instrument with the lamp on;

130 µL was collected, and made up to 580 µL with 130 mM NaOH in D2O.

2.2.2 Free solution capillary electrophoresis (CE)

The capillary length was 90 cm with an 81.5 cm effective length on the 7100 CE (Agilent).

The BGE consisted of 130 mM NaOH with 2 g·L-1 of sucrose in the capillary and 130 mM NaOH only,

in the inlet and outlet vials. The capillary was pre-treated prior to use by flushing with 1 M NaOH for

20 min followed by water for 5 min then the BGE for 10 min. The BGE containing sucrose was then

flushed between injections for 10 min. The electric field was applied for 12 min at 24 kV followed by

pressure mobilization at 50 mbar.

2.3 NMR

1H NMR and 13C NMR spectra were recorded at room temperature on a Bruker DRX300

spectrometer (Bruker, Alexandria, NSW, Australia) operating at 300 MHz for 1H, equipped with a 5

mm 1H-13C dual probe. 1H NMR spectra were recorded with a 5.3 µs 30 °pulse, a 5 s repetition delay

and 800-20,480 scans. 13C NMR spectra were recorded with a 7 µs 90 °pulse, a 3 s repetition delay

and 20,358 to 184,320 scans. 1H and 13C chemical shift scales were externally calibrated with the

resonance of the methyl signal of ethanol in D2O at 1.17 and 17.47 ppm, respectively [21].


3 Results and discussion:

3.1 Photo-oxidation assisted detection of ethanol

Pressure mobilization of sucrose dissolved in 130 mM NaOH led to a Gaussian peak, which

intensity decreased when ethanol was added to the sucrose (Figure 4.2-1A). Sucrose peak area,

height and shape are increasingly affected by ethanol when the ethanol concentration increases.

Ethanol interferes with the photo-oxidation reaction of sucrose and suppresses the sucrose signal

because of a decrease in concentration of UV absorbing intermediate(s). The signal is monotonically

decreasing with the amount of ethanol added. Ethanol disruption occurs in a narrower band than

that of the sucrose peak (Figure 4.2-1B): ethanol thus suppresses the signal corresponding to the

center of the sucrose peak, but not the tail, creating a valley. Considering the Taylor Dispersion

Analysis of pressure mobilization [22], this means that ethanol diffuses faster than sucrose in NaOH

130 mM, which is indeed expected from the difference of sizes of the molecules: ethanol diffuses

faster than glucose [23], which in turn diffuses faster than sucrose [24]. If sucrose is placed in the

mobile phase, then ethanol can be detected indirectly as a negative peak (Figure 4.2-1B). The

(negative) peak is then Gaussian confirming that the unusual peak shape in direct detection is due to

the difference of diffusion coefficients of the ethanol and the carbohydrate. The peak shape is of

importance for determining the ethanol concentration through either the loss in peak area or the

loss in peak height (discussed later).


Figure 4.2-1: Pressure mobilization at 50 mbar: (A) of 2 g·L-1 sucrose in 130 mM NaOH not spiked

(solid line) or spiked with ethanol at 250 mg·L-1 (dotted line), 1 g·L-1 (dashed line) and 2 g·L-1 (dotted-

dashed line), with NaOH 130 mM as the mobile phase. (B) of 2 g·L-1 sucrose in 130 mM NaOH (with

130 mM NaOH as the mobile phase, dotted line) and of 1 g·L-1 ethanol in 2 g·L-1 sucrose and 130 mM

NaOH (with 130 mM NaOH with 2 g·L-1 sucrose as the mobile phase, solid line). Performed on MDQ

instrument (n=5).


3.2 Understanding ethanol interference with carbohydrate photo-oxidation

Reactions of some free radicals with ethanol have been previously extensively studied

[19,20,25] especially with ethanol as a transfer agent during radical polymerization. The free radical

abstracts a hydrogen from the carbon bearing the alcohol group (α-carbon), and not from the

hydroxyl group itself (consistent with the bond dissociation energies, BDE, Table 4.3-1), producing a

carbon centered radical [18,26] depicted in Figure 4.2-2.

Figure 4.2-2: Hydrogen abstraction from ethanol by a free radical R·. Adapted from [18].

To investigate this mechanism and ensure that it is not due an impurity in ethanol,

interference from ethanol, methanol, iso-propanol, tert-butanol and triethylamine with sucrose

photo-oxidation was compared. The interference is also observed for these four other compounds

confirming that the detection is not due to an impurity present in ethanol. The transfer coefficient to

ethanol, methanol and iso-propanol has been determined previously in the radical polymerization of

alkyl acrylates [25]. The transfer coefficient to isopropanol is 3 times higher than the one to ethanol

and 29 times higher than the one to methanol. This trend indicates a higher reactivity of alcohols

toward the acrylate carbon-centered radicals when the alkyl group (on the alcohol) increases in size.

The BDE [27] indicate the same trend (Table 4.3-1), increasing from isopropanol, to ethanol and

methanol, with tert-butanol having the highest BDE of all while the BDE of ethanol and methanol

have a lot closer values. Triethylamine has a slightly lower BDE than isopropanol. The BDEs of the

alcohols and amines and their relative ability to interfere with the photo-oxidation of carbohydrates

were compared. At the lower alcohol/amine concentration, ethanol has the highest inference and

tert-butanol and methanol have the lowest. Although methanol has a more favorable BDE, tert-

butanol has more hydrogens to abstract. iso-propanol has the highest peak difference, however not

significantly different from that of ethanol (Figure 4.2-3). The trend in the inhibition of photo-

oxidation is quantitatively but also qualitatively different from that in transfer in polymerization and

that of the BDEs, therefore the hydrogen abstraction on the ethanol is likely not a critical step in the

kinetics of the reaction.


Figure 4.2-3: Interference of alcohols and triethylamine at 5 mM (white) and 44 mM (striped) with

the photo-oxidation of 2 g·L-1 sucrose during pressure mobilization. Relative difference in peak

height (PHRD) is calculated as 𝑃𝑃𝑃𝑃RD = 𝑃𝑃𝑃𝑃S−𝑃𝑃𝑃𝑃EtOH𝑃𝑃𝑃𝑃S

where ‘PHS’ is the height of the sucrose peak,

‘PHEtOH’ is the height of the peak of sucrose spiked with ethanol. 10 cm effective length, 50 mbar

pressure mobilization (n=3), performed on MDQ instrument.

To investigate if the change in signal was due to difference of Refractive Index (RI), the

concentration of the analyte by the analytes RI [28] was plotted against the relative difference in

peak height. If the change in signal was due to a difference in RI, the relationship would have been

linear, but this is not the case as seen on Figure 4.3-1. A control injection of pure ethanol also shows

that RI does not play a significant role in the observed interference (Figure 4.3-2). Interference with

the photo-oxidation of sucrose may be due to ethanol reacting with the hydroxide/superoxide

radical initiator or the carbon or oxygen centered radical of the sucrose during photo-oxidation

(Figure 4.2-4). To determine whether sucrose and/or ethanol were consumed or regenerated after

passing the detection window, a multiple passing experiment was performed similar to what has

been done previously [5] but using pressure mobilization and in the presence of ethanol. Sucrose

was passed in front of the detection window, then the pressure was reversed and the sucrose


passed again. This was done to a total of 28 passes for sucrose with and without ethanol. Sucrose on

its own showed results similar to the previous passing experiment [5] with a buildup of UV absorbing

species in the first five passes of the detection window followed by a decrease (Figure 4.2-5A). This

change of the signal intensity with the number of passes would not happen if the detection was due

to differences in refractive index and thus confirms that the detection of ethanol is due to photo-

oxidation and not to a difference in refractive index.

In the presence of 250 mg·L-1 and 1 g·L-1 ethanol the highest peak is observed at the 7th pass

and the peak height is lower than with sucrose. The difference in peak height decreases in parallel,

with the number of passes and then becomes constant, within the (large) experimental error after

10 passes (Figure 4.2-5B). The decrease of the peak height difference may indicate a faster

consumption of ethanol than sucrose over time. As expected, the peak height difference is smaller

with 250 mg·L-1 than 1 g·L-1 ethanol, but the behavior, decrease of the peak height differences for the

first 10 passes and plateau is similar. After 10 passes, the large amount of RSD makes the difference

not significant. The multiple passes of sucrose in the detection window showed that interference of

ethanol is at a maximum on its first pass.

Figure 4.2-4: Possible reaction scheme for the interference of ethanol with glucose photo-oxidation.


Figure 4.2-5: Peak heights in the pressure mobilization of 2 g·L-1 sucrose (black square), 2 g·L-1

sucrose and 250 mg·L-1 ethanol (circle) and 2 g·L-1 sucrose and 1 g·L-1 ethanol (cross) in 130 mM

NaOH passing the detection window multiple times (A) and the relative difference in their peak

height (B). Initial pressure was 50 mbar (outlet to inlet) for 6 min then reversed (inlet to outlet) for 3

min and reversed every 3 min for a total of 28 passes. Error bars show standard deviation (n=3).

Peak overlay can be seen in Figure 4.3-3. Performed on MDQ instrument.

The role of ethanol was further investigated by 1H and 13C NMR. As NMR experiments

require a deuterated solvent, it was first checked that the same interference of ethanol was

observed in D2O as in H2O. Injections were carried out with 130 mM NaOH in H2O as well as in D2O:

the relative peak height difference between sucrose peaks with and without ethanol in D2O is only

slightly larger in D2O (Table 4.3-2). This indicates that either the reactivities of the radicals ·OH and

·OD toward carbohydrate are not significantly different or more likely that water is not a reactant in

the ethanol interference with the photo-oxidation reaction, the carbohydrate UV irradiation being

directly responsible for the hydrogen abstraction and free radical formation. The differing values

between the D2O and H2O can be attributed to the change in peak tailing (Figure 4.3-4) due to the

higher viscosity of D2O. Assuming that the photo-oxidation reaction as well as the ethanol

interference are similar in D2O and H2O, a sample of 1 g·L-1 glucose (13C fully labelled) in D2O was


continuously pressure injected at 50 mbar for 94.5 hours (after 89 h, pressure was increased to 100

mbar to offset backpressure from the outlet vial), as previously performed [13], but in the presence

of 2 g·L-1 ethanol. The sample was consecutively analyzed by 1H (Figure 4.2-6 and Figure 4.3-5) and 13C NMR (Figure 4.2-7 and Figure 4.3-6). To determine whether ethanol was consumed or

regenerated after passing the detection window, the area of the 1H NMR signal areas of the methyl

group of ethanol were compared for the sample, control as well as fresh control (Figure 4.2-6). The

sum of total area of the normalized peaks was respectively 30 for the sample, 69 for the control and

191 for the fresh control. The loss of ethanol in the control shows that ethanol underwent thermal

decomposition over the period of 8 days (at 4°C). Taking temperature and time into account (Table

4.3-3) ca 17 % of the ethanol was consumed by the photo-oxidation reaction. This is consistent with

the multiple pass experiment and a faster consumption of ethanol than carbohydrate. Ethanol can

be directly photo-oxidized to some acetate (Figure 4.3-7) and aldehydes [17] and in high alkaline

conditions used in this work acetaldehyde (ethanal) would further react to form sodium acetate. The

radical formed from ethanol abstraction reaction (Figure 4.2-2) might also interfere with the photo-

oxidation of glucose. Numerous possible interferences of the ethanol with glucose photo-oxidation

are consistent with the NMR results; they are represented on Figure 4.2-4.


Figure 4.2-6: 1H NMR of 2 g·L-1 ethanol in the presence of 1 g·L-1 fully labelled 13C glucose

continuously and hydrodynamically injected into a 7100 CE instrument (solid line) with non-

irradiated control of the same age (dotted line) as well as freshly prepared control (dashed line).The

spectra were normalized by the number of scans (20480, 2400 and 800 respectively) and the dilution

factor (the controls were undiluted, sample was diluted 1/4.46 as described in section 2.2.1).

The photo-oxidation of glucose under these conditions has been studied previously [13,29].

The possible products of the interference that are not consistent with the NMR results are

represented on Figure 4.3-8. At point (a) of the reaction on Figure 4.2-4, glucose would form a

carbon centered radical from hydrogen abstraction by a hydroxyl radical formed by the splitting of

water or more likely by direct photo-irradiation [15]. The glucose carbon-centered radical is a

tertiary radical that is thus unlikely to abstract a hydrogen from ethanol regenerating glucose and

forming a secondary radical on ethanol. Abstraction of hydrogen from ethanol might also occur on

the free radicals (b) following the ring opening step which was described previously [13]. A glucose

would be regenerated in an altered open form containing an aldehyde functional group and thus

more prone to thermal degradation. Similar to this, ethanol might interfere with the production of

free radicals formed by the low-UV irradiation of water (Figure 4.3-9) but this is unlikely as D2O

would give a different interference. The carbon centered free radical of glyceraldehyde (c) (2,3-


dihydroxypropanal) might also abstract the same hydrogen from ethanol displacing the equilibrium

from the semidiones previously identified as possible UV absorbing intermediates [13].

Glyceraldehyde can be oxidized to form glyceric, glycolic, acetic and methanoic acid as well as

carbon dioxide (d) [30]. Hydrogen abstraction from ethanol would most likely occur from oxygen

centered radicals (e) rather than carbon centered radicals. The resulting product, (Z)-prop-1-ene-

1,2,3-triol (f), was not observed in 13C NMR (Figure 4.3-8) (alkene would produce signals between130

and 160 ppm), but it may rather be simply an intermediate. Alternative end products such as

malondialdehyde would still be formed then oxidized to carboxylates (g).

Figure 4.2-7: 13C NMR of 1 g·L-1 fully labelled 13C glucose in the presence of 2 g·L-1 ethanol

continuously and hydrodynamically injected into a 7100 CE instrument (solid line, top) and control

(dotted line, bottom). The rectangles indicate the ethanol signals.

The ethanol was not 13C labeled in this experiment but it is detected with a signal-to-noise

ratio of 43 for the fresh control and 6.5 for the sample for the shift at 18 ppm. Any products of

reactions with ethanol representing less than 20 % of the initial ethanol should thus not be detected

(Table 4.3-4). In previous work [13] glucose did not significantly thermally degrade on a month scale

in 130 mM NaOD in D2O. In the control experiment of this work some glucose was degraded


thermally by the alkaline conditions in D2O in the presence of the ethanol after 14 days. Ethanol

might form transient hemiacetal and acetal leading to higher proportion of open glucose making its

aldehyde functional group more prone to thermal degradation. The products from such degradation

have the same functional groups, carboxylates, as that from the photo-oxidation as shown by

identical shifts at 170, 180 as well as similar massifs between 60 and 80 ppm (Figure 4.2-7). The

photo-oxidation by the CE’s deuterium lamp speeds up the formation of carboxylates (≈ 170 and 180

ppm) and produced other end products (≈ 20 and 40 ppm) seen previously [13]. The formation of

glycolate and methanoate, as discussed previously [13], as well as glycerate and malonate (Table

4.3-5) would fit the shifts observed in the 13C NMR. Glucose, in its cyclic form, is observed in the

control sample through signals at 98 and 102 ppm. The loss of these signals indicates complete

degradation of glucose either by photo-oxidation or alkaline degradation. The photo-oxidation of

glucose in the presence of ethanol did not lead to the identification of any new end products thus

ruling out combination reactions of ethanol based radicals with glucose based radicals leading to

larger unique end products (Figure 4.3-8), however 2 ethanol based radicals could form butan-2,3-

diol (Figure 4.3-8) that cannot be ruled out as an end product (Table 4.3-5). Ethanol is still present in

the NMR spectra after UV irradiation, thus ethanol is not completely photo-oxidized to its end

products acetic acid and its aldehyde [17]. Previous studies have investigated the potential end

products (D-Glucose penta-acetate, D-Gluconic acid, D-Glucuronic acid and D-Glucosaccharic acid)

[31] and UV absorbing intermediates (Asorbic acid, (Z)-3-hydroxyacrylaldehyde, 2-keto-gluconic acid

and 4-deoxy-5-keto-3,6-manno saccharolactone) [16] of the photo-oxidation of glucose solutions in

the presence of oxygen, however none of these are observed in our 13C NMR spectrum (Table 4.3-5

and 4.3-6).

3.3 Quantification of ethanol by pressure mobilization

Before application to samples analysis, the effect of carbohydrate concentration, residence

time in the detection window and ethanol concentration were determined.

3.3.1 Effect of sucrose concentration and residence time on ethanol determination.

As mention in 2.2.1, sucrose was chosen as it does not reduce in basic conditions. To

determine the effect of residence time in the detection window, the difference in peak height from

ethanol interference was measured with pressure mobilization at pressures of 10, 50 and 100 mbar.

As shown previously [12], pressure mobilization at 10 mbar creates the highest signal due to the

prolonged residence time in the detection window (Figure 4.3-10) allowing more time for photo-

oxidation to take place. However this also results in the longest analysis time (17 min for 10 cm). The


differences in peak height between sucrose with and without ethanol (Table 4.3-7) are not

significantly different at 10 and 50 mbar, and using 50 mbar instead of 10 mbar reduces analysis

time from 17 to 4.6 min (3-5 fold increase when 15 % error is taken into account).

The effect of sucrose concentration on the difference in peak height from ethanol

interference was studied in the range of 0.25 to 8 g·L-1sucrose in 130 mM NaOH spiked with 1 g·L-1

ethanol (Figure 4.2-8). The largest difference in peak height is observed at 2 g·L-1 sucrose. At 4 g·L-1

sucrose the difference in peak height decreases and the difference in area even decreases to zero

(see Figure 4.3-11). The difference in peak height above 4 g·L-1 sucrose is thus likely only due to peak

broadening but not to the photo-oxidation process. Overloading of the capillary is shown by peak

tailing (Figure 4.3-12) especially at 8 g·L-1 likely due to viscous fingering. To avoid tailing the

difference in viscosity of the sample and the mobile phase needs to be kept minimal. Above 2 g·L-1

sucrose, the free radical concentration formed during the photo-oxidation may be too high and

induce more termination reactions: the ethanol is then not playing a role any more. This is similar to

the increase of the detection sensitivity adding a photo-initiator only up to a certain photo-initiator

concentration in previous work [13]. To analyze samples which do not contain photo-oxidizing

carbohydrates, carbohydrates need to be added but no more than 2 g·L-1 should be added as it

provides optimal peak height as well as the largest difference in peak height making the detection

more sensitive. The analysis of samples already containing photo-oxidizing carbohydrates is a

challenge with the pressure mobilization method. The total sugar concentration could be controlled,

possibly by dilution into a known concentration of carbohydrates where the concentration in the

sample is negligible compared to that of the added sucrose. CE is a preferable alternative over

pressure mobilization in this case as it can separate the carbohydrates, as well as other compounds,

from ethanol (discussed later). Using separation by CE, the ethanol band is free of carbohydrates and

ethanol can be detected if a known amount, such as 2 g·L-1 sucrose, of carbohydrate is added to the

BGE at the time the ethanol reaches the detection window to obtain indirect detection of ethanol as

on Figure 4.2-1B. The effect of sucrose concentration on the signal-to-noise ratio (S/N) was also

considered (Figure 4.3-13) and S/N was found to be optimal between 2 and 8 g·L-1 sucrose.


Figure 4.2-8: Sucrose peak height (solid line), sucrose spiked with 1 g·L-1 ethanol peak height (dotted

line) and difference between sucrose peak heights with and without ethanol (dashed) after pressure

mobilization at 50 mbar in a 90 cm (10 cm effective length) capillary (n = 5). Error bars on peak

height difference are ± sum of the standard deviations of both peaks (n=5). Performed on MDQ

instrument.

3.3.2 Performances of the quantification of ethanol method

Using pressure mobilization, ethanol can be quantified by either the peak area or peak

height. The precisions of both methods were compared (Table 4.2-1). The RSD values are of the

order of 2-10 % above 125 mg·L-1 of ethanol at this stage since no internal standard is used.

Quantification by peak height is more precise than that by peak area. This may be due to the

irregular peak shape and some pressure variation during the injection. Slight variation in pressure

results in variation in the width of the ethanol band: the peak tail is affected by these variations,

thus the peak area, while the peak height is not influenced. As expected, the RSD values increase

when the ethanol concentration decreases.


The limit of detection (LOD) and the limit of quantification (LOQ) were also compared in

BGEs containing sucrose and xylitol (same molar concentration) with 130 mM NaOH (Table 4.2-2).

There is in fact a range of concentration in which ethanol can be detected: too low of an ethanol

concentration leads to no interference of the photo-oxidation, while too high of an ethanol

concentration leads to complete inhibition of the photo-oxidation. The lower LOD is taken as the

concentration for which S/N is equal to 3 [13,32]; where S/N was determined as the ratio of the

signal taken as the difference of the peak heights between the sucrose only injection and the

sucrose and ethanol injection, and of the noise over estimated as the sum of the noises of each

signal. The lower LOQ is defined for the same reason as the ethanol concentration for which S/N is

equal to 10, with S/N determined as above. The upper LOQ is related to the blank injection of pure

ethanol and defined as the ethanol concentration for which the peak has a S/N equal to the sum of

10 and of the S/N of pure ethanol, with the signal taken as the one of the ethanol injections (the

lowest point in the middle valley of the peak, Figure 4.2-1A, in the presence of sucrose). Sucrose and

xylitol were chosen as they are stable in basic media.

Table 4.2-1: Comparison of peak height and peak area in the pressure mobilization of xylitol and

sucrose (2 g·L-1) in 130 mM NaOH for the quantification of ethanol (n=5). Peak height was measured

to the lowest valley point (Figure 4.2-1A). Performed on MDQ instrument.

Concentration of ethanol (mg·L-1)

Sucrose Xylitol Relative difference in peak area

RSD (%)

Relative difference in peak height

RSD (%)

Relative difference in peak area

RSD (%)

Relative difference in peak height

RSD (%)

31.25 -1.57 160 4.81 15 6.08 68 6.53 11 62.5 8.32 37 12.9 15 12.5 12 14.1 40 125 12.5 12 21.7 3.0 14.3 7.7 18.4 29 250 22.6 12 29.8 4.2 29.7 5.1 37.1 7.1 500 30.9 4.3 47.1 0.75 46.3 2.2 53.0 2.6 1000 49.4 4.7 64.2 0.82 52.8 2.4 62.8 0.56 2000 73.0 1.1 78.7 < 0.1 62.9 3.5 71.1 0.21


Table 4.2-2: Linearity of ethanol quantification, LOD, LOQ and recovery in pressure mobilization and

CE with sucrose and xylitol as background carbohydrates. n=5 for all standards and samples.

Carbohydrate Linear Equation R2 LOD (Lower) LOQ (Lower) LOQ

(Upper) Sample recovery

Quantification by pressure mobilization Sucrose y1/3 = 8.67x - 22.1 0.999 34.9 mg·L-1 117 mg·L-1 1850c mg·L-1 100%a Xylitol y1/3 = 8.18x - 18.6 0.997 29.8 mg·L-1 102 mg·L-1 1710c mg·L-1 - Quantification by CE Sucrose y = 8.76x + 226 0.987 34.4 mg·L-1 167 mg·L-1 - 110%b a From a sample of vodka b From a spiked fermentation sample c Calculated with the S/N ratio of pure ethanol at 2 g·L-1

A linear fit, determined by best empirical fit, can be achieved when the relative difference in

peak height is plotted over the cube root of the ethanol concentration. A cubic fit is likely due to the

reaction not being first order. The standard curve obtained with the MDQ maintained a similar cubic

fit when the standard was injected on 4 different days over a month were analyzed together. A

combination of the standard curves obtained by the MDQ and 7100 has a correlation coefficient of

0.98 (Figure 4.3-14) and thus indicate no significant difference between the standard curves

obtained from the MDQ or from the 7100. The curve for xylitol and sucrose are similar (Figure 4.3-

15) and may suggest a universal calibration with all photo-oxidizing carbohydrates. Sucrose leads to

more repeatable peak heights than xylitol as the photo-oxidizing even though a more regular peak

shape is obtained with xylitol in the presence of ethanol (Figure 4.3-16). The S/N was also compared

between the 7100 CE and the MDQ instruments (Figure 4.3-17). The latter instrument was found to

be more sensitive for ethanol detection, by 40 % (S/N of 149 for the 7100 CE and of 209 for the

MDQ), as was previously observed for carbohydrates [12]. The lamp usage time may play a role and

the MDQ instrument had a more recent lamp (990 h) than the 7100 CE one (1950 h).

Pressure mobilization was applied to the determination of ethanol in a “real” well-

characterized sample to benchmark the method: vodka was chosen since as it has a considerable

ethanol concentration, no carbohydrates and minimal interfering compounds. The declared ethanol

concentration was 37 %, the sample was diluted by 1:600 with a solution of 2 g·L-1 sucrose in 130

mM NaOH. Quantification of the ethanol content was found identical to the one determined by the

vodka manufacturer (to 2 significant digits) indicating that trace amounts of other compounds [33]

did not have an impact on the quantification of ethanol by photo-oxidation interference. To measure

ethanol in samples containing both carbohydrates and ethanol, a CE method was developed as

follows.


3.3.2 Quantification of ethanol in real samples by CE

As mentioned previously, samples containing both carbohydrates and ethanol require

separation before ethanol determination. CE was used to separate ethanol in a standard containing

glucose and fructose. A BGE of 130 mM NaOH was placed in the inlet and outlet vials and the

capillary was flushed with a BGE containing 130 mM NaOH with 2 g·L-1 of sucrose. Sucrose, as a

disaccharide, has an apparent mobility lower than the EOF marker but higher than the glucose and

fructose, and thus should completely migrate past the detection window before glucose and

fructose migrate past the detection window. Ethanol, due to its higher pKa than that of sucrose,

should pass the detection window whilst sucrose is still in the background. As a result, ethanol can

be detected by interference with the photo-oxidation of sucrose only, while glucose and fructose

can be detected directly by photo-oxidation (Figure 4.2-9). When the electric field was applied for

the entire duration of the separation, the detection of the ethanol peak was not repeatable due to a

large amount of instability (Figure 4.3-18). When the electric field was stopped after 12 min and 50

mbar pressure applied, the ethanol peak was repeatable (Figure 4.2-9B) and separated sufficiently

from the carbohydrates. The carbohydrates were incompletely separated from each other in

comparison to separations seen previously in similar conditions [5,13]. In this case, a reinjection in

conditions optimal to the separation of carbohydrates should be done separately. As expected there

was a drop in the baseline at 26-27 min indicating that the sucrose has completely migrated out of

the capillary. As in pressure mobilization, the extent of interference was proportional to the amount

of ethanol added (Figure 4.2-9B).


Figure 4.2-9: Detection of ethanol and carbohydrates via CE (A) and detection of varying

concentrations of ethanol by interference with the photo-oxidation (B). BGE in outlet and inlet was

130 mM NaOH, BGE in capillary was 130 mM NaOH + 2 g·L-1 of sucrose. Migration was by electric

field (24 kV) for 12 min followed by pressure mobilization at 50 mbar. Assignment of ethanol

concentrations for (B): 2 g·L-1 (solid line), 1 g·L-1 (short dotted line), 500 mg·L-1 (short dashed line),

250 mg·L-1 (dotted line), 125 mg·L-1 (dashed line) and 0 mg·L-1 (dashed-dotted line). Current was 147

µA. Performed on 7100 CE instrument.

The maximum increase in temperature inside the capillary due to Joule heating was only 1.9

°C as calculated previously [13,34]. The LOD, and LOQ and recovery were determined (Table 4.2-2)

and are similar to the one obtained by pressure mobilization only on simpler samples. The LOD of

the CE method (34.4 mg·L-1) can compete with the sensitivity of an optical alcohol meter (LOD <

1580 mg·L-1 [35]) for the ability to determine carbohydrates with the same equipment. The

instability was similar with BGEs containing either 2 g·L-1 or 0.5 g·L-1 sucrose (Figure 4.3-19). A diluted

fermentation sample spiked with 500 mg·L-1 of ethanol was used to test the recovery of the method.

The sample showed 110 %. The precision may be improved with an internal standard as discussed in

previous work [5]. The accuracy and robustness of the method are however demonstrated using this

complex matrix without any filtration being required. Vials for online fermentation monitoring by CE


were recently developed and tested on acids [36]. The CE separation presented in this work will

allow the method to be extended to the quantification of both ethanol and carbohydrates in

complex mixtures (not in the same injection) for a comprehensive online monitoring of ethanol

fermentations.

4 Conclusions

Ethanol can be detected and quantified by interference with carbohydrate photo-oxidation.

Methanol, propanol or triethylamine are also shown to be detected by this method. The presence of

ethanol during photo-oxidation did not lead to the observation by 1H or 13C NMR of any end-

products that were not seen previously in the absence of ethanol. The photo-oxidation might be due

to direct UV irradiation of the carbohydrate leading to the formation of free radicals and then UV

absorbing intermediate. Ethanol might react with oxygen centered radicals along this reaction

pathway and partially suppress the UV absorption.

This detection can be utilized with pressure mobilization for a simple and fast detection of

ethanol with a capillary electrophoresis instrument or an even simpler set-up. When sucrose is used

as the photo-oxidizing carbohydrate in pressure mobilization, the optimal concentration is 2 g·L-1.

This detection has a LOD and LOQ of 34.9 and 117 mg·L-1 respectively. The quantitative recovery

(100%) was measured with a sample of vodka. CE was used to determine ethanol in spiked

fermentation samples containing glucose and fructose. Ethanol in CE can be detected as an indirect

peak when sucrose is placed in the BGE in the capillary. A fermentation sample spiked with ethanol

showed 110 % recovery, showing that the robustness of the CE with direct UV detection of

carbohydrates also applies to ethanol quantification. The ability of CE with direct UV detection to

monitor both complex mixtures of carbohydrates, as shown by previous research, and now to

monitor ethanol, makes it highly promising method to monitor ethanol fermentations online. The

method can be applied to a number of compounds, for example antioxidants, taking advantage of

the robustness of the method.

5 Acknowledgements

The authors wish to acknowledge Dr Yohann Guillaneuf (Aix-Marseille University) for

discussions and David Fania and Prof. Kamali Kannangara for 13C glucose.


References [1] M.J. Playne, J. Sci. Food Agric. 36 (1985) 638. [2] R. Pecina, G. Bonn, E. Burtscher, O. Bobleter, J. Chromatogr. 287 (1984) 245. [3] V.P. Hanko, J.S. Rohrer, Anal. Biochem. 283 (2000) 192. [4] A.I. Ruiz-Matute, O. Hernández-Hernández, S. Rodríguez-Sánchez, M.L. Sanz, I.

Martínez-Castro, J. Chromatogr. B 879 (2011) 1226. [5] J.D. Oliver, M. Gaborieau, E.F. Hilder, P. Castignolles, J. Chromatogr. A 1291 (2013)

179. [6] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, P. Gareil, Talanta 99 (2012) 202. [7] K.D. Altria, J.S. Howells, J. Chromatogr. A 696 (1995) 341. [8] L.A. Colon, R. Dadoo, R.N. Zare, Anal. Chem. 65 (1993) 476. [9] T. Soga, M. Serwe, Food Chem. 69 (2000) 339. [10] S. Rovio, J. Yli-Kauhaluoma, H. Siren, Electrophoresis 28 (2007) 3129. [11] S. Rovio, H. Simolin, K. Koljonen, H. Siren, J. Chromatogr. A 1185 (2008) 139. [12] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, J.M. Mallet, P. Gareil, Anal. Chem. 83

(2011) 7381. [13] J.D. Oliver, A.A. Rosser, C.M. Fellows, Y. Guillaneuf, J.-L. Clement, M. Gaborieau, P.

Castignolles, Anal. Chim. Acta 809 (2014) 183. [14] M.G. Gonzalez, E. Oliveros, M. Wörner, A.M. Braun, J. Photochem. Photobiol. C:

Photochem. Rev. 5 (2004) 225. [15] G.O. Phillips, in: L.W. Melville, R.S. Tipson (Eds.), Advances in Carbohydrate

Chemistry, Academic Press, 1963, p. 9. [16] T.C. Laurent, J. Am. Chem. Soc. 78 (1956) 1875. [17] J.L. Bolland, H.R. Cooper, Proc. R. Soc. London, Ser. A 225 (1954) 405. [18] W.H. Urry, F.W. Stacey, E.S. Huyser, O.O. Juveland, J. Am. Chem. Soc. 76 (1954) 450. [19] G.A. Mortimer, J. Polym. Sci., Part A1: Polym. Chem. 4 (1966) 881. [20] B. Grassl, A.M. Alb, W.F. Reed, Macromol. Chem. Phys. 202 (2001) 2518. [21] G.R. Fulmer, A.J.M. Miller, N.H. Sherden, H.E. Gottlieb, A. Nudelman, B.M. Stoltz, J.E.

Bercaw, K.I. Goldberg, Organomet. 29 (2010) 2176. [22] T. Le Saux, H. Cottet, Anal. Chem. 80 (2008) 1829. [23] B.J.M. Hannoun, G. Stephanopoulos, Biotechnol. Bioeng. 28 (1986) 829. [24] L.G. Longsworth, J. Am. Chem. Soc. 75 (1953) 5705. [25] U.S. Nandi, M. Singh, P.V.T. Raghuram, Makromolekul Chem 183 (1982) 1467. [26] M.S. Kharasch, J.L. Rowe, W.H. Urry, J. Org. Chem 16 (1951) 905. [27] Y.-R. Luo, Handbook of bond dissociation energies in organic compounds, CRC Press,

Boca Raton, Fla., 2003. [28] D.R. Lide, Hdbk of Chemistry & Physics 74th Edition, Taylor & Francis, 1993. [29] B.C. Gilbert, D.M. King, C.B. Thomas, J. Chem. Soc., Perkin Trans. 2 (1982) 169. [30] L.M. Andronov, Z.K. Maizus, B Russ Acad Sci Ch+ 16 (1967) 504. [31] G.O. Phillips, G.J. Moody, J Chem Soc (1960) 3398. [32] A.Z. Carvalho, J.A.F. da Silva, C.L. do Lago, Electrophoresis 24 (2003) 2138. [33] L.-K. Ng, Anal. Chim. Acta 465 (2002) 309. [34] C.J. Evenhuis, R.M. Guijt, M. Macka, P.J. Marriott, P.R. Haddad, Anal. Chem. 78

(2006) 2684. [35] M. Rocchia, M. Ellena, G. Zeppa, J. Agric. Food Chem. 55 (2007) 5984. [36] H. Turkia, S. Holmström, T. Paasikallio, H. Sirén, M. Penttilä, J.-P. Pitkänen, Anal.

Chem. 85 (2013) 9705.



Supporting information for:

Ethanol determination using pressure mobilization and Free Solution Capillary Electrophoresis (CE)

by photo-oxidation assisted UV detection

James D. Oliver,1 Marianne Gaborieau,2 and Patrice Castignolles*1


2 University of Western Sydney (UWS), Molecular Medicine Research Group (MMRG), School of Science and Health, Locked Bag 1797, Penrith NSW 2751, Australia, [email protected]


This supporting information contains supplementary chromatograms and

electropherograms as well as peak heights and peak areas values from the Pressure Mobilization and

Free Solution Capillary Electrophoresis (CE) experiments. It also contains supplementary NMR

spectra and data on the 1H and 13C NMR experiments and hypothesized reaction schemes.

If the detection of ethanol described in this work was due to refractive index detection, then

the relative different in peak height should be proportional to the difference of refractive index of

the analyte and sucrose multiplied by the ethanol concentration. Such a plot is given a Figure 4.3-1

and is not linear.



Figure 4.3-1: Relationship between the multiplication of the analyte Refractive Index (RI) by the

concentration of the analyte and the relative peak difference. The analytes are methanol (square),

ethanol (triangle), isopropanol (star), tert-butanol (pentagon) and triethylamine (circle). RI values are

20 °C [1].


Figure 4.3-2: Blank of injection 130 mM NaOH (green), 1 g·L-1 Ethanol in 130 mM NaOH (blue), 2 g·L-1

sucrose in 130 mM NaOH (black) and 1 g·L-1 Ethanol in 2 g·L-1 sucrose in 130 mM NaOH (red).

The bond dissociation energy (BDE) are lower for the C-H bond in alpha position of an

alcohol than for the O-H bond of the alcohol functional group.

Table 4.3-1: BDE from [2].

Molecule Bond BDE (kJ/mol) Uncertainty Methanol H-CH2-OH 401.9 0.6 Ethanol CH3-C-H2OH 396.6 na Propan-2-ol (CH3)2C-HOH 381 4 2-methylpropan-2-ol (CH3)3CO-H 418.4 8.4 Triethylamine (CH3CH2)2NC-H(H)CH3 379.5 2 Water H-OH 499.2 0.2 Methanol H2CO-H 436.0 3.8 Ethanol H3CCH2O-H 437.6 3.3 Propan-2-ol (CH3)2CHO-H 442.3 2.8 Vinyl alcohol H2C=C(H)O-H 355.6 na 2-methylpropan-2-ol (CH3)3CO-H 439.7 4.2


Passing experiment with pressure mobilization

Figure 4.3-3: Pressure mobilization of 2 g·L-1 sucrose (black), 2 g·L-1 sucrose and 250 mg·L-1 ethanol

(red-1 min offset) and 2 g·L-1 sucrose and 1 g·L-1 ethanol (blue-2 min offset) in 130 mM NaOH passing

the detection window multiple times. Initial pressure was 50 mbar (outlet to inlet) for 6 min then

reversed (inlet to outlet) for 3 min and reversed every 3 min for a total of 28 passes. Performed on

MDQ instrument.


Table 4.3-2: Comparison of the effects of ethanol inhibited photo-oxidation of sucrose in 130 mM

NaOH in H2O and in D2O. Values are normalized by migration time. Examples of the corresponding

elugrams are shown on Figure 4.3-2.

Concentration of ethanol in 2 g·L-1 sucrose in 130 mM NaOH (mg·L-1)

Sucrose peak area (x 104)

RSD (%)

Relative difference in peak area (%)

RSD (%)

Sucrose peak height (mAU)

RSD (%)

Relative difference in peak height (%)

RSD (%)

130 mM NaOH in H2O 2000 0.38 6.5 71 3.1 0.13 0.084 82 1.7 1500 0.47 2.6 64 6.5 0.18 1.1 75 2.3 1000 0.64 2.4 51 11 0.23 1.8 68 2.9 500 0.85 4.7 35 18 0.34 4.0 53 3.6 250 0.98 6.8 25 22 0.43 5.9 40 3.1 0 1.3 14 - - 0.72 8.0 - - 130 mM NaOH in D2O 2000 0.35 5.0 68 2.0 0.05 4.9 88 ≥ 0.1 250 0.97 3.3 12 10 0.32 3.7 20 12 0 1.1 13 - - 0.40 4.9 - - RSD = Relative standard deviation

Figure 4.3-4: Pressure mobilization of sucrose in 130 mM NaOH in H2O (solid line) and 130 mM NaOD

inD2O (dotted line). Performed on MDQ instrument.


1H and 13C NMR of fully labeled 13C glucose in the presence of ethanol, before and after irradiation

Figure 4.3-5: 1H NMR of 1 g·L-1 fully labelled 13C glucose in the presence of 2 g·L-1 ethanol

continuously and hydrodynamically injected into a 7100CE instrument for 94.5 h (black), control with

no UV exposure for the same length of time (blue) and prepared fresh (red).


Figure 4.3-6: 13C NMR of 1 g·L-1 fully labelled 13C glucose in the presence of 2 g·L-1 ethanol

continuously and hydrodynamically injected into a 7100CE instrument for 94.5 h (black), control with

no UV exposure for the same length of time (blue) and freshly prepared control (red).

Table 4.3-3: Calculation of rate of ethanol thermal decomposition obtained from the integration of

Figure 4.2-4.

Time spent at 4 °C

Time spent at 20 °C

Total equivalent time at 20 °C *

Total loss in peak area

Loss in peak area due to thermal decom-position

Rate of thermal decom-position at 20 °C (loss/hour)

Peak area loss by photo-oxidation

Peak area loss by photo-oxidation (%)

Control 317 3 108 122 122 1.13 0 0 Sample 0 118 118 161 161 # 1.13 28 17 * assuming that the decomposition reaction is 3 times slower at 4 °C than 20 °C. # calculated as the loss of peak area in the control multiplied by the ratio of time spent at 20 °C by the control and the sample.


Table 4.3-4: Estimate of the minimal concentration (E) of end products that, resulting from

decomposition of ethanol, could be detected by 13C NMR.

Signal to Noise ratio (S/N) of ethanol signal at 18 ppm in fresh control (A)

Number of scans of fresh control (undiluted)

(B)

Signal to Noise ratio (S/N) of ethanol signal at 18 ppm in sample

Dilution factor of sample (C)

Number of scans of sample (D)

𝐸𝐸 = �3

𝐴𝐴𝐶𝐶 × √𝐷𝐷𝐵𝐵

� × 100

43 75776 6.5 4.46 184320 20 %

NB: this calculation assumes that all signals of interest are fully relaxed between scans in the 13C NMR experiment. Table 4.3-5: Predicted 13C shifts of potential end products of carbohydrate photo-oxidation in the

presence of oxygen. Prediction done with ChemDraw Ultra 12. Bold, underlined chemical shifts are

not observed in the 13C NMR spectrum (Figure 4.2-4).

D-Glucose penta-acetate [3]

D-Gluconic acid [3]

D-Glucuronic acid [3]

D-Glucosaccharic acid [3]

Malonic acid (Malonate)

Glyceric acid (Glycerate)

Butan-2,3-diol

200.8 170 176 176 176 169 176 94.9 88.3 74.7-69.0 73.1-64.4 71.3-67.3 73.4-68.3 45.2 64.4-71.5 75.3 21.0, 20.7 18.6

Table 4.3-6: Predicted 13C shifts of potential UV absorbing intermediates from carbohydrate photo-

oxidation as studied by [4]. Prediction done with ChemDraw Ultra 12. Bold, underlined chemical

shifts are not observed in the 13C NMR spectrum (Figure 4.2-4).

Asorbic acid (Z)-3-hydroxyacrylaldehyde (reductone)

2-keto-gluconic acid (diol form)

4-deoxy-5-keto-3,6-manno saccharolactone

191.1 175 180.2 173 167 165 167 162 119 137 116 105.3 72.6-63.4 117 67.4 76.5, 75.9


Mechanism of the photo-oxidation and of the interference with ethanol

Figure 4.3-7: Oxidation of ethanol radical to acetic acid (a-d) adapted from [5] and to butan-2,3-diol

(e). G-H represents glucose and G· represents glucose derived radical as shown in Figure 4.2-4.


Figure 4.3-8: Possible but unobserved products of glucose photo-oxidation in the presence of

ethanol. Unobserved chemical shifts are in brackets.

Figure 4.3-9: Possible interference of water derived radicals by ethanol.


Effect of pressure, sucrose concentration, CE instrument and photo-oxidizing sugar on the detection

of ethanol in pressure mobilization

Table 4.3-7: Effect of pressure on the peak height (n=5) of sucrose with and without ethanol (Figure

4.3-5).

Pressure (mbar)

2000 mg·L-1 sucrose, A

2000 mg·L-1 sucrose spiked with 1000 mg·L-1 ethanol, B

Difference (A-B) (Difference in peak height) x (sucrose velocity)

Peak height (mAU)

SD Peak height (mAU)

SD Peak height (mAU)

Sum of SDs

Value SD

100 1.62 0.0425 0.858 0.0835 0.764 0.126 1.3 0.21

50 6.12 0.0327 3.80 0.156 2.32 0.189 1.1 0.09

10 15.4 0.393 12.9 0.555 2.54 0.948 0.56 0.21


Figure 4.3-10: The velocity of sucrose at 10, 50 and 100 mbar (A) is indicated by star symbols on

dashed line Error bars are ± 15 % to account for pump fluctuations. Effect of residence time in the

window (B) on peak height of sucrose 2 g·L-1 (squares on dotted line) and sucrose spiked with 1 g·L-1

ethanol (squares on solid line) in pressure mobilization at different pressures. Error bars are ±

standard deviations (n=5). Performed on MDQ instrument.


Figure 4.3-11: Peak areas of sucrose (solid line) and sucrose spiked with 1 g·L-1 ethanol (dotted line),

as well as difference between sucrose peak areas with and without ethanol (dashed) after pressure

mobilization at 50 mbar in a 90 cm (10 cm effective length) capillary (n = 5). Error bars on peak area

difference are ± sum of the standard deviations of both peaks (n=5). Performed on MDQ instrument.


Figure 4.3-12: Sucrose peak at 500 mg·L-1 (black solid), 1000 mg·L-1 (black dotted), 2000 mg·L-1 (red

solid) 4000 mg·L-1 (red dotted) and 8000 mg·L-1 (blue solid) without ethanol (A) with 1000 mg·L-1

ethanol (B). Performed on MDQ instrument.


Figure 4.3-13: Effect of sucrose concentration on the signal to noise ratio (S/N).

Figure 4.3-14: Standard curve obtained from MDQ (red) obtained from 4 separated days spaced over

a month, 7100 (black) and a combination of the 2 (blue).


Figure 4.3-15: Calibration curve of ethanol concentration against difference in peak height for

sucrose (black circles) and xylitol (red triangles) (n=5).

Figure 4.3-16: Pressure mobilization of 5.8 mM sucrose (black) and xylitol (red) in the presence of 1

g·L-1 ethanol. Performed on MDQ instrument.


Figure 4.3-17: Comparison of the signal to noise ratio of a sucrose peak (2 g·L-1) between the 7100 CE

and the MDQ instruments (n=5).

Figure 4.3-18: CE of ethanol when the electric field (24 kV) was applied for the entire separation.

Performed on 7100 CE instrument.


Figure 4.3-19: Detection of 1 g·L-1 ethanol via CE by interference with the photo-oxidation of sucrose.

Indirect ethanol peak is shown in the dashed boxes. BGE in outlet and inlet was 130 mM NaOH, BGE

in capillary was 130 mM NaOH + 2 g·L-1 of sucrose (black, S/N = 37) and 130 mM NaOH + 0.5 g·L-1 of

sucrose (red, S/N = 36). Migration was by electric field (24 kV) for 12 min followed by pressure

mobilization at 50 mbar. Current was 160 µA. Performed on 7100 CE instrument.

References: [1] D.R. Lide, Hdbk of Chemistry & Physics 74th Edition, Taylor & Francis, 1993. [2] Y.-R. Luo, Handbook of bond dissociation energies in organic compounds, CRC Press, Boca

Raton, Fla., 2003. [3] G.O. Phillips, G.J. Moody, J Chem Soc (1960) 3398. [4] T.C. Laurent, J. Am. Chem. Soc. 78 (1956) 1875. [5] J.L. Bolland, H.R. Cooper, Proc. R. Soc. London, Ser. A 225 (1954) 405.


5. Publication “Simple and robust monitoring of ethanol fermentations by capillary

electrophoresis”

5


5.1.1 Fermentation monitoring by CE

Lignocellulosic fermentations have a complex carbohydrate mixture and sample matrix. In

this project, CE with direct UV detection has been shown to be a simple and robust method for

carbohydrate determination in lignocellulosic fiber samples. With ethanol determination by a

compatible method a possibility after the 3rd publication, various ethanol fermentation samples

including ones from lignocellulosic fermentations were studied. This publication was undertaken to

determine the best BGE for separation of carbohydrates in a fermentation samples as well as

comparing them to HPAEC and HPLC on a cation exchange resin. The final experiment demonstrated

the monitoring of sugars, sugar alcohols and ethanol in a lignocellulosic fiber fermentation. The acid

pre-treatment and an enzymatic hydrolysis were used to maximize the liberation of fermentable

carbohydrate available for fermentation to ethanol and arabitol. Two ethanologens were used in this

study to generate fermentation samples. Simple fermentation samples were produced by

fermenting glucose and fructose by the bacterium Zymomonas mobilis. More complex mixtures

including lignocellulosic samples were fermented by the yeast Pichia stipitis. Both organisms have

been previously used successfully (1.2.3.7). The research question of the 4th publication was “Can CE

be used for monitoring of lignocellulosic fermentations?”

This publication contributed to the field of study by providing an in-depth investigation on

the effect the BGE has on the analyte’s electrophoretic mobility, resolution and the resolution

achieved per min (denoted Tres in this publication). Based on this study, we were able to recommend

the conditions that would maximize the throughput or resolution depending on the complexity of

the mixture and demonstrate the ability of CE to determine sugars, sugar alcohols and ethanol in

complex lignocellulosic fiber fermentation samples.



This publication contributed to my personal development by giving me the opportunity to

present a poster at the international conference located in Dresden, the 6th International

Symposium on the Separation and Characterization of Natural and Synthetic Macromolecules (SCM-

6 see “Conference and seminar presentations”). I was also provided training on HPAEC. Professional

development was achieved through my continued collaboration with Professor Emily Hilder and a

new collaboration with Dr Naama Karu both from ACROSS at UTas. Determination of resolution in

CE, which is unusual in comparison to HPLC and HPAEC was discussed with statistician Dr Glenn

Stone of UWS (mentioned in Acknowledgements). I also trained undergraduate student and co-

author Adam Sutton in CE.


direction of the paper. Adam Sutton reproduced some of the injections that determined the optimal

concentration for the BGE. These results are presented in the supporting information. Prof. Emily

Hilder had the idea to use HPAEC and Dr Naama Karu provided training on HPAEC. Michael Phillips,

Julie Markham and Paul Peiris provided direction and feedback on carrying out the fermentations

that were used as samples for this publication.

I performed all background research, experiments and data acquisition except for some of

the injections noted in the supporting information (Table 5.3-2) which were performed by Adam

Sutton. I performed all data analysis as well as writing the first draft of the publication. I developed

the idea of comparing different mixtures of BGE and comparing simple BGE quantitatively in regards

to electrophoretic mobility, resolution and time of separations. I developed the idea to compare not

only HPLC as done in the first publication but also do a comparison of CE to HPAEC building on Prof.

Hilder’s idea to use HPAEC. HPAEC was not available at UWS even though it is a preferred method in

the field for its high sensitivity and flexible separation. A second trip to UTas was organized for the

purpose of using HPAEC.


5.2 Publication

Simple and robust monitoring of ethanol fermentations by capillary electrophoresis

James D. Oliver,1,2 Adam T. Sutton,1 Naama Karu,3 Michael Phillips,2 Julie Markham,2 Paul Peiris,2

Emily F. Hilder,3Patrice Castignolles1*

1 University of Western Sydney, Australian Centre for Research On Separation Sciences (ACROSS), School of Science and Health, Parramatta Campus, Locked Bag 1797, Penrith NSW 2751, Australia

2 University of Western Sydney, School of Science and Health, Hawkesbury Campus, Locked Bag 1797, Penrith NSW 2751, Australia

3Australian Centre for Research on Separation Science (ACROSS), School of Chemistry, University of Tasmania, Hobart TAS 7001, Australia, [email protected]

*Corresponding author [email protected]

Running title: capillary electrophoresis to monitor fermentations

Synopsis

Free solution capillary electrophoresis (CE), or capillary zone electrophoresis, with direct UV

detection was used for the first time for the determination of mono- and disaccharides, sugar

alcohols and ethanol in fermentation broths. Sample preparation proved to be minimal: no

derivatization or specific sample purification was needed. The CE conditions can be adapted to the

type of fermentation by simply altering the background electrolyte (BGE). 130 mM KOH or 130 mM

NaOH as the BGE led to the fastest analysis time when monitoring simple fermentations. A mixture

of 65 mM NaOH and 65 mM LiOH led to a 19 % improvement in resolution for a complex mixture of

carbohydrates. Quantification of a simple carbohydrate fermentation by CE showed values in close

agreement with that of High Performance Anion Exchange Chromatography (HPAEC) and High

Performance Liquid Chromatography (HPLC) on a cation exchange resin. For complex fermentations,

quantification of carbohydrates by HPLC and CE led to similar results while CE requires an injection

volume of only 10-20 nL. Analysis of an ethanol fermentation of hydrolyzed plant fiber

demonstrated the robustness of the separation and detection of carbohydrates, as well as ethanol.

Ethanol determination is by coupling the CE method to pressure mobilization, using the same

instrument and the same sample.

Keywords: capillary electrophoresis, carbohydrate, fermentation, high performance anion exchange

chromatography (HPAEC), high performance liquid chromatography (HPLC), resolution




1 Introduction:

Research on ethanol fermentations has been increasing due to its ability to replace

petroleum as a liquid fuel or building block for commodity plastics. Significant advances have been

made in relation to the biomass to ethanol conversion process (1). Methods for analyzing the

feedstock and monitoring the fermentation substrates and products have not progressed as rapidly.

Gas Chromatography (GC) is commonly used to monitor ethanol as well as other fermentation

products (2) however carbohydrates require derivatization to become volatile for separation (3, 4).

High Performance Liquid Chromatography (HPLC) using Hydrophilic Interaction Liquid

Chromatography (HILIC), ligand exchange and ion exchange resins have proven useful in monitoring

ethanol fermentations from carbohydrates with little sample preparation (5). HILIC can provide

separation of most carbohydrates and is particularly useful for oligosaccharides. However, samples

need to be diluted in a relatively polar organic mobile phase such as acetonitrile which can cause

precipitation of proteins and polysaccharides in the sample. For higher recovery and resolution, solid

phase extraction needs to be carried out for removal of interfering compounds (6). Lead based

ligand exchange columns are popular for analyzing both acid treated fiber and fermentation samples

(7-9). Fermentation monitoring with this column, as with most, requires sample pre-treatment,

centrifugation, filtration of the supernatant and in some cases sample neutralization (10). Sample

preparation can lead to variability in the amount of carbohydrates determined (7). Columns based

on hydrogen-form exchange resins are popular in monitoring fermentation of complex mixtures

based on their ability to separate carbohydrates as well as the fermentation products ethanol and

acetic acid (11-13). Samples only require filtration prior to injection on this column (10) however

there are issues with the plant fiber carbohydrates galactose, xylose and mannose co-eluting (14,

13). Reverse phase liquid chromatography leads to higher resolution of these sugars (15) however

retention as well as selective and sensitive UV or visible detection require derivatization, which is

tedious and may introduce errors. High Performance Anion Exchange Chromatography (HPAEC)

utilizing pulsed amperometric detection (PAD) was previously used to monitor carbohydrates in

fermentation media with filtration and dilution as the only pre-treatment (16, 17). PAD provides

higher detection sensitivity and can be used with a gradient flow in contrast to Refractive Index (RI)

detection which is most suitable for the HPLC methods previously mentioned. HPAEC has been used

for substrate analysis and monitoring fermentation of carbohydrates utilizing a column with a

MicrobeadTM pellicular resin (18, 19). A separate column containing a macroporous polymeric anion

exchange resin was required for the determination of ethanol and sugar alcohols in samples (20).


Free solution capillary electrophoresis (CE) is a fast and robust technique for monitoring

complex carbohydrate samples obtained from plant fibers and it requires minimal injection volumes

and sample treatment (21, 22). CE enables the detection of carbohydrates with indirect UV

detection, mass spectrometry and also, more recently, a simple direct UV detection.(23) CE in highly

alkaline electrolytes (pH≥12.6) has shown to be successful for the separation of carbohydrates in

fruit juice (24), hydrolyzed plant fiber (22, 21) and glycoproteins (25), but it has never been applied

to fermentation monitoring. In this system, the pH of the electrolyte is above the pKa of the

carbohydrates, thus the analytes are charged and separated based on their charge-to-size ratio. The

flow of ions adsorbed on the capillary surface creates an electro-osmotic flow (EOF), marked by an

uncharged molecule. The electrophoretic velocity of the carbohydrates is in the opposite direction to

the EOF, the separation thus being counter-EOF (Figure 5.3-1 and Equation 5.3-1). An unexpected

yet sensitive and robust direct UV detection at 250-266 nm is made possible by an electric field

assisted photo-oxidation reaction (25, 21, 26). The application of free solution CE with direct UV

detection in biotechnology is increasing (27, 28, 22). Previous work has demonstrated the superior

robustness and resolution of CE with direct UV detection over HPLC with a cation exchange resin for

the analysis of treated plant biomass (21). Similar work has investigated the detection of

carbohydrates in biomass based samples with comparison to HPAEC (22) and HPLC with ligand

exchange (28). Detection of ethanol from fermentation samples via photo-oxidation interference has

been developed very recently: the free solution CE method is used to separate ethanol from the

matrix and is coupled to a simple pressure mobilization in the presence of sucrose for an indirect

detection of ethanol (29). When combined with adequate separation of carbohydrates, an ethanol

fermentation may thus be monitored online with the same equipment both in terms of

carbohydrates and ethanol. Analysis of fermentation samples by CE with direct UV detection as well

as its comparison to HPLC and HPAEC have not previously been undertaken. The aim of this paper

was to develop and evaluate a simple and robust method of monitoring carbohydrates and end

products, including ethanol, in fermentation samples using CE with direct UV detection. CE with

direct UV detection was then quantitatively compared to established methods of HPLC on an ion

exchange resin and HPAEC for fermentation samples with model carbohydrate mixtures. The

method was then used to monitor the ethanol fermentation of a lignocellulosic fiber.


2 Materials and Methods:

2.1 Materials

MilliQ quality water (Millipore, USA) was used throughout the research. Ammonium sulfate

99 %, L-arabitol 98 %, xylitol ≥99 %and xylose ≥99 % were obtained from Alfa Asear (Ward Hill, MA,

USA). Sodium hydroxide pellets (NaOH) ≥99.8 %, disodium hydrogen phosphate powder ≥99 %,

lithium hydroxide monohydrate ≥98 %, dimethyl sulfoxide (DMSO)≥99.5 %, magnesium chloride

hexahydrate ≥99%, D+glucose ≥99.5 %, D+galactose ≥99 %, L-rhamnose monohydrate ≥99 %,

D+fructose ≥99 % L-arabinose ≥99 % and absolute ethanol ≥99.5 % were obtained from Sigma-

Aldrich (Sydney, Australia). Potassium hydroxide ≥85 % was obtained from Chemsupply (Adelaide,

Australia). Monopotassium phosphate ≥99 %, and lactose (4.8-5.4 % water, other impurities < 0.3 %)

were obtained from Univar (Ingleburn, Australia). Mannose (Lot # 76585) was obtained from AJAX

Chemicals (Sydney, Australia). Malt extract agar, technical agar and yeast extract were obtained

from Oxoid (Thermo Fisher Scientific, Adelaide, Australia). pH was measured with a Mettler Toledo

(Melbourne, Australia) InPro® 3250/120/Pt1000 pH electrode with a Seven Compact™ pH/Ion S220

pH meter utilizing pH standards of 7.00 and 10.01.

2.2 Microorganisms, Media and Fermentation Parameters

Zymomonas mobilis (ATCC 10988) was obtained from the University of New South Wales. Z.

mobilis was cultured on glucose agar (20.0 g·L-1 glucose, 10.0 g·L-1 yeast extract, 15.0 g·L-1 technical

agar and 2.0 g·L-1 KH2PO4) at 30 °C for 48 h. Pichia stipitis (WM 810) was obtained from Westmead

Clinical School, University of Sydney, originally from Centraalbureau voor Schimmelcultures (CBS No.

5773). P. stipitis was cultured on malt extract agar. Inocula were prepared in 50 mL of liquid glucose

medium (20.0 g·L-1 glucose, 10.0 g·L-1 yeast extract, 1.0 g·L-1 MgCl2, 1.0 g·L-1 (NH4)2SO4 and 1.0 g·L-1

KH2PO4) in a 250 mL conical flask and incubated at 30 °C for 12 h stationary for Z. mobilis and at 100

rpm for P. stipitis. The fermentation was initiated by centrifuging the inoculum, removing the

supernatant and re-suspending the pellet in the fermentation medium (yeast extract, 1.0 g·L-1 MgCl2,

1.0 g·L-1 (NH4)2SO4 and 1.0 g·L-1 KH2PO4) with carbohydrates or plant fiber (composition and

concentrations described in the results and discussion section) in a 250 mL conical flask and

incubated at 30 °C. All samples were syringe filtered through a 0.2 µm nylon membrane (Grace,

Sydney, Australia) for sterilization.


2.3 Preparation of plant fiber

Cladodes of Opuntia fiscus-indicia were obtained from the wild in Richmond, NSW, Australia

in November 2010 and identified by the National Herbarium of New South Wales. They were

homogenized with water and centrifuged at 3000 rpm for 30 min. The insoluble fraction was dried to

a constant weight at 75 °C and milled to fit through a 1 mm sieve. Fiber was pre-treated by acid

hydrolysis; 5 % (w/v) of dried fiber was added to a 2 % (v/v) sulfuric acid and heated by reflux to 134

°C for 2 h. The pH of the solution was adjusted to 5.0 with barium hydroxide and enzyme hydrolysis

was carried out at 40 °C, pH 5 with 1 g of Viscozyme® L (Novozymes, Denmark).

2.4 High performance liquid chromatography (HPLC)

Separations were performed on a Shimadzu 20A Series System with a RID-10A refractive

index detector (Shimadzu Scientific Instruments, Rydalmere, Australia) and a Sidewinder column

heater (Restek, Bellefonte, PA, USA). Separations were performed using a Bio-Rad HPX-87H

(Hercules, CA, USA) column at 60 °C with an aqueous mobile phase containing 5 mM H2SO4, at a flow

rate of 0.6 mL·min-1. 20 µL of the sample was injected into the column. Data acquisition and analysis

was by VP class v7.3 software from Shimadzu.

2.5 Free Solution Capillary electrophoresis (CE)

Separations were performed on an Agilent 7100 (Agilent Technologies, Waldbronn,

Germanry) with a Diode Array Detector (DAD) monitoring at 200 and 266 nm with a 10 nm

bandwidth (except where noted otherwise in the supporting information). Fused-silica capillaries (50

µm i.d., 360 µm o.d.) were obtained from Polymicro (Phoenix, AZ, USA). Capillary length was 90 cm

with an 81.5 cm effective length. The capillary was pre-treated prior to use by flushing with 1 M

NaOH followed by water then the background electrolyte (BGE) for 20 min each. The sample was

injected by applying 17 mbar of pressure for 8 s (≈10 nL in 130 mM NaOH) followed by BGE, injected

in the same manner. Between each run, the capillary was flushed with BGE for 10 min. At the end of

a series of injections, the capillary was flushed for 1 min with 1M NaOH, 10 min with water and 10

min with air. DMSO was added to each sample to make a concentration of 1 % (v/v) to mark the

electro-osmotic flow (EOF) and 1 g·L-1 of lactose was added as an internal standard. The EOF was

determined at 200 nm. Integration was performed with Origin Pro 8.5 (Northampton, MA, USA) on

electropherograms corrected for the EOF by plotting the intensity against the electrophoretic

mobility (µep) (see supporting information, Equation 5.3-2). µep for each analyte was measured at the

peak maximum.


2.6 Ethanol determination by capillary electrophoresis coupled with pressure mobilization

Ethanol was determined by photo-oxidation inhibition as previously described (29) using the

same instrument as above for CE. A 90 cm capillary (81.5 cm effective length) was pre-treated as

described above for CE. It was then flushed with BGE (65 mM NaOH and 65 mM LiOH) containing 2

g·L-1 of sucrose for 10 min before each injection. The sample was injected at 17 mbar of pressure for

8 s followed by BGE (without sucrose) in the same manner. The electric field was then applied for 12

min (BGE without sucrose in the inlet and outlet vials) followed by pressure at 50 mbar until ethanol

detection at 266 nm.

2.7 High Performance Anion Exchange Chromatography (HPAEC)

HPAEC was conducted on a Dionex (Thermo Scientific, Sunnyvale, CA, USA) IC system

consisting of a GP50 gradient pump and LC30 column oven. Pulsed amperometric detection was

conducted using an ED40 electrochemical detector in an amperometric cell mode using a gold

working electrode. 10 µL of 1:300 diluted samples (in water) were injected onto a Dionex CarboPac

PA1 column (4 x 250mm) with a PA1 guard column (4 x 50mm) at a flow rate of 1 mL·min-1.

Separation of glucose, fructose, arabinose and ethanol was under isocratic conditions of 30 mM

NaOH for 10 min followed by a gradient to 100 mM NaOH in 1 min and held for 8 min then returned

to 30 mM over 1 min for pre-equilibration of 4 min, adapted from (16). The mobile phase was

degassed with nitrogen at 3.5 bar at the headspace of eluent bottles. Data acquisition was

performed by Chromeleon V6.5, and post-processing was conducted using Origin Pro 8.5.

3 Results and Discussion:

3.1 Choice of standard mixture

Fermentation samples of plant fiber can contain a large variety of both saccharides and

sugar alcohols. CE in highly alkaline conditions has the potential to separate these analytes, but it

had never been applied to these fermentation samples so it was not known which BGE and

conditions CE best correspond for a given fermentation sample. A mixture of the common fiber

monosaccharides galactose, glucose, rhamnose, mannose, arabinose and xylose as well as the value-

added fermentation end products, arabitol and xylitol were chosen as a representative standard.

Lactose was added to the mixture as it has been used in a previous study as internal standard for

quantification and a mobility marker for identification (21).


3.2 Electrophoretic mobilities of carbohydrates in CE: Effect of the BGE

Separation of glucose, rhamnose and mannose is a known challenge in CE (24) and a

relevant one for fermentation monitoring. In order to achieve baseline separation of these sugars a

large selectivity (difference in electrophoretic velocity and thus electrophoretic mobility) is desired.

Band-broadening also plays a role, as discussed in the next paragraph together with resolution

values. Separation in the simplest BGE (to lead to the most robust separation) was examined first.

Separations were performed in this work using NaOH (25) but also LiOH and KOH (30), as well as

various mixtures (Table 5.2-1). The maximum relative standard deviation (RSD) for any

electrophoretic mobility value of a carbohydrate was 2.5 % (n=3). The electrophoretic mobility

values in Table 5.2-1 allow identification of the carbohydrates separated by CE in the relevant BGE

irrespective of the capillary length and electric field.


Table 5.2-1: Electrophoretic mobility (µep) of carbohydrates and related fermentation end products

(0.5 g·L-1 each) in different BGE (a more extensive version is given as Table 5.3-1). Conditions:

Voltage 24 kV, temperature 15 °C, current of 160 ± 6 µA. The values are an average of three

sequential injections.

Carbohydrate

Background Electrolyte (BGE)

130 mM KOH 52 mM KOH 26 mM LiOH 52 mM NaOH

130 mM NaOH 130 mM LiOH 130 mM NaOH 36 mM Na2HPO4

µep ( - 10−8 m2 V-1 s−1)

Xylitol 0.431 0.443 0.441 0.465 0.441 Arabitol 0.500 0.512 0.513 0.535 0.517 Lactose 1.58 1.55 1.54 1.53 1.42 Galactose 1.84 1.80 1.78 1.76 1.64 Glucose 1.91 1.86 1.83 1.81 1.70 Rhamnose 1.96 1.90 1.86 1.83 1.73 Mannose 2.00 1.94 1.90 1.88 1.77 Arabinose 2.06 2.00 1.96 1.94 1.82 Xylose 2.20 2.12 2.08 2.06 1.95 Time of EOF marker (min) [RSD (%)]

8.82 [3.95]

12.23 [1.47]

12.94 [1.13]

14.43 [2.61]

14.49 [0.54]

Viscosity (η) (mPa s) of the BGE at 25 °Ca

2.2 Not measured 3.7 3.5 Not measured

Time of EOF/Viscosity (mPa-1)

241 - 210 247 -

Rhamnose relative position in relation to glucose and mannoseb

1.09 1.10 1.11 0.55 0.84

Difference in µep between mannose and glucose (10−8 m2 V-1 s−1)

0.09 0.08 0.07 0.07 0.07

pHc 12.66 Not measured 12.65 12.44 12.6 a Interpolated from the correlation in (31). b Calculated by the difference in µep between rhamnose (mR) and glucose (mG) over the difference in µep between rhamnose and mannose (mM) x = ((𝑚𝑚𝑅𝑅− 𝑚𝑚𝐺𝐺)

(𝑚𝑚𝑀𝑀− 𝑚𝑚𝑅𝑅)). x = 1 when rhamnose is equal distance

between glucose and mannose, x> 1 when rhamnose is closer to mannose, x<1 when rhamnose is closer to glucose. c Experimental pH, (the theoretical pH, assuming the activity co-efficient is 1, would be 13.1).


An increase in BGE concentration from 30 to 170 mM resulted in an increase in the

electrophoretic mobilities (due to higher pH) as well as the separation selectivity (Table 5.3-2). A

concentration of 170 mM however resulted in a total poor resolution (Table 5.3-3; discussed later)

and an increased analysis time. Therefore a concentration of 130 mM was chosen for this study as

the BGE concentration as it provided adequate resolution in an appropriate time and has been used

in previous studies (25). At 130 mM, KOH provided the highest electrophoretic mobilities and the

largest selectivity between the carbohydrates (lactose – xylose 1.58 - 2.20 x 10−8 m2 V-1 s−1) as well as

the fastest EOF. 130 mM LiOH provided the smallest electrophoretic mobilities and the smallest

selectivity (1.53 – 2.06 x10−8 m2 V-1 s−1) and EOF (Table 5.2-1).

The electrophoretic mobility of the carbohydrates depends on the ratio of the charge to the

product of hydrodynamic radius and BGE viscosity (See Equation 5.3-4). Taking the differences in

viscosities into account (see Equation 5.3-4), the hydrodynamic radius of the carbohydrate in KOH

might be larger than in NaOH or LiOH (Table 5.3-5). Viscosity might be the main contributor to the

lowest EOF (see Equation 5.3-3) and higher electrophoretic mobilities of the carbohydrates in KOH.

The increase in carbohydrate size in KOH might be due to a stronger complexation of the

carbohydrates with K+ leading to a larger hydrodynamic radius than with Na+ or Li+ in the conditions

used (32, 33), ion pairing or a difference in the structure of the carbohydrate in the presence of the

cation (34) as observed with the helix conformation of gellan gums induced by K+ but not by Na+

(35). Although the carbohydrates have a larger electrophoretic mobility in KOH than in NaOH, the

sugar alcohols have a smaller electrophoretic mobility: sugar alcohols may not complex with K+. The

electrophoretic mobility of rhamnose is also affected differently by the change in counterion

compared to the electrophoretic mobility of glucose and mannose.

To achieve complete separation, one needs both a large difference in electrophoretic

mobility between mannose and glucose (i.e. a larger window of separation), and rhamnose in the

middle. The electrophoretic mobilities in several mixtures of LiOH, NaOH and KOH BGE were

compared (Table 5.2-1 and Table 5.3-1) on a contour plot (Figure 5.3-2). The optimal position of

rhamnose was achieved with 43.3 mM KOH, 43.3 mM LiOH and 43.3 mM NaOH (designated M4),

while the optimal (largest) selectivity was with 130 mM KOH. M4 is a good candidate for separation

of complex fermentation samples containing glucose, rhamnose and mannose, while KOH is a good

candidate for less complex fermentations that do not contain rhamnose (Figure 5.2-1).


Figure 5.2-1: Separation of carbohydrates and related fermentation end products (0.5 g·L-1 each) in

M4 BGE comprised of 43.3 mM KOH, 43.3 mM LiOH and 43.3 mM NaOH (A) and 130 mM KOH (B).

An illustration of the peak to valley ratio is given in C and of the orthogonal peak to valley ratio is

given in D. Conditions: Capillary length 90 cm (81.5 cm effective length), voltage 24 kV, temperature

15 °C, current of 160 ± 6 µA. Peak assignments: (1) xylitol, (2) arabitol, (3) lactose, (4) galactose, (5)

glucose, (6) rhamnose, (7) mannose, (8) arabinose, (9) xylose.

The separation time is shorter in KOH than M4 however this results in poorer sensitivity,

since the sensitivity is proportional to the residence time in the detection window, as it is linked to

the photo-oxidation reaction that allows direct UV detection (25, 26).

Resolution of carbohydrate separation by CE and choice of the BGE

The effect of the counterion on the separation of carbohydrates has been qualitatively

studied (24, 30). Colon et al. compared the separation of a carbohydrate mixture with a BGE

containing Na+, Li+ or K+ as well as a different Na+ concentration. They concluded that NaOH gave a

‘good resolution’ in a suitable time (41 min for xylose), however resolution values were not

quantified. The typical resolution equation used for chromatographic separations (Equation 5.3-6,

Figure 5.3-4) is not an appropriate tool to determine the quality of the separations in this work since


peaks are highly asymmetric (36), which is typical of CE, and cannot be described by a Gaussian

function. Resolution in CE can be predicted but only if the exact diffusion coefficient is known for

each analyte (37). Resolution (Rvp) of the separation of asymmetric peaks can be instead measured

by the valley to peak ratio (38) or an improved version proposed as the orthogonal valley to peak

ratio (Rovp) (39).

Rovp or Rvp = 100 × 𝑉𝑉s𝑃𝑃

Equation 5.2-1

In the case of Rvp, ‘Vs’ is the height of the valley (defined as the minimum between the two

apexes) between the two peaks and ‘P’ is the height of the lowest peak (Figure 5.2-1C), whereas in

the case of Rovp, ‘Vs’ is defined as the height of the valley and ‘P’ is the distance from the baseline to

the interpolated peaks height at the same time (Figure 5.2-1D); the valley is in this case determined

as the largest P-Vs distance obtained when a straight line orthogonal to the interpolation of the

peaks is moved from one maximum to the other one. The orthogonal valley to peak ratio was used

to determine the resolutions of the separations in this work (Table 5.2-2). The valley to peak ratios,

shown in Table 5.3-6 and Table 5.3-3, are less time-consuming to determine manually. For both Rvp

and Rovp, the lower the ratio, the better the resolution. Resolution of all the studied analytes in

different BGE concentrations from 30 to 170 mM LiOH were determined and 130 mM gave the best

resolution (Table 5.3-3) for the sugars while 170 mM led to slightly better resolved separations of

the sugar alcohols.


Table 5.2-2: Resolution (expressed as orthogonal valley to peak ratio expressed as 100 x Vs/P) of the mixture of carbohydrates (the lowest value is given in

bold). Separation conditions: 24 kV, 90 cm capillary (81.5 cm effective length). Mixture contains 0.5 g L-1 xylitol, arabitol, lactose, galactose, glucose,

rhamnose, mannose, arabinose and xylose. n=3. The lowest values are indicated in bold.

Xylitol-Arabitol Glucose-Rhamnose Rhamnose-Mannose Mannose-Arabinose Sum of Rovp

RSD (%)

Product of all Rovp

RSD (%)

Time of last peak (min) Rovp RSD (%) Rovp RSD (%) Rovp RSD (%) Rovp RSD (%)

130 mM NaOH

28.6 34.0 26.3 5.74 38.6 29.5 3.45 49.1 96.9 15.6 100000 66.8 27.5

130 mM LiOH

4.57 45.8 43.5 2.81 33.9 45.9 19.3 72.3 101 20.8 130000 97.1 34.5

130 mM KOH

23.2 15.6 54.1 2.33 100 0 72.1 17.5 250 5.29 905000 23.6 14.2

65 mM LiOH 65 mM NaOH (M1)

5.64 19.3 30.9 7.80 36.9 1.70 5.10 27.4 78.5 3.88 32800 34.5 33.4

43.3 mM KOH, 43.3 mM LiOH 43.3 mM NaOH (M4)

13.4 40.4 36.1 10.5 50.1 4.01 44.2 36.9 144 12.3 1070000 55.8 27.0

130 mM NaOH, 36 mM Na2HPO4

2.35 72.4 42.0 5.27 47.5 5.91 45.1 22.2 137 7.86 211000 76.1 32.6


The greatest difference in electrophoretic mobility was observed with 130 mM KOH buffer

(Table 5.2-1), although this resulted in poor resolution (Table 5.2-2), likely due to the fastest EOF

resulting in insufficient time for the analytes to separate. Rhamnose had the optimal electrophoretic

mobility value in M4 BGE, being between glucose and mannose electrophoretic mobility values, but

did not lead to the optimal resolution of these three sugars, partially due to the second fastest EOF.

For separations of complex mixtures with a single electrolyte (contain only one counter-ion; simplest

to prepare), 130 mM NaOH, provided the best resolution overall however not noticeably different

from LiOH. Separation of rhamnose-mannose and arabitol-xylitol was better achieved with 130 mM

LiOH. An even combination of the two electrolytes (designated M1) gave a better overall resolution

(19 % improvement relative to 130 mM NaOH). There was a decrease in resolution between glucose-

rhamnose in M1 in comparison to 130 mM NaOH due to the rhamnose’s electrophoretic mobility

shifting closer to glucose (see Table 5.3-1 and Figure 5.3-2). Comparison of the resolution with

different counterions in the BGE was previously conducted at the same EOF by Colon et al. (30). This

was achieved by altering the electric field while keeping EOF constant. Although altering the electric

field should not alter the electrophoretic mobility of the charged species for the same BGE, it may

affect the resolution and therefore a fair comparison cannot be made with other BGEs unless the

same electric field is used.

Resolution in CE generally increases with analysis time which can be achieved by increasing

the capillary length (without changing the electric field) or slowing down the EOF. The EOF can be

slowed by increasing the viscosity of the BGE, lowering the temperature (Figure 5.3-5), adding an

organic solvent (40) or adding a buffering agent such as Na2HPO4 to the BGE. The latter is more

difficult without changing the conductivity of the buffer which was shown previously to be an factor

influencing the separation (41). The addition of 36 mM Na2HPO4 to 130 mM NaOH slowed the EOF

by 1.5 min (Table 5.2-1) but also decreased all electrophoretic mobilities (Table 5.2-1): contrary to

previous reports (24) it did not result in greater resolution. The addition of methanol (Figure 5.3-5)

to the BGE led to loss of signal, due to inhibition of the photo-oxidation reaction that allows

detection of the carbohydrates (26, 29). Alternatively, pressure may be applied to the outlet vial (or

vacuum to the inlet vial) (42) to slow the separation (Figure 5.3-5). This improves resolution but at

the cost of analysis time (Table 5.3-7, 5.3-8 and 5.3-9). For a complex fermentation sample

containing glucose, rhamnose and mannose, a mixture of 65 mM LiOH and 65 mM NaOH is

recommended, although this results in an increase of analysis time.


Throughput of carbohydrate separation by CE and choice of the BGE

Throughput is also important in monitoring fermentation. To account for time and resolution

of the separation, the orthogonal valley to peak ratio was multiplied by the migration time at the

valley (t) (Equation 5.2-2). For a biotechnological process, this quantity might be referred as

“efficiency”; the term efficiency in CE (and separation science in general) however already has an

existing and different definition (plate count). Equation 5.2-2 accounts for both resolution and the

time taken for that resolution to be achieved: Tres is the time in min to achieve a given resolution and

the objective is to minimize Tres.

TRes = 𝑉𝑉s𝑡𝑡𝑃𝑃

Equation 5.2-2

where VS and P are defined in Equation 5.2-1 (Rovp). Separation of a carbohydrate mixture in 130 mM

NaOH, KOH and LiOH was carried out and the Tres was measured for each (Table 5.3-8). TRes values

calculated with the valley to peak ratio (instead of the orthogonal valley to peak ratio) are also

shown in Table 5.3-9.

Comparing the separation in BGE with individual compounds at concentrations of 130mM,

LiOH led to the lowest (best) TRes for the sugar alcohols and highest (worse) TRes for rhamnose and

glucose as expected from the selectivity. 130 mM NaOH as a simple BGE, led to lowest TRes for the

mono- and di-saccharide separation as well as overall (shown by the product of all the resolutions

for each analyte measured). The BGE M4 does not give a lower TRes than 130 mM NaOH (Table 5.3-8)

nor does it give a better resolution (Table 5.2-2). The M1 mixture, giving a better resolution than 130

mM NaOH, did not lead to a significantly lower TRes. The 130 mM NaOH BGE, led to the lowest TRes of

all separations except for the sugar alcohols.

The use of 130 mM NaOH as a BGE was compared to the sodium phosphate buffer by both

Rovio et al. (24) and Sarazin et al. (25). Rovio et al. (24) noted that the detection in disodium

phosphate benefited from lower baseline noise and resolution of the separation improved between

glucose, mannose, rhamnose and arabinose. In contrast Sarazin et al. (25) noted similar analytical

performance between the two BGEs in terms of separation efficiencies, corrected peak areas and

limits of detection as well as a simpler buffer preparation, but for less complex samples. In this

study, a more complex sample was analyzed and it was found that 130 mM NaOH/36 mM Na2HPO4

did not perform better than 130 mM NaOH in both resolution and TRes for the peaks measured. A

mixture of 65 mM LiOH and 65 mM NaOH is recommended for separation of the most complex


mixtures, such as the fermentation of plant fiber to sugar alcohols (Table 5.2-3). However, for fiber

fermentations where the rhamnose is negligible, 130 mM NaOH would give lower (better) TRes. For

the simplest fermentation sample such as the glucose-xylose or a single sugar, 130 mM KOH would

provide separation in less than 15 min (Table 5.2-3). It is to be noted that use of pressure to slow

down the separation lead to higher (worse) Tres.

Table 5.2-3: List of current/potential fermentation substrates and the recommended BGE to monitor

the fermentation using CE.

Substrate Fermentable carbohydrates of interest Recommended BGE

Reference for the substrate composition

sugar cane Juice glucose, fructose, sucrose

130 mM KOH (or 130 mM NaOH with 30 kV) (43)

switch grass glucose, xylose

130 mM KOH (or NaOH with 30 kV) (44)

corn stover, wheat straw, rice straw, rice hulls, cotton gin trash, Douglas fir

glucose, mannose, galactose, xylose, arabinose

130 mM NaOH (45)

Opuntia sp.

galactose, rhamnose, glucose, xylose, mannose, arabinose

65 mM LiOH + 65 mM NaOH (21)

sugar cane bagasse glucose, xylose, arabinose

130 mM NaOH (46)

A list of current and potential bioethanol substrates and the recommended BGE can be

found in Table 5.2-3. Based on results reported above, the BGE was chosen to give the optimal

analysis time with adequate resolution for the reported composition of the substrates. Various

fermentations were analyzed with the recommended BGE. A fermentation of glucose and fructose

was analyzed by CE with 130 KOH BGE, a fermentation of glucose, galactose, arabinose and xylose

was analyzed by CE with 130 NaOH BGE and a fermentation of plant fiber from Opuntia ficus-indica

was monitored with a BGE of 65 mM NaOH and 65 mM LiOH. Under some hydrolysis methods, the

plant fiber Opuntia ficus-indica may yield rhamnose (21) which can be the most challenging to

separate. This BGE may also be used to monitor the fermentation of other lignocellulosic material

that can also contain rhamnose (22). The results are shown and discussed in the next section.


3.3 CE Performance comparison to HPLC and HPAEC on simple fermentations

To examine the quantitative ability of CE, fermentation was carried out and separation

performance of CE compared to two commonly used chromatographic methods. A mixture of

glucose and fructose (10.0 g·L-1 glucose and 10.0 g·L-1 fructose) was fermented by Zymomonas

mobilis and the carbohydrates were monitored by HPLC, HPAEC and CE (Figure 5.2-2). In CE, the BGE

comprised of 130 mM KOH, as the sample contained only two carbohydrates that were easily

separated and detected.


Figure 5.2-2: Quantitative comparison of glucose (A), fructose (B) and total carbohydrate (C) in terms

of absolute concentration (bar graph) and remaining fraction (line graph) in a simple fermentation

sample by HPAEC ( and ), HPLC ( and ) and CE ( and ). Error bars indicate ± standard

deviation (n=3).

The sugar concentrations were in close agreement between CE, HPLC and HPEAC with a less

than 7 % difference from the average total detected amount (Table 5.3-14). While CE is less precise

than HPAEC and HPLC, no method is clearly more accurate than another (standards curves for each

method had a correlation co-efficient > 0.97). Retention times for the three methods

(electrophoretic mobility for CE) were determined with similar precision (Tables 5.3-11, 5.3-12 and

5.3-13). Separation of glucose and fructose in CE was achieved in less than 16 min separation time

(26 min total time, including the time needed to flush the capillary with fresh BGE in between

injections) in comparison to 12 min with HPLC (23 min total elution time, including the time needed

to ensure everything is eluted from the column) and 13 min with HPAEC (30 min total time, including

the time needed to flush the column between injections). However, electropherograms exhibited an

elevated baseline between the two carbohydrates peaks (Figure 5.3-6), which reduced the precision

of peak integration and possibly contributed to the outlying values for glucose at the higher

concentrations (0 hour sample Figure 5.2-2A and 5.2-2C). Asymmetric peaks are common in CE, but

integration is still easily achieved, e.g. in (47).


A fermentation with a larger variety of sugars, glucose, galactose, arabinose and xylose (12.0 g·L-1of

each) to ethanol and arabitol by Pichia stipitis, was monitored by HPLC and CE (Figure 5.2-3).

Figure 5.2-3: Quantitative comparison of glucose (A), arabinose (B) and arabitol (C) in a complex

fermentation sample by HPLC ( ) and CE ( ). Error bars represent ± STD (n=3).

Xylose and galactose were quantified by CE; however, as they co-eluted using the HPLC

column and conditions used in this study, no comparison was made for these two individual sugars.

The concentration values of the analytes measured by CE and HPLC were in close agreement,

however arabinose was marginally higher using HPLC and arabitol was marginally higher using CE.

The comparison for quantitative analysis shows that the detection by photo-oxidation in CE,

although not fully understood (26), was not affected by this sample matrix. For fiber samples, the CE

method detected a larger amount of several carbohydrates compared to HPLC (21), while this was

not the case for the other fiber samples tested by HPAEC (22) or for the fermentation samples in this

work. The more complex matrix in fiber samples might have led to the loss of some carbohydrates in

the HPLC column, while this was not observed in fermentation samples.


3.4 Determination of carbohydrates and ethanol during fermentation of lignocellulosic plant fiber by

CE.

The CE method was used to analyze the fermentation of carbohydrates from hydrolyzed

plant fiber to the end products ethanol and arabitol. The hydrolyzed plant fiber of Opuntia ficus-

indica has a complex mixture of carbohydrates (21) and, arising from the mucilage, uronic acids (48)

as well as incompletely hydrolyzed oligo- and polysaccharides. Based on a previous study, the matrix

was expected to contain trace amounts of hydroxymethylfurfural (HMF) and furfural as by-products

arising from the acid pre-treatment (28), products from the enzyme solution (e.g. stabilizers) and

trace amounts of barium sulfate from neutralization (max 3.1 mg/L (49)). A BGE of 65 mM LiOH and

65 mM NaOH was used for the optimal separation of carbohydrates (see Table 5.2-3). Ethanol was

determined via photo-oxidation inhibition as detailed in one of our recent papers (29).

Figure 5.2-4: Fermentation of hydrolyzed plant fiber to ethanol. Samples taken at 0 hours (A), 6

hours (B) and 24 hours (C). Peak assignments: (1) lactose (internal standard), (2) galactose, (3)

glucose, (4) mannose, (5) fructose, (6) arabinose, (7) xylose, (8) arabitol, (9) unknown (for migration

plot see Figure 5.3-8). Ethanol peak in sequential injection given as inverted peak for 0 h ( ), 6 h (

) and 24 h ( ).


The analysis of the hydrolyzed plant fiber revealed 4 minor peaks (S/N ≥ 10; unnumbered) as

well as the most predominant peaks of glucose, galactose, mannose, arabinose and xylose which

were identified by their electrophoretic mobility. Fructose was identified by an electrophoretic

mobility higher than mannose and lower than arabinose as observed in a previous study (28). Over

the course of the fermentation all peaks decreased to undetectable levels, with the exception of

arabinose which was only in minor amounts (S/N = 10) after 24 hours, indicating that all analytes

were utilized by the organism. Arabitol was detected (see Table 5.2-4). It was observed, after 24

hours of fermentation when arabinose began to be utilized by the organism. Other peaks were

identified with electrophoretic mobilities close to that of arabitol.

Table 5.2-4: Precision of mobility measured in standard and fiber fermentation samples.

Standards Sample Electrophoretic Mobilitya

(- 10-8 m2V-1s-1)

SD (- 10-8 m2V-1s-1)

RSD (%) n =

Electrophoretic Mobilitya

(- 10-8 m2V-1s-1)

SD (- 10-8 m2V-1s-1)

RSD (%) n=

Arabitol 0.632 0.00468 0.74 25 0.620 0.00823 1.33 5 Galactose 1.72 0.00906 0.53 25 1.72 0.00295 0.17 10 Glucose 1.77 0.0111 0.63 25 1.77 0.00103 0.06 5 Rhamnose 1.79 0.0136 0.76 25 N.D - - - Mannose 1.82 0.0140 0.77 25 1.81 0.0129 0.71 10 Arabinose 1.89 0.0113 0.60 25 1.89 0.00872 0.46 15 Xylose 2.00 0.0108 0.54 25 2.00 0.00584 0.29 10 a electrophoretic mobility calculated using lactose internal standard as mobility marker (µep = -1.52 x 10-8 m2V-1s-1) and not from the EOF marker as in Table 5.2-1 and 5.3-1 (Equation 5.3-7).

The precision of the electrophoretic mobility for each analyte was excellent in both the

standards as well as the fiber fermentation samples. The electrophoretic mobility of arabitol in the

sample has the highest variability as it is the furthest from the mobility marker. If peaks were

observed close to arabitol then a double mobility correction would need to be used (with sucrose for

example).The high precision of the electrophoretic mobility allows for accurate identification of the

carbohydrates of interest. The complex matrix of hydrolyzed plant fiber in fermentation media did

not hinder the identification of the analytes by their electrophoretic mobility. This illustrates the

robustness of the CE method.


Figure 5.2-5: Quantification of carbohydrates, arabitol and ethanol during ethanol fermentation of

plant fiber. Samples were analyzed (n=5) at 0 h ( ), 6 h ( ) and 24 h ( ).

The fermentation followed the expected trend with glucose and mannose being utilized

within the first 6 h and the pentoses being utilized last (no significant decrease of arabinose and

xylose in the first 6 h). Determination of ethanol is essential in fermentation monitoring. The inability

to determine ethanol at all was a significant disadvantage of HPLC with ligand exchange and CE in

highly alkaline electrolyte. HPLC on a cation exchange resin is able to determine ethanol as well as

some carbohydrates, however not all fiber sugars are resolved (21). HPAEC can also determine

ethanol in standards with the column used in this study, however, in fermentation samples, it co-

elutes with other fermentation media components that are as weakly charged (Figure 5.3-9). Very

recently, we showed that ethanol can be detected by the interference of the sugars’ photo-oxidation

(29) and ethanol is observed by indirect detection in a BGE containing sucrose during pressure

mobilization (Figure 5.2-4). While both sugars and ethanol are separated within one run, the

presence of sucrose results in a high level of noise for the sugar detection: ethanol cannot yet be

determined in the same injections as the CE of carbohydrates (29). This is a limited issue in terms of


sample amount, as only ≈10nL is injected in both cases, but this results in a longer total separation

time as 2 injections need to be performed sequentially. The same capillary is used in both

separations. Applying this new method to fermentation monitoring, ethanol was quantified with a 6

% and 10 % RSD for the 6 and 24 hour sample respectively. Although this is not as precise as

headspace GC (RSD 2 % (50)), the accuracy of the CE coupled to pressure mobilization to determine

ethanol has been positively assessed in our previous paper (29). CE has the advantage of monitoring

carbohydrates and other end products such as arabitol without derivatization or without time-

consuming or costly sample preparation. The separation time was 33-38 min (43-48 min total time)

for determination of carbohydrates for the most complex fermentation samples and 21 min (31 min

total elution time) for determination of ethanol in the following injection, so 74-79 min total time.

The simultaneous determination of ethanol and carbohydrates in this complex mixture with a single

instrument and capillary could not be accomplished by any other method in the literature. The

ethanol yield was 105 % of the theoretical maximum after 6 hours as calculated from the quantified

carbohydrates, likely because the carbohydrates below quantifiable levels also contributed to the

ethanol production. The last sample showed a decrease in ethanol (ethanol yield of 59 % of the

theoretical maximum) when arabitol was being produced from arabinose. A similar observation has

been made during the production of ethanol and xylitol by yeasts (51).

A summary of some of the advantages and drawbacks in CE, HPLC with a cation exchange resin and

HPAEC are given in Table 5.2-5.


Table 5.2-5: Advantages and drawbacks in CE, HPLC with a cation exchange resin and HPAEC for

determination of carbohydrates

Parameter CE HPLC (cation exchange resin) HPAEC

Robustnessa ++ - ++ Total Time (Glucose Fructose sample) 26 min 23 min 30 min

Set-up time (Column pre-equilibrium time /capillary flush time) (min)

35 min 45-60 mind 30 min

Determination of ethanol and fiber sugarsf with the same capillary/column

yes no no

LODb (Glucose)

1.8 mg·L-1 (26) 3.7 mg·L-1 (28)c (3.3 mg·L-1 rhamnose (28))c

70 mg·L-1 (52) 0.090 mg·L-1 (20)

Pre-filter required for separation no yes yes

Dilution required 1:5 – 1:20 None 1:50 – 1:300 Injection volume 10 – 20 nL 10 – 20 µL 10 – 20 µL Mobile phase or BGE volume per run ≈ 0.5 mL ≈ 13.8 mL ≈ 30 mL

Set-up coste AU$7.2 (21) AU$3461 (21) AU$2100 a defined as “a method that can be applied to analytes in a wide variety of matrices” (53) bLimit of detection (LOD) calculated as a Signal-to-noise = 3 cLOD calculated by (28) using analytical curve parameter evaluation d Heated column e cost of setting up the system before performing the separation. Inclusive of purchasing columns/capillaries as for Dec 2013 estimated as in (21). Not inclusive of purchasing instrument. f in lignocellulosic fermentation sample


4 Conclusions:

Free solution capillary electrophoresis (CE) is a good candidate for routine analysis of

carbohydrates, sugar alcohols and also ethanol in fermentation samples. The composition of the BGE

can be adjusted to the complexity of the carbohydrate mixture to improve separation and/or

throughput. Adjusting the composition of the BGE in CE is equivalent to using a different column in

HPLC and this confers CE a high flexibility at a very affordable cost. Although KOH provided the best

selectivity, the low viscosity of the BGE and the resulting size to charge ratio of the carbohydrates

did not lead to the highest resolution of complex mixtures. 130 mM NaOH resulted in the best

resolution amongst single-salt BGEs while a mixture of 65 mM LiOH and 65 mM NaOH increased the

resolution but increased analysis time. Quantification of carbohydrates in a simple fermentation

with CE shows values in close agreement with HPAEC and HPLC using a cation exchange column.

Quantification of a more complex fermentation by CE in comparison to HPLC also shows values in

close agreement. CE has the advantages of requiring no sample preparation (other than dilution)

and set-up costs lower than that of HPLC, a significant advantage over HPLC and HPAEC. CE has the

ability to monitor carbohydrates including arabitol and xylitol in a fermentation of plant fiber

without the need for sample preparation other than dilution. In a subsequent injection of the same

sample in the same capillary, ethanol can be determined by coupling the CE separation to pressure

mobilization with indirect detection. The ability to monitor ethanol as well as carbohydrates on the

same instrument and capillary to provide a complete picture of the fermentation sample is a major

advantage over other methods currently used. Another advantage of free solution CE, especially

given the recent developments in coupling CE systems to bioreactors (54), is that it can be used

without filtration in most situations (35), including online fermentation monitoring. Sensitivity may

be increased through the use of photo-initiators (26) if needed.

5 Acknowledgments

The authors wish to acknowledge Dr Marion Gaborieau (UWS) for discussions, Dr Glenn

Stone (UWS) for discussions regarding resolution, Prof. Peter Rogers (University of New South

Wales) for providing the Zymomonas mobilis strain, Prof. Wieland Meyer (Westmead Clinical School,

University of Sydney) for providing the Pichia stipitis strain, Dr Greg Dicinoski (UTas) and Dr Sara

Sandron (UTas) for setting up the IC system with PAD. Support from the Australian Research Council

is gratefully acknowledged: EFH is recipient of an ARC Future Fellowship (FT0990521).


References

1. Lin, Y. and Tanaka, S. (2006) Ethanol fermentation from biomass resources: current state and prospects. Appl. Microbiol. Biotechnol., 69, 627-642.

2. Playne, M. J. (1985) Determination of ethanol, volatile fatty acids, lactic and succinic acids in fermentation liquids by gas chromatography. J. Sci. Food Agric., 36, 638-644.

3. Sweeley, C. C., Bentley, R., Makita, M. and Wells, W. W. (1963) Gas-Liquid Chromatography of Trimethylsilyl Derivatives of Sugars and Related Substances. J. Am. Chem. Soc., 85, 2497-2507.

4. Crowell, E. P. and Burnett, B. B. (1967) Determination of the carbohydrate composition of wood pulps by gas chromatography of the alditol acetates. Anal. Chem., 39, 121-124.

5. Bonn, G. and Bobleter, O. (1984) HPLC-analyses of plant biomass hydrolysis and fermentation solutions. Chromatographia, 18, 445-448.

6. Bell, R. G. and Newman, K. L. (1993) Carbohydrate analysis of fermentation broth by high-performance liquid chromatography utilizing solid-phase extraction. J. Chromatogr. A, 632, 87-90.

7. Buttler, T., Lidén, H., Jönsson, J. A., Gorton, L., Marko-Varga, G. and Jeppsson, H. (1996) Evaluation of detection and sample clean-up techniques for on-and off-line fermentation monitoring systems. Anal. Chim. Acta, 324, 103-113.

8. Fu, N., Peiris, P., Markham, J. and Bavor, J. (2009) A novel co-culture process with Zymomonas mobilis and Pichia stipitis for efficient ethanol production on glucose/xylose mixtures. Enzyme Microb. Technol., 45, 210-217.

9. Ramos, L. P., Breuil, C. and Saddler, J. N. (1992) Comparison of steam pretreatment of eucalyptus, aspen, and spruce wood chips and their enzymatic hydrolysis. Appl. Biochem. Biotechnol., 34-35, 37-48.

10. Irick, T. J., West, K., Brownell, H. H., Schwald, W. and Saddler, J. N. (1988) Comparison of colorimetric and HPLC techniques for quantitating the carbohydrate components of steam-treated wood. Appl. Biochem. Biotechnol., 17, 137-149.

11. Li, X. and Kim, T. H. (2012) Bioconversion of corn stover derived pentose and hexose to ethanol using cascade simultaneous saccharification and fermentation (CSSF). Bioprocess Biosyst Eng, 35, 99-104.

12. da Cruz, S. H., Dien, B. S., Nichols, N. N., Saha, B. C. and Cotta, M. A. (2012) Hydrothermal pretreatment of sugarcane bagasse using response surface methodology improves digestibility and ethanol production by SSF. J. Ind. Microbiol. Biotechnol., 39, 439-447.

13. Pecina, R., Bonn, G., Burtscher, E. and Bobleter, O. (1984) High-performance liquid chromatographic elution behaviour of alcohols, aldehydes, ketones, organic acids and carbohydrates on a strong cation-exchange stationary phase. J. Chromatogr. A, 287, 245-258.

14. Scarlata, C. J. and Hyman, D. A. (2010) Development and validation of a fast high pressure liquid chromatography method for the analysis of lignocellulosic biomass hydrolysis and fermentation products. J. Chromatogr. A, 1217, 2082-2087.

15. Alwael, H., Connolly, D. and Paull, B. (2011) Liquid chromatographic profiling of monosaccharide concentrations in complex cell-culture media and fermentation broths. Anal. Methods, 3, 62-69.

16. Herber, W. K. and Robinett, R. S. R. (1994) Determination of carbon sources in fermentation media using high-performance anion-exchange liquid chromatography and pulsed amperometric detection. J. Chromatogr. A, 676, 287-295.

17. Robinett, R. S. R. and Herber, W. K. (1994) Analysis of substrates and metabolites in fermentation broth by ion chromatography. J. Chromatogr. A, 671, 315-322.


18. Kosugi, A., Kondo, A., Ueda, M., Murata, Y., Vaithanomsat, P., Thanapase, W., Arai, T. and Mori, Y. (2009) Production of ethanol from cassava pulp via fermentation with a surface-engineered yeast strain displaying glucoamylase. Renew energ, 34, 1354-1358.

19. Martın, C., Galbe, M., Wahlbom, C. F., Hahn-Hägerdal, B. and Jönsson, L. J. (2002) Ethanol production from enzymatic hydrolysates of sugarcane bagasse using recombinant xylose-utilising Saccharomyces cerevisiae. Enzyme Microb. Technol., 31, 274-282.

20. Hanko, V. P. and Rohrer, J. S. (2000) Determination of carbohydrates, sugar alcohols, and glycols in cell cultures and fermentation broths using high-performance anion-exchange chromatography with pulsed amperometric detection. Anal. Biochem., 283, 192-199.

21. Oliver, J. D., Gaborieau, M., Hilder, E. F. and Castignolles, P. (2013) Simple and robust determination of monosaccharides in plant fibers in complex mixtures by capillary electrophoresis and high performance liquid chromatography. J. Chromatogr. A, 1291, 179-186.

22. Rovio, S., Simolin, H., Koljonen, K. and Siren, H. (2008) Determination of monosaccharide composition in plant fiber materials by capillary zone electrophoresis. J. Chromatogr. A, 1185, 139-144.

23. Klampfl, C. W., Himmelsbach, M. and Buchberger, W. (2011), in Capillary Electrophoresis of Carbohydrates, (Volpi, N., ed.), Humana Press, pp. 1-21.

24. Rovio, S., Yli-Kauhaluoma, J. and Siren, H. (2007) Determination of neutral carbohydrates by CZE with direct UV detection. Electrophoresis, 28, 3129-3135.

25. Sarazin, C., Delaunay, N., Costanza, C., Eudes, V., Mallet, J. M. and Gareil, P. (2011) New avenue for mid-UV-range detection of underivatized carbohydrates and amino acids in capillary electrophoresis. Anal. Chem., 83, 7381-7387.

26. Oliver, J. D., Rosser, A. A., Fellows, C. M., Guillaneuf, Y., Clement, J.-L., Gaborieau, M. and Castignolles, P. (2014) Understanding and improving direct UV detection of monosaccharides and disaccharides in free solution capillary electrophoresis. Anal. Chim. Acta, 809, 183-193.

27. Metsämuuronen, S., Lyytikäinen, K., Backfolk, K. and Sirén, H. (2013) Determination of xylo-oligosaccharides in enzymatically hydrolysed pulp by liquid chromatography and capillary electrophoresis. Cellulose, 20, 1121-1133.

28. Vaher, M., Helmja, K., Käsper, A., Kurašin, M., Väljamäe, P., Kudrjašova, M., Koel, M. and Kaljurand, M. (2012) Capillary electrophoretic monitoring of hydrothermal pre-treatment and enzymatic hydrolysis of willow: Comparison with HPLC and NMR. Catal. Today, 196, 34-41.

29. Oliver, J. D., Gaborieau, M. and Castignolles, P. (2014) Ethanol determination using pressure mobilization and free solution capillary electrophoresis by photo-oxidation assisted ultraviolet detection. J. Chromatogr. A, 1348, 150-157.

30. Colon, L. A., Dadoo, R. and Zare, R. N. (1993) Determination of carbohydrates by capillary zone electrophoresis with amperometric detection at a copper microelectrode. Anal. Chem., 65, 476-481.

31. Sipos, P. M., Hefter, G. and May, P. M. (2000) Viscosities and Densities of Highly Concentrated Aqueous MOH Solutions (M+= Na+, K+, Li+, Cs+, (CH3)4N+) at 25.0 °C. J. Chem. Eng. Data, 45, 613-617.

32. Angyal, S. J. (1972) Complexes of Carbohydrates with Metal Cations .1. Determination of Extent of Complexing by NMR-Spectroscopy. Aust. J. Chem., 25, 1957-1966.

33. Andrasko, J. and Forsen, S. (1973) Pulsed NMR studies on Na + binding to simple carbohydrates. Biochem. Biophys. Res. Commun., 52, 233-239.

34. Franks, F., Hall, J. R., Irish, D. E. and Norris, K. (1986) The effect of cations on the anomeric equilibrium of d-glucose in aqueous solutions — a raman-spectral study. Carbohydr. Res., 157, 53-64.


35. Taylor, D. L., Ferris, C. J., Maniego, A. R., Castignolles, P., in het Panhuis, M. and Gaborieau, M. (2012) Characterization of Gellan Gum by Capillary Electrophoresis. Aust. J. Chem., 65, 1156.

36. Song, D. and Wang, J. (2003) Modified resolution factor for asymmetrical peaks in chromatographic separation. J. Pharm. Biomed. Anal., 32, 1105-1112.

37. Weinberger, R. (1993) Practical capillary electrophoresis. ed. Academic Press San Diego, CA. 38. Christophe, A. B. (1971) Valley to peak ratio as a measure for the separation of two

chromatographic peaks. Chromatographia, 4, 455-458. 39. López-Grıo, S. J., Vivó-Truyols, G., Torres-Lapasió, J. R. and Garcıa-Alvarez-Coque, M. C.

(2001) Resolution assessment and performance of several organic modifiers in hybrid micellar liquid chromatography. Anal. Chim. Acta, 433, 187-198.

40. Schwer, C. and Kenndler, E. (1991) Electrophoresis in fused-silica capillaries: the influence of organic solvents on the electroosmotic velocity and the .zeta. potential. Anal. Chem., 63, 1801-1807.

41. Sarazin, C., Delaunay, N., Costanza, C., Eudes, V., Gareil, P. and Vial, J. (2012) On the use of response surface strategy to elucidate the electrophoretic migration of carbohydrates and optimize their separation. J. Sep. Sci., 35, 1351-1358.

42. Leclercq, L. and Cottet, H. (2012) Fast characterization of polyelectrolyte complexes by inline coupling of capillary electrophoresis to Taylor dispersion analysis. Anal. Chem., 84, 1740-1743.

43. Grote, W. and Rogers, P. L. (1985) Ethanol production from sucrose-based raw materials using immobilized cells of Zymomonas mobilis. Biomass, 8, 169-184.

44. Alizadeh, H., Teymouri, F., Gilbert, T. I. and Dale, B. E. (2005) Pretreatment of switchgrass by ammonia fiber explosion (AFEX). Appl. Biochem. Biotechnol., 124, 1133-1141.

45. Lee, J. (1997) Biological conversion of lignocellulosic biomass to ethanol. J. Biotechnol., 56, 1-24.

46. Jeon, Y. J., Xun, Z. and Rogers, P. L. (2010) Comparative evaluations of cellulosic raw materials for second generation bioethanol production. Lett. Appl. Microbiol., 51, 518-524.

47. Ibrahim, A., Ohshima, H., Allison, S. A. and Cottet, H. (2012) Determination of effective charge of small ions, polyelectrolytes and nanoparticles by capillary electrophoresis. J. Chromatogr. A, 1247, 154-164.

48. Trachtenberg, S. and Mayer, A. M. (1981) Composition and properties of Opuntia ficus-indica mucilage. Phytochemistry, 20, 2665-2668.

49. Lide, D. R. (2004) CRC Handbook of Chemistry and Physics, 85th Edition. ed. Taylor & Francis. 50. Li, H., Chai, X.-S., Deng, Y., Zhan, H. and Fu, S. (2009) Rapid determination of ethanol in

fermentation liquor by full evaporation headspace gas chromatography. J. Chromatogr. A, 1216, 169-172.

51. Jeon, Y. J., Shin, H. S. and Rogers, P. L. (2011) Xylitol production from a mutant strain of Candida tropicalis. Lett. Appl. Microbiol., 53, 106-113.

52. Chinnici, F., Spinabelli, U., Riponi, C. and Amati, A. (2005) Optimization of the determination of organic acids and sugars in fruit juices by ion-exclusion liquid chromatography. J Food Comp Anal, 18, 121-130.

53. Harvey, D. (2000) Modern Analytical Chemistry. ed. McGraw-Hill, Boston. 54. Turkia, H., Holmström, S., Paasikallio, T., Sirén, H., Penttilä, M. and Pitkänen, J.-P. (2013)

Online Capillary Electrophoresis for Monitoring Carboxylic Acid Production by Yeast during Bioreactor Cultivations. Anal. Chem., 85, 9705-9712.



Supporting information for

Simple and robust monitoring of ethanol fermentations by capillary electrophoresis

James Oliver,1,2 Adam T. Sutton,1 Naama Karu,3 Michael Phillips,2 Julie Markham,2 Paul Peiris,2Emily

F. Hilder,3 Patrice Castignolles1

1University of Western Sydney, Australian Centre for Research On Separation Sciences (ACROSS), School of Science and Health, Parramatta Campus, Locked Bag 1797, Penrith NSW 2751, Australia

2University of Western Sydney, School of Science and Health, Hawkesbury Campus, Locked Bag 1797, Penrith NSW 2751, Australia

3Australian Centre for Research on Separation Science (ACROSS), School of Chemistry, University of Tasmania, Hobart TAS 7001, Australia, [email protected]

This supporting information contains supplementary equations and electrophoretic

mobilities values as well as electropherograms from the free solution Capillary Electrophoresis (CE)

experiments. It also contains supplementary data on the quantification of sugars by CE as well as

High Performance Anion Exchange Chromatography (HPAEC) and High Performance Liquid

Chromatography (HPLC) on a cation exchange resin.

Free solution capillary electrophoresis: Separation of carbohydrates

Figure 5.3-1: Mechanism of separation by free solution capillary electrophoresis.


The electrophoretic velocity is weaker than the EOF and so the sugars migrate toward the

cathode. The difference between the EOF and the apparent velocity of the sugars corresponds to the

electrophoretic velocity. The electrophoretic velocity is directly proportional to the electric field

strength, and the proportionality constant between these variables is the electrophoretic mobility

(which is proportional to the charge-to-size ratio).

Equation 6.3-1: relationship between apparent velocity (vapp), electroosmotic velocity (veof) and

electrophoretic velocity (vep)

vapp = veof + vep

The electrophoretic mobility ‘µep’ was determined the following equation (1):

Equation 6.3-2: Formula used to calculate the experimental electrophoretic mobility values

𝜇𝜇ep =𝑙𝑙 ∙ 𝐿𝐿𝑉𝑉

�1𝑡𝑡m

−1𝑡𝑡eo

�

Where ‘l’ is the length to the detection window (effective length), ‘L’ is the total length of the

capillary, ‘V’ is the applied voltage, ‘tm’ is the migration time of the carbohydrate, ‘teo’ is the

migration time of the electro-osmotic flow (EOF) marker (2).


Table 5.3-1: Comparison of various Background Electrolytes (BGE) and their effect on electrophoretic mobility and electro-osmotic flow (EOF).

Electrophoretic mobility was calculated using Equation 5.3-1.

Carb

ohyd

rate

Background Electrolyte (BGE) 13

0mM

KO

H

65 m

M K

OH

65

mM

NaO

H (M

2)

52 m

M K

OH

52 m

M N

aOH

26 m

M L

iOH

(M5)

43

.33

mM

KO

H 43

.33

mM

NaO

H 43

.33

mM

LiO

H (M

4)

52 m

M K

OH

26 m

M N

aOH

52 m

M L

iOH

(M6)

130

mM

NaO

H

26 m

M K

OH

52 m

M N

aOH

52 m

M L

iOH

(M7)

65 m

M K

OH

65 m

M L

iOH

(M3)

65 m

M L

iOH

65

mM

NaO

H (M

1)

130

mM

LiO

H

130

mM

NaO

H 36

m

M N

a2HP

O4+

(3)

98 m

M N

aOH

120

mM

NaC

l++

(4)

Electrophoretic mobility (-10−8 m2 V-1 s−1) Arabitol 0.431 0.435 0.443 0.455 0.45 0.441 0.449 0.448 0.451 0.465 0.441 0.364 Xylitol 0.5 0.504 0.512 0.523 0.518 0.513 0.517 0.516 0.532 0.535 0.517 0.423

Lactose 1.58 1.56 1.55 1.55 1.55 1.54 1.55 1.54 1.52 1.53 1.42 1.44 Galactose 1.84 1.81 1.8 1.8 1.8 1.78 1.79 1.78 1.75 1.76 1.64 1.68 Glucose 1.91 1.87 1.86 1.86 1.86 1.83 1.85 1.83 1.8 1.81 1.7 1.73

Rhamnose 1.96 1.92 1.9 1.9 1.89 1.86 1.89 1.87 1.83 1.83 1.73 1.78 Mannose 2 1.96 1.94 1.94 1.94 1.9 1.93 1.91 1.87 1.88 1.77 1.81 Arabinose 2.06 2.02 2 2 2 1.96 1.99 1.97 1.93 1.94 1.82 1.87

Xylose 2.2 2.15 2.12 2.13 2.12 2.08 2.11 2.09 2.04 2.06 1.95 2.01 EOF (min) 8.82 10.94 12.23 12.7 12.86 12.94 13.15 13.23 14.26 14.43 14.49 15.08


Table 5.3-2: Electrophoretic mobility of carbohydrates and related fermentation end products (0.5

g·L-1 each) in LiOH with varying concentration. Conditions: Voltage 24 kV, temperature 15 °C.

The electrophoretic mobility values and EOF are 9-16 % higher in Table 5.3-2 than the values

in Table 5.2-1. The standard solution had 1 g·L-1 of each analyte, as opposed to 0.5 g·L-1 in Table 5.2-

1, which would alter the EOF and mobility due to a change in sample viscosity. Injections were

performed on a different instrument (HP-3D, also from Agilent Technologies, USA) with a different

capillary of the same length with the signal was monitored with a diode array detector (DAD) at 270

nm. However the resolution values (Table 5.3-3) were very similar.

The contour plot on Figure 5.3-2 shows the relative position of rhamnose in-between

glucose and mannose as well as the absolute difference in electrophoretic mobility between glucose

and mannose. To achieve complete separation, one needs both a large difference in electrophoretic

mobility between mannose and glucose (i.e. a larger window of separation), and rhamnose in the

middle. The difference in electrophoretic mobility (selectivity; defined differently by other authors

(7)) of glucose and mannose was calculated by 1(𝑚𝑚𝑀𝑀− 𝑚𝑚𝐺𝐺)

were mM and mG are the electrophoretic

mobility of mannose and glucose respectively. The relative position of rhamnose is expressed

as (𝑚𝑚𝑅𝑅− 𝑚𝑚𝐺𝐺)(𝑚𝑚𝑀𝑀− 𝑚𝑚𝑅𝑅)

where a value of 1 corresponds to rhamnose at equal distance between glucose and

Carbohydrate

Background Electrolyte (BGE) 30 mM 60 mM 90 mM 130 mM 170 mM

µep (-10−8 m2 V-1 s−1)

Xylitol 0.104 0.255 0.301 0.424 0.508 Arabitol 0.149 0.311 0.350 0.484 0.598 Glucose 1.07 1.31 1.40 1.53 1.71

Rhamnose 1.11 1.34 1.44 1.54 1.71 Mannose 1.18 1.41 1.49 1.57 1.74 Arabinose 1.24 1.44 1.52 1.65 1.84

Time of EOF marker (min) 11.72 13.58 16.16 17.85 18.40

Viscosity (η/mPa s) of

the BGE at 25 °C *

1.65 2.27 2.84 3.51 4.19

Time of EOF marker/

Viscosity (mPa-

1)

426 359 341 305 263


mannose, a value above 1 corresponds to rhamnose closer to mannose than glucose and a value

below 1 corresponds to rhamnose closer to glucose than mannose.

Figure 5.3-2: Contour plot of the varying KOH and LiOH proportion in 130 mM total alkaline

concentration (when relevant the third component is NaOH). Contour shows the distribution of

inverse difference in electrophoretic mobility of glucose and mannose where the lowest value is

shown by the darkest region. The labels (stars) display the relative position of rhamnose to glucose

and mannose defined as (𝑚𝑚𝑅𝑅− 𝑚𝑚𝐺𝐺)(𝑚𝑚𝑀𝑀− 𝑚𝑚𝑅𝑅)

.

In LiOH, rhamnose migrates at a similar velocity to glucose, while it migrates faster than

glucose in both KOH and NaOH as it is detected closer to mannose. The lower pH of LiOH BGE than

that of NaOH and KOH BGEs might cause a stronger decrease in charge for rhamnose than for

glucose and mannose at these high pHs.


Table 5.3-3: Resolution (expressed as Rvp = 100 x Vs/P) of a mixture of carbohydrates in varying

concentrations of LiOH (the best values are given in bold). Separation conditions: 24 kV, 90 cm

capillary (81.5 cm effective length). Mixture contains 0.5 g L-1 xylitol, arabitol, lactose, galactose,

glucose, rhamnose, mannose, arabinose and xylose.

BGE Xylitol-Arabitol

Glucose-Rhamnose

Rhamnose-Mannose

Mannose-Arabinose

Total V/P Ratio

Rvp 30 mM LiOH 81.2 83.9 31.4 38.9 235 60 mM LiOH 68.1 57.8 100 60.9 287 90 mM LiOH 40.7 51.2 32.6 61.5 186 130 mM LiOH 2.06 68.9 9.44 5.18 85.5 170 mM LiOH 1.51 100 7.94 0.00 109.5


Figure 5.3-3: Comparison of separation of 130 mM NaOH (bottom), 130 mM LiOH (middle) and BGE

M5 (top) on time based (A) and mobility based (B) electropherograms. Peak assignment is given in

Figure 5.2-1.


The EOF depends on the ratio of the zeta potential (ζ) of the capillary and the viscosity of the

BGE (Equation 5.3-3). Different counterions in the BGE lead to different viscosity, conductivity and

pH. The viscosity of LiOH, KOH and NaOH was interpolated from (5) (Equation 5.3-5) and given in

Table 5.2-1. 130 mM KOH is less viscous than LiOH or NaOH. The zeta potential of the capillary

surface in these BGE is directly proportional to the viscosity of the BGE multiplied by the migration

time of the EOF marker (given in Table 5.2-1) so the slower EOF in LiOH, compared to NaOH, can be

attributed to its higher BGE viscosity.

Equation 5.3-3: Expression of electro-osmotic flow (6)

𝑉𝑉eo = 𝜀𝜀 × ζ𝜼𝜼

𝐸𝐸

Where ‘Veo’ is the velocity of the electro-osmotic flow, ‘ε’ is the di-electric constant, ‘ζ’ is the zeta

potential on the capillary ‘E’ is the electric field strength and ‘η’ is the viscosity of the BGE.

The electrophoretic mobility of the carbohydrates depends on the ratio of the charge to the

product of hydrodynamic radius and BGE viscosity: an increase in charge of the carbohydrate or a

decrease in the carbohydrates hydrodynamic radius results in an increase in electrophoretic mobility

for a given viscosity. The ratio of the charge of the carbohydrate to its hydrodynamic radius can be

estimated by multiplying the electrophoretic mobility by 6 x π xη (Equation 5.3-4). The ratio of

charge to hydrodynamic radius is similar in NaOH and LiOH, but lower in KOH (see Table 5.3-5). The

difference in pH does not explain the difference in ratio (the pH of NaOH and KOH were similar): the

difference of charges of the carbohydrates might not play a role in the separation.

Equation 5.3-4: Stokes law governing electrophoretic mobility (6)

𝑚𝑚ep �m2

V ∙ s�=

𝑣𝑣 (m/s)𝐸𝐸 (V/m)

=𝑞𝑞

6π𝜂𝜂𝜂𝜂

Where, ‘v’ is the ionic velocity, ‘q’ is the effective charge, and ‘r’ is the hydrodynamic radius.


Equation 5.3-5: Calculation of viscosity of KOH, NaOH and LiOH. (5)

Ƞ = Ƞ0 + ac + bc2 + dc3 + ec4 + fc5

Where η0 is the viscosity of the pure solvent, a, b, d, e, and f are defined in Table 5.3-4 by (5). Values

are as published.

Table 5.3-4: Values for a, b, d, e, and f for exploration viscosity by Equation 5.3-5

LiOH NaOH KOH 102 ‘a’ (mPa s mol-1 L) 21.893 20.275 8.6933 102 ‘b’ (mPa s mol-2 L2) 8.711 2.2961 1.289 104 ‘d’ (mPa s mol-3 L3) 415.92 6.1979 1.9984 105 ‘e’ (mPa s mol-4 L4) 1535.9 89.526 8.2422 105 ‘f’ (mPa s mol-5 L5) 165.66 2.9925 1.1563

Table 5.3-5: Calculation of the ratio of ionic charge to hydrodynamic radius calculated by Equation

5.3-4

130 mM KOH 130 mM NaOH 130 mM LiOH

Electrophoretic Mobility ( - 10−8 m2 V-1 s−1)

Glucose 1.91 1.83 1.81

Rhamnose 1.96 1.86 1.83

BGE Viscosity (mPa s) 2.15 3.71 3.51

Ionic charge/hydrodynamic radius (10−6 m2mPa V-1)

Glucose 0.774 1.28 1.20

Rhamnose 0.793 1.30 1.21


Equation 5.3-6: Calculation for resolution of symmetric peaks

Rs=2(𝑅𝑅𝑅𝑅b−𝑅𝑅𝑅𝑅a)𝑊𝑊a+ 𝑊𝑊b

Where ‘RTa’ and ‘RTb’ are the migration time of the molecules ‘a’ and ‘b’, and ‘Wa’ and ‘Wb’ are the

peak widths at the baseline for the molecules ‘a’ and ‘b’. (Figure 5.3-4)

Figure 5.3-4: Graphical determination of peak widths and retention times taken as an example and

extracted from Figure 5.2-1A glucose and galactose peaks.


Table 5.3-6: Resolution as calculated by the valley-to-peak ratio of the mixture of carbohydrates (the lowest value is given in bold). Separation conditions:

24 kV, 90 cm capillary (81.5 cm effective length). Mixture contains 0.5 g L-1 xylitol, arabitol, lactose, galactose, glucose, rhamnose, mannose, arabinose and

xylose. n=3. The best values are given in bold. These resolutions values are obtained on the same electropherograms as for Table 5.2-2.

Xylitol-Arabitol

Glucose-Rhamnose

Rhamnose-Mannose

Mannose-Arabinose Sum

of Rvp

RSD (%)

Product of Rvp

RSD (%)

Time of last peak (min) Rvp RSD

(%) Rvp RSD (%) Rvp RSD

(%) Rvp RSD (%)

130 mM NaOH 16.7 37 22.8 16 24.4 11 3.33 15 67.1 11 30900 45 27.5

130 mM LiOH 4.67 50 59.4 32 15.2 38 5.03 33 84.2 23 21200 78 34.5

130 mM KOH 22.4 8.1 74.8 6.7 100 0.0 46.1 8.8 243 2.7 7720000 13 14.2

65 mM LiOH 65 mM NaOH (M1) 5.57 20 34.7 14 14.1 28 3.49 14 57.9 11 9510 40 33.4

43.3 mM KOH, 43.3 mM LiOH 43.3 mM NaOH (M4)

10.5 5.9 52.4 3.9 39.9 2.1 7.23 10 110 2.2 159000 13 27.0


2.05 90 24.2 6.7 12.0 19 4.25 29 42.6 8.4 2530 97 32.6


Figure 5.3-5: Separation in M5 in standard conditions (A), with temperature at 13 °C (B), with 10

mbar back pressure (C), with 1 % methanol (D) on time based electropherogram.

Lower temperature or back pressure slows the separation modifying the resolution (Table

5.3-6) and the Tres (Table 5.3-7).

Table 5.3-7: Resolution in BGE: 52 mM KOH 52 mM NaOH 26 mM LiOH (M5), capillary length 112.0

cm (103.5 cm effective length), 29.8 kV voltage. The lowest valley to peak ratio is given in bold.

Arabitol-Xylitol Glucose-

Rhamnose Rhamnose-Mannose

Mannose-Arabinose Total

Rvp Rvp Ratio RSD Rvp Ratio RSD Rvp Ratio RSD Rvp Ratio RSD M5 normal 12.5 30 40.7 9.2 36.0 5.8 5.79 11 95.0 13 °C 13.7 14 49.5 8.5 61.6 3.1 7.17 10 132 10 mbar back pressure

16.1 29 38.6 15 28.0 54 5.09 76 87.8


Table 5.3-8: Time to achieve a given resolution, Tres, based on Rovp, for a mixture of carbohydrates.

Separation conditions: 24 kV, 90 cm capillary (81.5 cm effective length). Mixture contains 0.5 g·L-1

xylitol, arabitol, lactose, galactose, glucose, rhamnose, mannose, arabinose and xylose. Lowest Tres is

in bold.

BGE

Xylitol-Arabitol Glucose-Rhamnose

Rhamnose-Mannose

Mannose-Arabinose Sum of

TRes

(min)

RSD (%)

Product of TRes

(min)

RSD (%) TRes

(min) RSD (%)

TRes

(min) RSD (%)

TRes

(min) RSD (%)

TRes

(min) RSD (%)

130 mM NaOH

4.22 35 6.40 5.8 9.53 28 0.882 50 21.0 15 227 67

130 mM LiOH 0.762 44 12.9 1.2 10.2 43 5.94 71 29.8 20. 594 94 130 mM KOH 2.22 11 7.19 7.7 13.4 5.7 9.84 22 32.6 7.5 2100 27 65 mM LiOH 65 mM NaOH (M1)

0.931 18 8.98 11 10.9 3.8 1.54 23 22.3 5.1 141 32

43.3 mM KOH, 43.3 mM LiOH, 43.3 mM NaOH (M4)

1.94 39 8.71 12 12.3 3.9 11.2 38 34.1 13 232 57


0.396 72 11.9 5.8 13.7 6.1 13.4 21 39.3 7.7 861 76


Table 5.3-9: Tres based on Rvp (Table 5.3-8 for the equivalent values based on Rovp) for a mixture of

carbohydrates. Separation conditions: 24 kV, 90 cm capillary (81.5 cm effective length). Mixture

contains 0.5 g·L-1 xylitol, arabitol, lactose, galactose, glucose, rhamnose, mannose, arabinose and

xylose. Lowest Tres is in bold.

BGE

Xylitol-Arabitol

Glucose-Rhamnose

Rhamnose-Mannose

Mannose-Arabinose Total

TRes (min)

130 mM NaOH 2.46 5.54 6.02 0.847 14.9

130 mM LiOH 0.761 17.5 4.56 1.57 24.4

130 mM KOH 2.14 9.92 13.4 6.24 31.7

65 mM LiOH 65 mM NaOH (M1) 0.919 10.0 4.14 1.06 16.1

43.3 mM KOH, 43.3 mM LiOH, 43.3 mM NaOH (M4)

1.51 12.5 9.67 1.80 25.5


0.345 6.86 3.47 1.26 11.9


Table 5.3-10: TRes in BGE: 52 mM KOH 52 mM NaOH 26 mM LiOH (M5), capillary length 112 cm

(103.5 cm effective length), 29.8 kV electric field. The lowest values are given in bold.

Arabitol-Xylitol Glucose-

Rhamnose Rhamnose-Mannose

Mannose-Arabinose

Total TRes

(min) TRes (min) RSD TRes (min) RSD TRes (min) RSD TRes (min) RSD M5 normal 1.68 31 8.64 7.8 7.76 7.0 1.28 12 19.4 13C 1.90 14 11.1 8.1 14.0 2.5 1.66 10 28.6 10 mbar back pressure

2.78 28 12.9 14 9.53 54 1.80 75 27.0

Figure 5.3-6: Separation of glucose (a) and fructose (b) (equal concentration) in 130 mM KOH with a

fixed concentration of 500 mg·L-1 lactose (c) internal standard. Glucose and fructose at (A) 1000

mg·L-1 (B) 500 mg·L-1(C) 250 mg·L-1(D) 125 mg·L-1 (E) 62.5 mg·L-1 each.


Quantification with CE, high performance anion exchange chromatography (HPAEC) and high

performance liquid chromatography (HPLC) on a cation exchange resin

Supplementary information about CE, HPAEC and HPLC performances for the Zymomonas

mobilis fermentation (Figure 5.2-2) are given in the Tables 5.3-11 to 5.3-14 below.

Table 5.3-11: Repeatability of determined concentration and electrophoretic mobility from CE

injections of 6 fermentation samples (n=3). BDL= below detectable limit. Separation in 90 cm

capillary (81.5 cm effective length) at 24 kV with an electrolyte 130 mM KOH operating a 15 °C.

Sample Sugar Concentration (g L-1) RSD (%)

Electrophoretic mobility (x -

10−8 m2 V-1 s−1) RSD (%)

Media Glucose 10.9 14.8 1.888 0.11 Fructose 9.24 3.14 2.016 0.12

0 h Glucose 9.56 11.3 1.884 0.08 Fructose 10.0 3.70 2.014 0.07




8 h Glucose BDL BDL BDL BDL Fructose 0.54 0.563 1.987 0.22

Table 5.3-12: Repeatability of HPAEC injections of 2 fermentation samples in terms of determined

concentration and retention time (n=5). BDL= below detectable limit. PA1 column with a 30mM

NaOH mobile phase at 1 mL·min-1 operating at room temp.

Sample Sugar Concentration (g L-1) RSD (%) Retention

Time (min) RSD (%)

Media Blank

Glucose 9.44 1.5 6.05 0.62

Fructose 10.1 3.1 7.08 0.73

8 h Glucose BLD BLD 6.09 0.73

Fructose 0.63 2.6 7.14 1.0


Table 5.3-13: Repeatability of HPLC injections of 5 fermentation samples in terms of determined

concentration and retention time (n=5). HPX-87H hydrogen form cation exchange resin with a

mobile phase 5 mM H2SO4 at 0.60 mL·min-1 operating at 60 °C.

Sample Sugar Concentration (g L-1) RSD (%)

Retention Time (min)

RSD (%)

0 h Glucose 9.49 0.12 9.47 0.31 Fructose 9.15 0.17 10.28 0.32 Ethanol 0.20 30.7 21.2 0.29




8 h Glucose nd nd 9.43 0.23 Fructose 0.37 16.8 10.2 0.07 Ethanol 8.41 0.36 21.3 0.12

Table 5.3-14: Analysis of results displayed in Figure 5.2-2.

Analytical Method Carbohydrate

Concentration (g·L-1) Deviation from total average (%)

Sample Sum of detected amount

Media Blank*

0* 2* 4* 6* 8*

HPAEC Glucose 9.44 8.83 6.52 0.44 0.04 0.00 25.3 6.9 Fructose 10.0 9.78 9.66 7.16 1.98 0.63 39.3 3.7

HPLC Glucose 9.53 9.49 7.48 1.39 0.04 0.00 27.9 2.9 Fructose 9.27 9.15 9.46 7.29 2.07 0.37 37.6 0.62

CE Glucose 10.9 9.56 6.24 0.93 0.57 0.00 28.2 3.9 Fructose 9.25 10.0 7.72 7.02 2.13 0.54 36.7 3.1

Average Glucose

28.2

Fructose 36.7 * Fermentation time (h)


Supplementary information of CE performances for the Pichia stipitis fermentation of plant fiber

(Figure 5.2-4 and 5.2-5 and Table 5.2-4) are given in below.

Analytes were identified by electrophoretic mobility with lactose as an internal standard by

the Equation 5.3-7, which is a modification of Equation 5.3-2.

Equation 5.3-7: Formula used to calculate the experimental electrophoretic mobility values with an

internal standard.

𝜇𝜇ep = �𝑙𝑙 ∙ 𝐿𝐿𝑉𝑉

�1𝑡𝑡m

−1𝑡𝑡is��+ 𝑚𝑚𝑖𝑖𝑖𝑖

Where ‘tis’ is the migration of the internal standard (lactose in this study) and ‘µis’ is the

electrophoretic mobility of the internal standard (which was -1.52 x 10-8 m2V-1s-1 in the BGE used,

based on Table 5.3-1).

Table 5.3-15: Calibration curves for quantification of carbohydrates in fiber fermentation samples by

CE.

Analyte Equation R2 value

Galactose y = -6.04 × 10-7 x2 + 1.83 × 10-3 x - 5.51 × 10-3 0.999

Glucose y = -5.15 × 10-7 x2 + 1.93 × 10-3 x - 1.35 × 10-2 0.999

Mannose y = -6.65 × 10-7 x2 + 1.54 × 10-3 x - 1.31 × 10-2 0.999

Arabinose y = -4.65 × 10-7 x2 + 1.05 × 10-3 x - 2.88 × 10-3 0.999

Xylose y = -3.15 × 10-7 x2 + 6.12 × 10-4 x - 8.20 × 10-3 0.999

Arabitol y = -2.67 × 10-6 x2 + 4.99 × 10-3 x + 1.27 × 10-1 0.997


Figure 5.3-7: Calibration in CE for quantification of carbohydrates. (n=3)

Table 5.3-16: Concentration and RSD of fiber samples (n=5) corresponding to Figure 5.2-5.

Analyte

0 h 6 h 24 h

Concentration (g·L-1)

RSD (%)


RSD (%)


RSD (%)

Arabitol N.D - N.D - 0.464 2.7

Galactose 1.21 8.0 0.879 4.4 N.D -

Glucose 3.08 8.1 N.D - N.D -

Mannose 0.458 8.5 N.D - N.D -

Arabinose 1.56 7.6 1.51 3.6 0.431 5.6

Xylose 0.851 11 0.749 8.7 N.D -


Figure 5.3-8: Fermentation of hydrolyzed plant fiber to ethanol. Samples taken at time 0 h (A), 6 h

(B) and 24 h (C). Peak assignments: (1) lactose (internal standard), (2) galactose, (3) glucose, (4)

mannose, (5) fructose, (6) arabinose, (7) xylose, (8) arabitol, (9) unknown.


Figure 5.3-9: Separation of ethanol and carbohydrates in a 25 mg·L-1 standard (black) and

fermentation sample (red) with HPAEC-PAD. Peak assignment: 1. Void peak, 2. Ethanol, 3. Elevated

baseline indicating other analytes, 4. Media components, 5. Arabinose, 6. Glucose, 7. Fructose. PA1

column with a 30mM NaOH mobile phase at 1 mL•min-1 operating at room temp.

References

1. Susumu, H. (1996) Separation of neutral carbohydrates by capillary electrophoresis. J. Chromatogr. A, 720, 337-351.

2. Castignolles, P., Gaborieau, M., Hilder, E. F., Sprong, E., Ferguson, C. J. and Gilbert, R. G. (2006) High-Resolution Separation of Oligo(acrylic acid) by Capillary Zone Electrophoresis. Macromol. Rapid Commun., 27, 42-46.

3. Rovio, S., Yli-Kauhaluoma, J. and Siren, H. (2007) Determination of neutral carbohydrates by CZE with direct UV detection. Electrophoresis, 28, 3129-3135.

4. Sarazin, C., Delaunay, N., Costanza, C., Eudes, V. and Gareil, P. (2012) Application of a new capillary electrophoretic method for the determination of carbohydrates in forensic, pharmaceutical, and beverage samples. Talanta, 99, 202-206.

5. Sipos, P. M., Hefter, G. and May, P. M. (2000) Viscosities and Densities of Highly Concentrated Aqueous MOH Solutions (M+= Na+, K+, Li+, Cs+, (CH3)4N+) at 25.0 °C. J. Chem. Eng. Data, 45, 613-617.

6. Weinberger, R. (1993) Practical capillary electrophoresis. ed. Academic Press San Diego, CA:.


7. Dolník, V. (1996) Selectivity, differential mobility and resolution as parameters to optimize capillary electrophoretic separation. J. Chromatogr. A, 744, 115-121.


6. Conclusion and future directions

6

6.1 Conclusion

Determination of carbohydrates in lignocellulosic fiber samples and in their fermentation

samples is challenging. After hydrolysis, the sample contains a variety of monosaccharides,

oligosaccharides, proteins and amino acids, lignin and its monomers as well as the acids and

enzymes used for hydrolysis. During the fermentation, the ethanologen utilizes carbohydrates and,

depending on the microorganism, produces ethanol, sugar alcohols and/or other products. The

method for analysis of the lignocellulosic fiber samples and fermentation broth needs to be robust

and able to resolve all the fiber sugars. The most advantageous method would be one that could

determine carbohydrates as well as ethanol.

HPLC is a favored technique in the field for its ease of use and precision, both suited to

routine analysis. In the first publication it was shown that the well-established HPLC methods for

carbohydrate determination are limited by either inadequate resolution or poor robustness (Table

6.1-1). Popular lead ligand exchange LC is commonly utilized as it separates the targeted

monosaccharides, with the exception of rhamnose and galactose. However the sample treatment

required is tedious and time consuming and the column is not cost effective (Table 6.1-1). The cation

exchange resin, popular for being the most robust column, has the drawback of inadequate

separation (Table 6.1-1). The characterization of acid treated fiber samples were compared with CE

and this column. HPLC had consistently lower quantification values compared to CE. When CE with

direct UV detection was utilized, no sample pre-treatment was needed and the separation of all

targeted carbohydrates were at least partially resolved (Table 6.1-1).


Table 6.1-1: Comparison of HPLC, HPAEC and CE on various fermentation samples.

Method Free Solution CE HPLC-cation exchange resin

HPLC-ligand LC on lead based resin

HPAEC (MicrobeadTM pellicular resin)

Fermentation of glucose + fructose samples Separate Yes Yes Yes Yes

Total time3

26 min (extra 30 min for ethanol) 23 min 25 min 23 min

Quantify ethanol Yes1 Yes No No2

Pre-treatment requires

Dilution Filtration Sample clean-up + filtration + dilution Filtration + dilution

Fermentation of glucose, galactose, xylose and arabinose Separate Yes No Yes Yes

Total time3




Dilution Filtration Sample clean-up + Filtration + dilution Filtration + dilution

Fermentation of lignocellulosic material

Separate Yes No No Yes Total time3




Dilution Filtration Sample clean-up + filtration + dilution Filtration + dilution

1. Separate injections required 2. In samples with a complex matrix (e.g fermentation samples) 3. Including any capillary/column flush time and time for all molecules from the sample to

elute from the column

The separation of carbohydrate and sugar alcohols was investigated. The BGE had an

influence on mobility, resolution and resolution/min. Based on the different BGE investigated,

recommendations were made for the analysis of different fermentation broths. The method was

quantitatively compared to HPAEC and HPLC with values in close agreement.

In previous studies the repeatability of quantification by CE was a concern [113, 131]

however it was improved in this work by the use of an appropriate internal standard. Although CE

has superior selectivity compared to HPLC, the detection mechanism was still not well understood. It


was determined in this work that the detection was not due to enediolate formation, as previously

theorized [113], but by a UV initiated photo-oxidation reaction [112]. A free radical process was

proposed and end products investigated by 1H and 13C NMR spectroscopies. Based on the work of

Gilbert et al. (1982), and calculations on predicted UV spectra, the UV-absorbing intermediates are

theorized to be semidiones that were previously studied by ESR [143]. The endproducts were found

to be carboxylates and no aldehydes or ketones were detected even though they are theorized to be

present in the reaction [112]. This indicates that oxygen plays a role in the reaction pathway. The

new understanding of the mechanism allowed the sensitivity to be increased by 42 % with the use of

a photo-initiator. Although hydrogen peroxide is more readily available, its effect as a photo-initiator

was not repeatable. The photo-initiator Irgacure® 2959 was found to increase the sensitivity in a

repeatable fashion. The LOD for glucose was improved from previously published values of this

method [112].

One advantage that RI detection and PAD, used with HPLC and HPAEC respectively, still had

over the direct UV detection of CE was that the most important product of the fermentation,

ethanol, can be detected. A method that can determine ethanol in the same mode as the

carbohydrate method was developed. The direct UV detection was modified for the determination

of non-UV absorbing molecules. It was discovered that ethanol as well as methanol, iso-propanol,

tert-butanol and triethylamine were detected by interference of the photo-oxidation reaction.

Determination of ethanol in real samples of vodka was carried out with simple pressure mobilization

experiments with excellent recovery. For the analysis of spiked fermentation media, ethanol could

be detected but only partial separation of the carbohydrates, from each other, was achieved.

Separation was achieved by CE followed by pressure mobilization. The BGE contains sucrose when

ethanol is passing the detection window and free of sucrose when the carbohydrates are passing the

detection window. CE was unable to be used for the entirety of the characterization as the presence

of sucrose in the BGE when in the electric field led to an unstable and highly noisy baseline. The

method was successfully tested on a lignocellulosic fermentation and carbohydrates as well as

ethanol were determined by sequential injections in CE and then CE coupled to pressure

mobilization.

Overall this PhD work has provided a simple and robust method for the separation and

detection of carbohydrates, sugar alcohols and ethanol in complex matrixes. Free solution CE is an

effective analysis technique for carbohydrates in biotechnology samples. The understanding gained

of the detection has opened up some new avenues of research for the application of this detection.


6.2 Future directions

This separation with direct UV detection has a wide range of potential applications. This

detection is still relatively new in both understanding and application, but its ease of use combined

with the robustness of CE gives it potential yet to be fully realized.

6.2.1 Improving sensitivity and throughput

The use of photo-initiators is a promising avenue to enable trace detection with direct UV

detection. More photo-initiators need to be investigated, possibly one which has a UV absorbance

different from the measured wavelength and with decomposition products that have a high

reactivity with carbohydrates. One of the drawbacks of the method is the separation time which

ranges from 15 min for simple mixtures to 35 min for complex mixtures. Speeding up the separation

has the limitation of decreasing the detection. Some success in speeding up the separation has been

achieved with the use of coated capillaries [112, 116]. With a combined use of both coated

capillaries and photo-initiators, the throughput of the separation could be increased while

maintaining good sensitivity.

6.2.2 Fermentation monitoring

For the continued development of this method, further work should look at the

determination of carbohydrates and ethanol in a single separation. This was attempted in the third

publication however the separation of carbohydrates from each other was limited. Given the

developments made in this PhD project in determination of both ethanol and carbohydrates, as well

as recent development of a coupling between bioreactors and CE with a flow-through vial (Figure

6.2-1) [146], online monitoring of ethanol fermentations and feedback control could be achieved.

This could aid in the analysis and control of more complex fermentations such as those involving co-

cultures [72] or GMOs.


Figure 6.2-1: Flow through vial developed for Beckman CE adapted from [146].

The fermentation of lignocellulosic material by GMOs, such as those mentioned previously

(see 1.2.3.6) would also be interesting. E. coli K011 and Z. mobilis AX101 have been modified for

increased substrate range and a study on their metabolism can be carried out in more detail. CE with

direct UV detection could also be developed for the analysis of carboxylates, such as galacturonic

acid. Galacturonic acid is found in pectin (see 1.2.1.3), in lignocellulosic fiber and can be fermented

to ethanol. Carboxylates have a high pKa meaning they will migrate extremely slowly in a counter-

EOF separation. A combination of electric field and pressure mobilization may speed up migration

and allow carbohydrate and acid analysis in a single injection.

6.2.3 Application to polysaccharide characterization

CE has been useful for the separation and characterization of the composition of gellan

gums [118] and chitosan [119] polysaccharides. The detection of oligosaccharides and

polysaccharides is poor in CE due to lack of strong chromophores. Photo-oxidation detection may aid

in the detection of such analytes.


6.2.4 Application to nutrition and health

This method is already being applied in our research group for the analysis of carbohydrates

in breakfast cereals [147]. It can determine mono- and disaccharides in cereals simply extracted

overnight in water without the need for any sample pre-treatment. Another possible application

may be measuring anti-oxidants which are important preservatives and have been investigated for

potential health benefits. Anti-oxidants could be detected by interference with the photo-oxidation

reaction, similar to ethanol. Since it was shown in the second publication that oxygen plays a role,

the anti-oxidants should produce more interference than ethanol, thus a greater sensitivity.

6.2.5 Conclusion of future work

This body of work has the potential to impact many other areas of scientific research. The

direct UV detection can still be improved further for trace analysis or applied to a variety of complex

samples.

Bioethanol remains a field of inquiry for the foreseeable future. Optimization of biofuel

production requires a simple and robust method for analysis. The contribution of the work

contained in this thesis will hopefully facilitate future research into the use of non-food based plants

for bioethanol.


7. References

[1] D.M. Mousdale, Biofuels : biotechnology, chemistry, and sustainable development, CRC Press, Boca Raton, Fl., 2008.

[2] Rob Bradley, Jonathan Pershing, Lee Schipper, Kevin A. Baumert, Navroz K. Dubash, Jose Roberto Moreira, Stanford Mwakasonda, Wei-Shiuen Ng, Luiz Augusto Horta Nogueira, Virginia Parente, H. Winkler, World Resources Institute, Washington, 2005.

[3] M.E.D. De Oliveira, B.E. Vaughan, E.J. Rykiel, Jr., Bioscience, 55 (2005) 593(10). [4] D. Lewis, The Sydney Morning Herald (http://www.smh.com.au/news/business/rising-grain-

prices-put-a-dent-in-fuel-revolution/2007/10/19/1192301043371.html) (last accessed 19/05/2014)), Sydney, Australia, October 20, 2007.

[5] D.R. Keshwani, J.J. Cheng, Bioresour. Technol., 100 (2009) 1515. [6] M.D. Ferrari, E. Neirotti, C. Albornoz, E. Saucedo, Biotechnol. Bioeng., 40 (1992) 753. [7] H.K. Sreenath, T.W. Jeffries, Bioresour. Technol., 72 (2000) 253. [8] C.A. Cardona, J.A. Quintero, I.C. Paz, Bioresour. Technol., 101 (2010) 4754. [9] J. Saucedo-Luna, A.J. Castro-Montoya, J.L. Rico, Campos-Garia, Revista Mexicana de

Ingeniería Química, 9 (2010) 91. [10] E. Varga, H.B. Klinke, K. Réczey, A.B. Thomsen, Biotechnol. Bioeng., 88 (2004) 567. [11] I.U.P.A.C, Analytical Chemistry Division (http://www.iupac.org/nc/home/projects/project-

db/project-details.html?tx_wfqbe_pi1[project_nr]=2002-058-1-500) (last accessed 02/07/2014), 2002.

[12] D. Harvey, Modern Analytical Chemistry, McGraw-Hill, Boston, 2000. [13] K. Kamide, Cellulose and cellulose derivatives molecular characterization and its

applications, Elsevier, Amsterdam, 2005. [14] A. Gandini, M.N. Belgacem, Chapter 1 - The State of the Art, In: Monomers, polymers and

composites from renewable resources, Elsevier, Amsterdam, 2008. [15] J. Lehmann, Carbohydrates : structure and biology, Thieme, Stuttgart ; New York, 1998. [16] Y. Kataoka, T. Kondo, Macromolecules, 29 (1996) 6356. [17] N. Kosaric, F. Vardar-Sukan, Potential Source of Energy and Chemical Products, In:The

biotechnology of ethanol classical and future applications, Wiley-VCH, New York, 2001. [18] W. Boerjan, J. Ralph, M. Baucher, Annu. Rev. Plant Biol., 54 (2003) 519. [19] P. Albersheim, A. Darvill, K. Roberts, R. Sederoff, A. Staehelin, Plant Cell Walls: From

Chemistry to Biology, 2011. [20] M. Micic, I. Benitez, M. Ruano, M. Mavers, M. Jeremic, K. Radotic, V. Moy, R.M. Leblanc,

Chem. Phys. Lett., 347 (2001) 41. [21] A.M. Smith, C. G., L. Dolan, N. Harberd, J. Jones, C. Martin, R. Sablowski, A. Amey, Plant

Biology, Garland Science, 2010. [22] C. Luo, D.L. Brink, H.W. Blanch, Biomass Bioenergy, 22 (2002) 125. [23] J. Puls, J. Schuseil, Chemistry of Hemicellulose: Relationship Between Hemicellulose

Structure and Enzymes Required for Hydrolysis Portland, London, 1993. [24] I. Spiridon, V.I. Popa, in: B. Mohamed Naceur, G. Alessandro (Eds.), Monomers, Polymers

and Composites from Renewable Resources, Elsevier, Amsterdam, 2008, p. 289. [25] H.-W. Heldt, F. Heldt, Plant Biochemistry (Third Edition), Academic Press, Burlington, 2005,

p. 1. [26] P.M. Dey, M.D. Brownleader, J.B. Harborne, in: P.M. Dey, J.B. Harborne (Eds.), Plant

Biochemistry, Academic Press, London, 1997, p. 1. [27] H.J. Zhong, M.A.K. Williams, R.D. Keenan, D.M. Goodall, C. Rolin, Carbohydr. Polym., 32

(1997) 27.


[28] H.-J. Zhong, M.A.K. Williams, D.M. Goodall, M.E. Hansen, Carbohydr. Res., 308 (1998) 1. [29] S. Vidal, T. Doco, P. Williams, P. Pellerin, W.S. York, M.A. O’Neill, J. Glushka, A.G. Darvill, P.

Albersheim, Carbohydr. Res., 326 (2000) 277. [30] M. Galbe, G. Zacchi, in: L. Olsson (Ed.), Biofuels (Advances in Biochemical

Engineering/Biotechnology), Springer, 2007, p. 41. [31] P. Kumar, D.M. Barrett, M.J. Delwiche, P. Stroeve, Industrial & Engineering Chemistry

Research, 48 (2009) 3713. [32] L. Tillman, A. Abaseed, Y. Lee, R. Torget, Appl. Biochem. Biotechnol., 20-21 (1989) 107. [33] Y. Sun, J. Cheng, Bioresour. Technol., 83 (2002) 1. [34] Y. Zheng, H.M. Lin, G.T. Tsao, Biotechnol. Prog., 14 (1998) 890. [35] B.E. Dale, M.J. Moreira, 1982, p. 31. [36] P.C. Badger, in: J. Janick, A. Whipkey (Eds.), American Society for Horticultural Science (ASHS)

Press, Alexandria VA USA, 2002. [37] M.J. Taherzadeh, K. Karimi, University College of Borås. School of Engineering, 2007. [38] G. Reed, T. Nagodawithana, Enzymes in food processing, Academic Press, New York, 1993. [39] M.J. Taherzadeh, K. Karimi, Bioresources, 2 (2007) 472. [40] S. Yaman, Energy Convers. Manage., 45 (2004) 651. [41] C. Ratledge, B. Kristiansen, Basic biotechnology, Cambridge University Press, Cambridge, UK ;

New York, 2006. [42] A.A. Maris, D. Abbott, E. Bellissimi, J. Brink, M. Kuyper, M.H. Luttik, H.W. Wisselink, W.A.

Scheffers, J. Dijken, J. Pronk, Antonie Van Leeuwenhoek, 90 (2006) 391. [43] E.A. Dawes, D.W. Ribbons, P.J. Large, Biochem. J., 98 (1966) 795. [44] C. Wyman, Handbook on bioethanol : production and utilization, Taylor & Francis,

Washington, DC, 1996. [45] M. Wayman, S.R. Parekh, Biotechnology of biomass conversion, Open University Press,

Milton Keynes, U.K., 1990. [46] A.N. Glazer, H. Nikaido, Microbial biotechnology : fundamentals of applied microbiology,

W.H. Freeman, New York, 1995. [47] J.N. Nigam, J. Biotechnol., 87 (2001) 17. [48] N. Fu, P. Peiris, J. Markham, J. Bavor, Enzyme Microb. Technol., 45 (2009) 210. [49] P.M. Bruinenberg, P.H.M. Bot, J.P. Dijken, W.A. Scheffers, Appl. Microbiol. Biotechnol., 18

(1983) 287. [50] T.H. Lee, M.Y. Kim, Y.W. Ryu, J.H. Seo, J. Microbiol. Biotechnol., 11 (2001) 384. [51] K. Skoog, B. Hahn-Hägerdal, Appl. Environ. Microbiol., 56 (1990) 3389. [52] M. Taniguchi, T. Tohma, T. Itaya, M. Fujii, J. Ferment. Bioeng., 83 (1997) 364. [53] P. Rogers, Y. Jeon, K. Lee, H. Lawford, Biofuels, 2007, p. 263. [54] B. Hahn-Hägerdal, M. Galbe, M.F. Gorwa-Grauslund, G. Lidén, G. Zacchi, Trends Biotechnol.,

24 (2006) 549. [55] I.M. Banat, P. Nigam, D. Singh, R. Marchant, A.P. McHale, World J. Microbiol. Biotechnol., 14

(1998) 809. [56] L.K. Bowles, W.L. Ellefson, Appl. Environ. Microbiol., 50 (1985) 1165. [57] J. Van Dijken, Eduard Van Den Bosch, John J. Hermans, Lennart Rodrigues De Miranda, W.

Alexander Scheffers, Yeast, 2 (1986) 123. [58] Saigal D, Indian J. Microbiol., 33 (1993) 159. [59] P.L. Rogers, K. Lee, D.E. Tribe, Biotechnol. Lett., 1 (1979) 165. [60] A.B. Blakeney, P.J. Harris, R.J. Henry, B.A. Stone, Carbohydr. Res., 113 (1983) 291. [61] G.A. Sprenger, FEMS Microbiol. Lett., 145 (1996) 301. [62] P.t. Zikmanis, R. Krúče, L. Auzin¸a, Arch. Microbiol., 167 (1997) 167. [63] A.A. Dimarco, A.H. Romano, Appl. Environ. Microbiol., 49 (1985) 151. [64] S.D. Feldmann, H. Sahm, G.A. Sprenger, Appl. Microbiol. Biotechnol., 38 (1992) 354. [65] T. Karsch, U. Stahl, K. Esser, Appl. Microbiol. Biotechnol., 18 (1983) 387.


[66] A.V. Tran, R.P. Chambers, Biotechnol. Lett., 7 (1985) 841. [67] I.C. Roberto, L.S. Lacis, M.F.S. Barbosa, I.M. de Mancilha, Process Biochem., 26 (1991) 15. [68] M. Zhang, C. Eddy, K. Deanda, M. Finkelstein, S. Picataggio, Science, 267 (1995) 240. [69] P. Kötter, M. Ciriacy, Appl. Microbiol. Biotechnol., 38 (1993) 776. [70] M. Sonderegger, M. Jeppsson, C. Larsson, M.-F. Gorwa-Grauslund, E. Boles, L. Olsson, I.

Spencer-Martins, B. Hahn-Hägerdal, U. Sauer, Biotechnol. Bioeng., 87 (2004) 90. [71] L.O. Ingram, T. Conway, D.P. Clark, G.W. Sewell, J.F. Preston, Appl. Environ. Microbiol., 53

(1987) 2420. [72] N. Fu, P. Peiris, World J. Microbiol. Biotechnol., 24 (2008) 1091. [73] G.L. Miller, Anal. Chem., 31 (1959) 426. [74] D.J. Bell, D.J. Manners, A. Palmer, J Chem Soc (1952) 3760. [75] D.B. Rivers, S.J. Gracheck, L.C. Woodford, G.H. Emert, Biotechnol. Bioeng., 26 (1984) 800. [76] D.J. Fox, P.P. Gray, N.W. Dunn, W.L. Marsden, Journal of Chemical Technology and

Biotechnology. Biotechnology, 34 (1984) 171. [77] R. Katoch, Analytical Techniques in Biochemistry and Molecular Biology, Springer New York,

2011, p. 67. [78] M. DuBois, K. Gilles, J.K. Hamilton, P.A. Rebers, F. Smith, Nature, 168 (1951) 167. [79] M. DuBois, K.A. Gilles, J.K. Hamilton, P.A. Rebers, F. Smith, Anal. Chem., 28 (1956) 350. [80] P. Rao, T.N. Pattabiraman, Anal. Biochem., 181 (1989) 18. [81] T. Masuko, A. Minami, N. Iwasaki, T. Majima, S.-I. Nishimura, Y.C. Lee, Anal. Biochem., 339

(2005) 69. [82] Z. Dische, Methods Biochem. Anal., John Wiley & Sons, Inc., 1955, p. 313. [83] R.J. Bruessau, Macromolecular Symposia, 110 (1996) 15. [84] G. Georges, L.G. Claude, Quantitative gas chromatography, Elsevier, 1988. [85] E.P. Crowell, B.B. Burnett, Anal. Chem., 39 (1967) 121. [86] J.S. Sawardeker, J.H. Sloneker, A. Jeanes, Anal. Chem., 37 (1965) 1602. [87] J.P. Zanetta, W.C. Breckenridge, G. Vincendon, J. Chromatogr. A, 69 (1972) 291. [88] D.R. Knapp, Handbook of Analytical Derivatization Reactions. , Wiley, New York, 1979. [89] A.I. Ruiz-Matute, O. Hernández-Hernández, S. Rodríguez-Sánchez, M.L. Sanz, I. Martínez-

Castro, J. Chromatogr. B, 879 (2011) 1226. [90] C.J. Biermann, G.D. McGinnis, Analysis of carbohydrates by GLC and MS, CRC Press, 1989. [91] H. Alwael, D. Connolly, B. Paull, Anal. Methods, 3 (2011) 62. [92] A.A. Ben-bassat, E. Grushka, J. Liq. Chromatogr., 14 (1991) 1051. [93] M. D'Amboise, D. Noēl, T. Hanai, Carbohydr. Res., 79 (1980) 1. [94] R.G. Bell, K.L. Newman, J. Chromatogr. A, 632 (1993) 87. [95] G. Bonn, J. Chromatogr., 350 (1985) 381. [96] G. Bonn, J. Chromatogr., 322 (1985) 411. [97] H. Caruel, L. Rigal, A. Gaset, J. Chromatogr., 558 (1991) 89. [98] M.J. Villanueva-Suárez, A. Redondo-Cuenca, M.D. Rodríguez-Sevilla, M. de las Heras

Martínez, J. Agric. Food Chem., 51 (2003) 5950. [99] R. Pecina, G. Bonn, E. Burtscher, O. Bobleter, J. Chromatogr. A, 287 (1984) 245. [100] Bio-Rad, Bulletin 1928, USA. [101] G. Zeppa, L. Conterno, V. Gerbi, J. Agric. Food Chem., 49 (2001) 2722. [102] Dionex, Technical Note 20 (2004). [103] Y. Zhang, Y.C. Lee, in: R. Ziad El (Ed.), Journal of Chromatography Library, Elsevier, 2002, p.

207. [104] W.R. LaCourse, D.C. Johnson, Anal. Chem., 65 (1993) 50. [105] M.R. Hardy, R.R. Townsend, Y.C. Lee, Anal. Biochem., 170 (1988) 54. [106] Y.C. Lee, Anal. Biochem., 189 (1990) 151. [107] J. Sullivan, M. Douek, J. Chromatogr. A, 671 (1994) 339.


[108] R. Weinberger, Practical Capillary Electrophoresis (Second Edition), Academic Press, San Diego, 2000.

[109] A. Tiselius, J Chem Soc (1954) 2650. [110] P. Castignolles, M. Gaborieau, E.F. Hilder, E. Sprong, C.J. Ferguson, R.G. Gilbert, Macromol.

Rapid Commun., 27 (2006) 42. [111] R. Weinberger, Practical capillary electrophoresis, Academic Press San Diego, CA, 1993. [112] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, J.M. Mallet, P. Gareil, Anal. Chem., 83 (2011) 7381. [113] S. Rovio, J. Yli-Kauhaluoma, H. Siren, Electrophoresis, 28 (2007) 3129. [114] T. Soga, D.N. Heiger, Anal. Biochem., 261 (1998) 73. [115] A. Vorndran, P. Oefner, H. Scherz, G. Bonn, Chromatographia, 33 (1992) 163. [116] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, P. Gareil, J. Vial, J. Sep. Sci., 35 (2012) 1351. [117] A. Gürel, J. Hızal, N. Öztekin, F. Erim, Chromatographia, 64 (2006) 321. [118] D.L. Taylor, C.J. Ferris, A.R. Maniego, P. Castignolles, M. in het Panhuis, M. Gaborieau, Aust.

J. Chem., 65 (2012) 1156. [119] M. Mnatsakanyan, J. Thevarajah, R. Roi, A. Lauto, M. Gaborieau, P. Castignolles, Anal.

Bioanal. Chem., 405 (2013) 6873. [120] A. Rendleman J, Carbohydrates in Solution (Advances in Chemistry, Vol. 117), American

Chemical Society, 1973, p. 51. [121] A. Taga, S. Kodama, Chromatographia, 75 (2012) 1009. [122] S. Hoffstetter-Kuhn, A. Paulus, E. Gassmann, H.M. Widmer, Anal. Chem., 63 (1991) 1541. [123] P. Schmitt-Kopplin, K. Fischer, D. Freitag, A. Kettrup, J. Chromatogr. A, 807 (1998) 89. [124] H. Schwaiger, P.J. Oefner, C. Huber, E. Grill, G.K. Bonn, Electrophoresis, 15 (1994) 941. [125] F. Foret, L. Křivánková, P. Boček, Capillary zone electrophoresis, Weinheim, New York, 1993. [126] L.A. Colon, R. Dadoo, R.N. Zare, Anal. Chem., 65 (1993) 476. [127] A.Z. Carvalho, J.A.F. da Silva, C.L. do Lago, Electrophoresis, 24 (2003) 2138. [128] P. Tůma, K. Málková, E. Samcová, K. Štulík, Anal. Chim. Acta, 698 (2011) 1. [129] S. Honda, E. Akao, S. Suzuki, M. Okuda, K. Kakehi, J. Nakamura, Anal. Biochem., 180 (1989)

351. [130] W. Lu, R.M. Cassidy, Anal. Chem., 65 (1993) 2878. [131] M. Vaher, K. Helmja, A. Käsper, M. Kurašin, P. Väljamäe, M. Kudrjašova, M. Koel, M.

Kaljurand, Catal. Today, 196 (2012) 34. [132] S. Rovio, H. Simolin, K. Koljonen, H. Siren, J. Chromatogr. A, 1185 (2008) 139. [133] C. Sarazin, N. Delaunay, C. Costanza, V. Eudes, P. Gareil, Talanta, 99 (2012) 202. [134] M.J. Playne, J. Sci. Food Agric., 36 (1985) 638. [135] A. Kosugi, A. Kondo, M. Ueda, Y. Murata, P. Vaithanomsat, W. Thanapase, T. Arai, Y. Mori,

Renew energ, 34 (2009) 1354. [136] C. Martın, M. Galbe, C.F. Wahlbom, B. Hahn-Hägerdal, L.J. Jönsson, Enzyme Microb.

Technol., 31 (2002) 274. [137] K.D. Altria, J.S. Howells, J. Chromatogr. A, 696 (1995) 341. [138] T. Soga, M. Serwe, Food Chem., 69 (2000) 339. [139] R.M. Silverstein, F.X. Webster, D.J. Kiemle, Spectrometric identification of organic

compounds, John Wiley & Sons, 2005. [140] J.B. Lambert, Organic Structural Spectroscopy, Prentice Hall PTR, 1998. [141] B.C. Gilbert, J. Davies, D.M. Murphy, D. Becker, Electron Paramagnetic Resonance, Royal

Society of Chemistry, 2006. [142] B.C. Gilbert, D.M. King, C.B. Thomas, J. Chem. Soc., Perkin Trans. 2 (1981) 1186. [143] B.C. Gilbert, D.M. King, C.B. Thomas, J. Chem. Soc., Perkin Trans. 2 (1982) 169. [144] W. Buchberger, in: P. Castignolles (Ed.), Personal communication, Austria, 2013. [145] S.T. Park, J. Kim, K. Choi, H.R. Lee, D.S. Chung, Electrophoresis, 33 (2012) 2961.


[146] H. Turkia, S. Holmström, T. Paasikallio, H. Sirén, M. Penttilä, J.-P. Pitkänen, Anal. Chem., 85 (2013) 9705.

[147] M.R. Toutounji, School of Science and Health, University of Western Sydney, 2013.


Documents

Investigating and applyingfree solution capillary ......Australasian Polymer Symposium proceedings. ISBN number: 978-0-646-57205-5 ISBN number: 978-0-646-57205-5 6 th International