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Technological University Dublin Technological University Dublin
ARROW@TU Dublin ARROW@TU Dublin
Doctoral Science
2020-12
Development of Novel Pectinase and Xylanase Juice Clarification Development of Novel Pectinase and Xylanase Juice Clarification
Enzymes via a Combined Biorefinery and Immobilization Enzymes via a Combined Biorefinery and Immobilization
Approach Approach
Shady Hassan Technological University Dublin, [email protected]
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Recommended Citation Recommended Citation Hassan, S. (2020) Development of Novel Pectinase and Xylanase Juice Clarification Enzymes via a Combined Biorefinery and Immobilization Approach, Doctoral Thesis, Technological University Dublin. DOI:10.21427/g2ey-hk88
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Development of Novel Pectinase and Xylanase Juice
Clarification Enzymes via a Combined Biorefinery
and Immobilization Approach
A THESIS SUBMITTED TO THE TECHNOLOGICAL UNIVERSITY DUBLIN FOR
THE AWARD OF DOCTOR OF PHILOSOPHY
SHADY HASSAN, B.Sc., M.Sc.
School of Food Science and Environmental Health,
College of Sciences and Health,
Technological University Dublin, Dublin, Ireland
December 2020
Supervisors: Dr. Amit K. Jaiswal
Dr. Gwilym Williams
i
Abstract
Hydrolytic enzymes, such as pectinase and xylanase, may be harnessed for numerous
industrial applications in food industry. Therefore, economic factors such as
achievement of optimum yields and overall production costs, in addition to biocatalyst
instability, are the main obstacles to the industrial production and exploitation of a
enzymes. For example, microbially-derived enzymes are typically produced in
fermenters using expensive growth media, which may account for 30 to 40% of the
production cost, and such expense may be compounded further by downstream
processing operations.
To counter such disadvantages, the major trend in industrial utilization of enzymes in
cost-sensitive processes has been to immobilize such biocatalysts on a solid support,
thereby mediating the key advantage of reusability. A complementary approach in
recent years has been to target reduction in upstream processing costs in enzyme
production by incorporation of negative cost raw materials into the medium
composition, Food waste such as Brewers’ spent grain (BSG) constitutes an
environmental problem in Ireland but has the potential to provide a continuous and
renewable feedstock (carbon source) for production of industrial enzymes, thereby
reducing production costs. However, a significant barrier to the exploitation of
lignocellulose is the recalcitrant nature of the biomass, which usually requires
expensive pre-treatment to release sugars. The key goal of this study was to pursue an
integrated upstream and downstream approach to develop novel immobilized enzyme
preparations for the juice industry. The aim of the project was achieved through the
following measures:
• Screening and isolation of microrganisms which produced xylanases and pectinases
possessing a physico-chemical profile of relevance to the fruit juice sector.
• Development of an optimal pretreatment strategy for BSG that would enhance their
suitability as a carbon source for the upstream processing of the microbe which
produced the enzymes.
• Optimisation of the upstream processing for the production of as the pectinase and
xylanase catalysts.
• Investigation of a novel method for enzyme immobilization, and establishment of
optimal process performance characteristics for use in juice clarification.
ii
The following were major outputs of this work:
• Microwave pre-treatment was the best measure for maximizing the yield of
reducing sugars from BSG. and demonstrated superior energy efficiency when
compared to ultrasound pretreatment.
• The highest sugar yield (64.4±7 mg) was obtained when 1.0 g of biomass was
pretreated with microwave energy of 600 w for 90 s, representing a relatively low
energy input of 0.001 Kwh of electricity.
• Among twenty-nine (29) microbial isolates recovered from spoiled fruits, Mucor
hiemalis, isolated from Bramley apple (Malus domestica), produced
xylanopectinolytic enzymes with the highest specific activity. The highest enzyme
activity (137 U/g, and 67 U/g BSG, for pectinase and xylanase, respectively) was
achieved in a medium that contained 15 g of BSG, at pH 6.0, temperature of 30°C,
and supplemented with 1.0 % xylan or pectin for inducing the production of
xylanase or pectinase, respectively. The partially purified and concentrated
enzymes were optimally active at 60°C and pH 5.0 (1602 U/ml of pectinase, and
839 U/ml of xylanase, respectively).
• Flake-shaped magnetic nanoparticles with average crystallite size of ~16 nm) were
biosynthesized using A. flavus. Pectinase and xylanase were covalently
immobilized on MNPs with efficiencies of ~84% and 77%, respectively.
• Deploying a coupling time up to 120 min, and an agitation rate of 213 rpm
(pectinase) - 250 rpm (xylanase), a maximum enzyme activity recovery onto
alginate beads was observed to be about 81-83% for both enzymes. Optimum
enzyme loading and genipin (crosslinker) concentration were found to be 50 U/ml
and 12 % (w/v), respectively. The immobilized enzyme preparations were suitable
for up to 5 repeated process cycles, losing about 45% (pectinase) - 49% (xylanase)
of their initial activity during this time. The maximum clarity of apple juice (%T660,
84%) was achieved at 100 min when immobilised pectinase (50 U/ml of juice) and
xylanase (20 U/ml of juice) were used in combination at 57°C.
iii
Declaration
I certify that this thesis which I now submit for examination for the award of Doctor
of Philosophy is entirely my own work and has not been taken from the work of others,
save and to the extent that such work has been cited and acknowledged within the text
of my work.
This thesis is prepared according to the regulations for graduate study by research of
the “Technological University Dublin” and has not been submitted in whole or in part
for another award in any other third level institution.
The work reported on in this thesis conforms to the principles and requirements of the
TU Dublin’s guidelines for ethics in research.
TU Dublin has permission to keep, lend or copy this thesis in whole or in part, on
condition that such use of the material of the thesis be duly acknowledged.
Signature Date 22/12/2020
Shady Hassan
iv
This dissertation is dedicated to both my parents …
my mother, and my late father
v
Acknowledgement
First of all, praise be to God for giving me strength to complete this work. I also
gratefully acknowledge the funding from TU Dublin (formerly DIT) under the
Fiosraigh PhD Scholarship programme, 2017.
I do not have words to express my real appreciation for my supervisors Dr. Amit K.
Jaiswal, and Dr. Gwilym A. Williams. They were always eager to help, be it a research
issue or a worldly matter. Their influence can be seen throughout this thesis, and I am
positive that it will be evident well beyond this thesis also.
My thanks also go out to the support I received from the collaborative work undertaken
with Dr. Brijesh K. Tiwari from Ashtown Food Research Centre, and Dr. Brendan
Duffy from the CREST centre (Centre for Research in Engineering Surface
Technology, TU Dublin). I would also like to thank Dr. Swarna Jaiswal (Former
CREST researcher), Dr. Susan Warren, and Dr. Aoife Power from the CREST centre
for their invaluable support during materials characterisation work. Sincere thanks to
Dr Jesus Frias from ESHI (Environmental Sustainability and Health Institute, TU
Dublin) for facilitating access to instrumental analysis laboratory. Big thanks go to the
technical officers, Jyoti Nair, Plunkett Clarke, Noel Grace, Tony Hutchinson, and
Aleksandra Krulikowska for being very supportive.
I greatly appreciate the support received from my wonderful lab-mates, especially Dr.
Rajeev Ravindran whose contribution during the early stage of my PhD has been
invaluable.
And to my wife, and daughter, without your constant support, and patience I would
not have completed this work.
vi
List of Abbreviations
BSG Brewer’s Spent Grain
NREL National Renewable Energy Laboratory
FTIR Fourier Transform Infra-red
FESEM Field Emission Scanning Electron Microscopy
XRD X-Ray Diffraction
DSC Differential Scanning Calorimetry
EDX Energy Dispersive X-ray
DNS Dinitrosalicylic Acid
SSF Solid State Fermentation
MNPs Magnetic Nanoparticles
OFAT One Factor at A Time
CCD Central Composite Design
CCRD Central Composite Rotatable Design
RSM Response Surface Methodology
ANN Artificial Neural Network
vii
Table of Contents
Abstract …………………………………………………………………….... i
Declaration …………………………………………………...……………... iii
Dedication …………………………………………………………………… iv
Acknowledgement …………………………………………………………... v
List of Abbreviations ………………………………………………………... vi
Table of Contents ……………………………………………………...……. vii
List of Tables …………………………………………………..…………… xii
List of Figures ……………………………………………………………… xiv
1 General introduction ……………………………………………….…..… 1
1.1 Motivation …………………………………………………….…..…… 2
1.2 Aim and Objectives …………………………………………..……….. 4
1.3 Organization of Thesis ……………………………….…………...…… 5
2 Literature review ………………………………………………………… 6
2.1 Introduction …………………………………..…………………..…… 8
2.2 Lignocellulosic feedstocks ………………………….…………..…….. 9
2.2.1 First generation ………………………………………..……...…… 9
2.2.2 Second generation ……………………………………..…...……... 10
2.3 Biobased products market ………………………………...……….….. 12
2.4 Enzyme production using lignocellulosic food waste …..………..…... 14
2.4.1 Lignocellulose as a raw material ……………………..……...…… 14
2.4.2 Structure of Lignocellulose …………………………..……...…… 14
2.4.3 Pretreatment of lignocellulose ….…………………..……..……… 16
2.4.3.1 Conventional approaches for pretreatment of lignocellulosic
biomass ………….………………………………………... 16
2.4.3.2 Green approaches for pretreatment of lignocellulosic biomass 18
2.4.3.3 Emerging technologies for pretreatment of Lignocellulosic
biomass …………………………………………….……… 20
2.4.3.3.1 Microwave Irradiation …………….……….…….……….. 20
2.4.3.3.2 Ultrasound ………………………………………..……….. 23
2.4.3.3.3 Techno-economic feasibility ……………….…....……….. 25
2.4.4 Agro-industry wastes as fermentation media ….……..….…...…… 27
2.4.4.1 Production of Xylanase …………………...……..……….…… 28
2.4.4.2 Production of Pectinase …………………...................…...…… 30
2.5 Immobilisation of enzymes ……………………..…….………….….. 31
2.5.1 Carrier materials ……………………………………..…..….….. 32
2.5.1.1 Magnetic nanoparticles ………………………….…..…..….….. 34
2.5.1.2 Alginate beads ………..……………………………..…..….….. 36
2.5.2 Enzyme immobilization methods ……………..…..…..………... 36
2.5.3 Application of Immobilised Enzymes in the Food and Beverage
Industry ……………………………………………….……………….. 38
viii
2.6 Enzymatic treatments in fruit juice production …….….…….……..…. 38
3 Materials & Methods ……………………………….…………………… 40
3.1 General ………………………………………….………………..…… 41
3.1.1 Commercial sources of common reagents ………………..……..... 41
3.1.2 Instruments …………………………………………...……..…….. 41
3.1.3 Software …………………………………….………..……..…….. 42
3.1.4 Analytical techniques ………………………….………..…..…….. 42
3.1.4.1 Compositional analysis ………………..………………...…..… 42
3.1.4.2 Reducing sugar analysis ……………..……………...…..…..… 43
3.1.4.3 Enzymatic hydrolysis ……………………………….....…..… 43
3.1.4.4 Assay of Total Protein ………………….……………...…..… 43
3.1.4.5 Assay of enzyme activity ………………………..…......…..… 43
3.1.4.6 Fourier transform infra-red spectroscopy analysis …………... 44
3.1.5 Molecular Identification of Fungi …………….……..……..…….. 44
3.1.6 Statistical analysis …………………………….……..……..…….. 45
Section I : Pretreatment of Lignocellulose 46
4 The application of microwave and ultrasound pretreatments for
enhancing saccharification of brewers' Spent Grain (BSG) ...…….… 47
4.1 Introduction …………………………………………...………..….… 48
4.2 Methodology ……………………………………………………....… 49
4.2.1 Pretreatment of substrate (BSG)………………...……….……..... 50
4.2.1.1 Microwave pretreatment ……………………………....…..… 50
4.2.1.2 Ultrasound pretreatment……………………………….…..… 50
4.2.2 Enzymatic hydrolysis …………………………………………..... 50
4.2.3 Total reducing sugars …………….........................…………........ 51
4.2.4 Fourier transform infrared spectroscopy analysis ……………...... 51
4.3 Results and discussion …………………………………….……....… 51
4.3.1 Pretreatment of BSG for recovery of fermentable sugars …......... 51
4.3.1.1 Microwave pretreatment ………………………….…...…..… 51
4.3.1.2 Ultrasound pretreatment ………………………….…...…..… 54
4.3.2 FTIR characterization of pretreated BSG …………………......... 57
4.2 Conclusion ……………………………………...………………....… 58
5 Optimisation of ultrasound pretreatment of brewers' Spent Grain
(BSG) …………………………………………………………..…....… 60
5.1 Introduction ……………………………………..……..……..….… 61
5.2 Methodology …………………………………….……………....… 63
5.2.1 Pretreatment of substrate (BSG)……………………….……..... 63
5.2.1.1 Experimental design…………………………............…..… 63
5.2.1.2 Model development and optimization …………………..… 64
5.2.2 Enzymatic hydrolysis …………………….…………………..... 65
5.2.3 Characterization of raw and pre-treated substrate …………...... 65
ix
5.2.3.1 Compositional analysis ………………………...........…..… 65
5.2.3.2 Total reducing sugars ………………………..….......…..… 65
5.2.3.3 Fourier transform infra-red spectroscopy analysis ……...… 65
5.2.3.4 Scanning electron microscopy ……………………....…..… 65
5.2.3.5 Thermal behavior …………………………...........……..… 65
5.3 Results and discussion …………………………….………….....… 66
5.3.1 Effect of pretreatment on composition of BSG ………….......... 66
5.3.1.1 Composition of BSG ………………………..........……..… 66
5.3.1.2 Modelling and optimization of ultrasound pretreatment …. 66
5.3.1.3 FTIR analysis……………………….……………..….....… 72
5.3.1.3 Scanning electron microscopy………………………......… 73
5.3.1.3 Differential scanning calorimetry ……………………....… 74
5.4 Conclusion ………………...…………………….………..………....… 75
Section II : Enzyme Production using Lignocellulosic Food Waste 77
6 Pectinase and xylanase production by fungi isolated from spoiled fruit 78
6.1 Introduction ………………………………………………..……..….… 79
6.2 Methodology ………………………………………………………....… 81
6.2.1 Pretreatment of substrate (BSG)………………...………….……..... 81
6.2.2 Isolation and screening of xylanopectinolytic enzyme-producing
fungi ………………………………………………………………... 81
6.2.2.1 Isolation of fruit-rotting fungi………………………..........…..… 81
6.2.2.2 Molecular identification ………………………………………… 82
6.2.2.2 Qualitative screening of xylanopectinolytic enzyme-producing
fungi...........................................................................................… 82
6.2.2.3 Quantitative determination of xylanopectinolytic enzymes
produced by fungi......................................................................… 83
6.2.3 Production of xylanopectinolytic enzymes using BSG under
different conditions………………………….…………..….……...... 83
6.2.3.1 Partial purification and concentration of pectinases and
xylanases..………… ………………………………………...… 84
6.2.3.1 Determination of pH and temperature profiles..……………..… 84
6.2.4 Protein and enzyme assays ……………….………………………... 85
6.2.4.1 Assay of Total Protein..………………….…………….……..… 85
6.2.4.2 Assay of enzyme activity..…………………..………………..… 85
6.3 Results and discussion …………………………….………………....… 85
6.3.1 Isolation and screening of xylanopectinolytic enzyme-producing
fungi …………………………………………….……………….… 85
6.3.1.1 Isolation xylanopectinolytic enzyme-producing fungi…..…....… 85
6.3.1.2 Screening of xylanopectinolytic enzyme-producing fungi……… 87
6.3.1.3 Molecular Identification of Mucor sp. AB1 ……………………. 88
6.3.2 Production of Xylanopectinolytic Enzymes using BSG…….............. 89
6.3.2.1 Optimization of production………………...….……………....… 89
6.3.2.2 Partial purification and concentration……………………….....… 91
x
6.3.2.3 Effect of temperature and pH on enzyme activity……………..… 92
6.4 Conclusion ………………………………….………………………....… 94
Section III : Immobilization of Enzymes 95
7 Magnetic nanoparticles as carrier for enzymes immobilization ……...… 96
7.1 Introduction ………….…………………………………………………... 97
7.2 Methodology ………………………………………………..…….……... 99
7.2.1 Isolation of Aspergillus sp. SR2 ………………………………..…… 99
7.2.2 Molecular Identification of Aspergillus sp. SR2……………….….… 99
7.2.3 Biosynthesis of magnetic nanoparticles …………………………..… 100
7.2.4 Characterization of MNPs ………………………………………..…. 100
7.2.5 Immobilization of biocatalysts on MNPs ………………….……..…. 101
7.2.6 Evaluation of the nano-immobilized enzymes ………………..…..…. 102
7.3 Results and discussion ……………………………………...…….……... 102
7.3.1 Molecular Identification of Aspergillus sp. SR2………………..…… 102
7.3.2 Biosynthesis of magnetic nanoparticles ……….………………..…… 103
7.3.3 Characterization of MNPs……….…………………….………..…… 105
7.3.4 Immobilization of biocatalysts on MNPs ……….……………...…… 108
7.3.5 Evaluation of the nano-immobilized enzymes ………….….……..… 110
7.4 Conclusion ……………………………………………..…...…….……... 113
8 Alginate hydrogel beads as carrier for enzymes immobilization ……..… 115
8.1 Introduction ………….…………………………………………………... 116
8.2 Methodology ………………………………………………..…….……... 118
8.2.1 Enzyme immobilisation …………………………………….…..…… 118
8.2.1.1 Experimental design and data acquisition for ANN modeling ..… 118
8.2.1.2 Covalent immobilization of pectinase and xylanase ……….....… 119
8.2.1.3 Evaluation of the immobilized enzymes ……………………..…. 120
8.2.2 Enzymatic treatment of apple juice ………………….….……...…. 121
8.2.2.1 Experimental design and data acquisition for ANN modeling ….. 121
8.2.2.2 Clarification of apple juice using immobilized enzymes …….…. 122
8.2.2.3 Artificial Neural Network (ANN) modelling and analysis ….….. 123
8.3 Results and discussion …………………………………...…….……... 124
8.3.1 Enzyme immobilisation………………………..……………..…… 124
8.3.1.1 The ANN model training …………………….…..………....…… 124
8.3.1.2 Selection neural network model……….……………..……..…… 126
8.3.1.3 Optimization of enzyme immobilization using trained ANNs .… 130
8.3.1.4 Evaluation of the immobilized enzymes ……..….….….……..… 134
8.3.2 Enzymatic treatment of apple juice ……..………..…….….…..… 136
8.3.2.1 The ANN model training ……..………………………..……..… 136
8.3.2.2 Selection neural network model ……..………………..….……… 137
8.3.2.3 Optimization of juice clarification process using trained ANNs ... 140
8.4 Conclusion …………………………………………..…...…….……... 144
xi
9 General conclusions and future recommendations ……………………… 146
9.1 General conclusions ………………………............................…………... 147
9.2 Future recommendations ………………………………………….……... 152
9.2.1 Other potential lignocelluosic wastes ………………………………. 152
9.2.2 Isolation of pectin and xylan degrading bacteria …………………… 152
9.2.3 The application of magnetic nanoparticles in food industry………… 153
9.2.4 Polices ………………………………………………………………. 154
9.2.5 Sustainable feedstock logistics…………………………………….… 154
10 References ………………………………………………………………….. 156
11 List of Publications ……………………………………..………………….. 192
11.1 Peer Review Publications ………………………...……………………. 193
11.2 Poster/Oral Presentations …………………………….………...………. 194
12 List of Employability Skills and Discipline Specific Skills Training …… 195
xii
List of Tables
Table 2.1 Chemical composition of different lignocellulosic feedstocks (% dry basis) 15
Table 2.2 Major advantages and disadvantages of each of the common pretreatment
methods ………………………………………………………………………………
17
Table 2.3 Major advantages and disadvantages of selected green chemistry pretreatment
methods.………………………………………………………………………..…...…
19
Table 2.4 Enzyme support materials and their properties ……………………………. 33
Table 4.1 Effect of microwave power on sugar yield from BSG …………………….. 52
Table 4.2 Effect of ultrasound frequency on sugar yield from BSG ………………… 55
Table 4.3 FTIR peak assignments for lignocellulose …………………………….…… 58
Table 5.1 Experimental range and levels of the independent variables ……………... 64
Table 5.2 ANOVA table for the quadratic model of ultrasound pretreatment ………. 67
Table 5.3 Experimental conditions and results of central composite design ………... 68
Table 5.4 FTIR peak assignments for lignocellulose ………………………………... 73
Table 6.1 List of fruit-spoilage fungi isolated from infected fruits …………………. 86
Table 6.2 Secondary evaluation of xylanases and pectinases production by fruit-
spoilage fungi ………………………………………………………………………...
87
Table 6.3 Nucleotide sequences of Mucor hiemalis …………………………………. 88
Table 6.4 Summary of purification of pectinases and xylanases ……………………. 93
Table 7.1 Nucleotide sequences of Aspergillus flavus SR2 ………………………… 103
Table 8.1 Independence factors and corresponding levels for enzyme immobilization. 119
Table 8.2 Independence factors and corresponding levels for clarification of apple
juice……………………………………………………………………………………
121
Table 8.3 Experimental design showing the observed and predicted values of enzyme
(pectinase or xylanase) activity recovery (%) as output for training dataset…
128
Table 8.4 Experimental design showing the observed and predicted values of enzyme
(pectinase or xylanase) activity recovery (%) as output for testing dataset…..
129
Table 8.5 Experimental validation of the optimization values predicted by ANN for
enzyme (pectinase or xylanase) activity recovery (%)…………….…………
134
Table 8.6 Experimental design showing the observed and predicted values of juice
clarification (%) as output for training dataset………………………………………..
139
xiii
Table 8.7 Experimental design showing the observed and predicted values of juice
clarification (%) as output for testing dataset…………………………………………
140
Table 8.8 Application of different commercial enzyme preparations in apple juice
clarification…………………………………………………………………………….
144
Table 9.1 Comparison between MNP and alginate beads as carriers for pectinase and
xylanase
151
xiv
List of Figures
Figure 1.1 Industrial enzyme market (USD Million), 2013-2024 …………. 02
Figure 2.1 Schematic diagram shows differences between lignocelluosic
feedstocks from the first and second generation: sources, valorisation processes,
and end products ……………………………….………………………………. 11
Figure 2.2 Projected production of biobased chemicals and materials in Europe
2020/2030 …………………………………………………...…………………. 13
Figure 4.1 FTIR spectra of native, microwave pretreated, and ultrasound
pretreated BSG……………………………..……………..……………….…… 57
Figure 5.1 Response surface plots representing the effect of independent
variables on reducing sugar yield: (a) Effect of solid-to-liquid ratio and
temperature on reducing sugar yield when the response surface is fixed at
ultrasound power = 60% and time = 40 min; (b) Effect of solid-to-liquid ratio
and ultrasound power on reducing sugar yield when the response surface is fixed
at temperature = 40°C and time = 40 min; (c) Effect of solid-to-liquid ratio and
time on reducing sugar yield when the response surface is fixed at temperature
= 40°C and ultrasound power = 60%; (d) Effect of temperature and ultrasound
power on reducing sugar yield when the response surface is fixed at Solid-to-
liquid ratio = 15% (w/v) and time = 40 min; (e) Effect of temperature and time
on reducing sugar yield when the response surface is fixed at solid-to-liquid
ratio = 15% (w/v) and ultrasound power = 60%; and (f) Effect of ultrasound
power and time on reducing sugar yield when the response surface is fixed at
solid-to-liquid ratio = 15 % (w/v) and temperature = 40°C……………………... 69
Figure 5.2 FTIR spectra of native and pretreated BSG………………………… 72
Figure 5.3 Micrographs by scanning electron microscopy (SEM) of native (on
Left), and the modified structure of the pretreated BSG (on Right).
Magnification, 1000-fold………………………………………………………. 74
Figure 5.4 DSC thermogram of native and pretreated BSG…………….……. 75
Figure 6.1 Effect of temperature, substrate, and pH on pectinases and
xylanases production by Mucor hiemalis …………………………………… 90
Figure 6.2 Effect of temperature, and pH on pectinase and xylanase
activity……………………………………………………………………..…. 92
xv
Figure 7.1 Shows the culture filtrate of A. flavus (A), iron salts solution (B),
black coloration due to mixing of solutions A+B for 5 min (C), separation of
MNPs from reaction mixture using an external magnet (D) , and the magnetic
behavior of dried MNPs in the presence of a magnet (E).……………….…….
104
Figure 7.2 UV-vis spectrum of MNPs.…………….…………………………. 105
Figure 7.3 MNPs observed in EDX spectrum (A), electron micrograph region
at 200 µm (B), distribution of Fe in elemental mapping (C), and distribution of
O in elemental mapping (D).……………………………………….…….…….
106
Figure 7.4 SEM micrograph shows irregular flakes-like clusters.……………. 107
Figure 7.5 XRD spectrum of MNPs.…………………………………….……. 108
Figure 7.6 Panels (A–C) shows the effect of temperature (A), pH (B), storage
time (C), and recycle count (D) on relative activity of immobilized pectinase
and xylanase in comparison with free enzyme.……………………….….…….
111
Figure 8.1 Schematic diagram of the experimental set-up for clarification of
apple juice.…………….……………………………………………………….
122
Figure 8.2 The performance of different learning algorithms (Incremental
backpropagation algorithm, IBP; Batch backpropagation algorithm, BBP;
Quick propagation algorithm, QP; and Levenberg-Marquardt algorithm, LM)
for training data versus of the number of neurons in the hidden layer for
predicting the activity recovery of pectinase (A) and xylanase (B) onto alginate
beads.…………………………………………………………….
125
Figure 8.3 The illustration of multilayer normal feed-forward neural network.
The neural network having three inputs of variables (genipin, enzyme load,
coupling time, and agitation rate), one hidden layer with three neurons (nodes)
and one output of response (enzyme activity recovery).…………………..….
126
Figure 8.4 Comparison of different learning algorithms (Incremental
backpropagation algorithm, IBP; Batch backpropagation algorithm, BBP;
Quick propagation algorithm, QP; and Levenberg-Marquardt algorithm, LM)
with 6 neurons in the hidden layer for predicting the activity recovery of
pectinase and xylanase onto alginate beads.………………….……….…….
126
Figure 8.5 Grid color charts representing the effect of independent variables on
pectinase activity recovery (%): (a) Effect of genipin concentration and
pectinase load on activity recovery when coupling time and agitation rate are
xvi
fixed at 120 min and 213 rpm, respectively; (b) Effect of genipin concentration
and coupling time on activity recovery when pectinase load and agitation rate
are fixed at 50 U/ml and 213 rpm, respectively; (c) Effect of genipin
concentration and agitation rate on activity recovery when pectinase load and
coupling time are fixed at 50 U/ml and 120 min, respectively; (d) Effect of
pectinase load and coupling time on activity recovery when genipin
concentration and agitation rate are fixed at 12 % (w/v) and 213 rpm,
respectively; (e) Effect of pectinase load and agitation rate on activity recovery
when genipin concentration and coupling time are fixed at 12 % (w/v) and 120
min, respectively; and (f) Effect of coupling time and agitation rate on activity
recovery when pectinase load and genipin concentration are fixed at 50 U/ml
and 12 % (w/v) , respectively…………………………………………….…….
131
Figure 8.6 Grid color charts representing the effect of independent variables on
xylanase activity recovery (%): (a) Effect of genipin concentration and xylanase
load on activity recovery when coupling time and agitation rate are fixed at 120
min and 250 rpm, respectively; (b) Effect of genipin concentration and coupling
time on activity recovery when xylanase load and agitation rate are fixed at 50
U/ml and 250 rpm, respectively; (c) Effect of genipin concentration and
agitation rate on activity recovery when xylanase load and coupling time are
fixed at 50 U/ml and 120 min, respectively; (d) Effect of xylanase load and
coupling time on activity recovery when genipin concentration and agitation
rate are fixed at 12 % (w/v) and 213 rpm, respectively; (e) Effect of xylanase
load and agitation rate on activity recovery when genipin concentration and
coupling time are fixed at 12 % (w/v) and 120 min, respectively; and (f) Effect
of coupling time and agitation rate on activity recovery when xylanase load and
genipin concentration are fixed at 50 U/ml and 12 % (w/v) ,
respectively.…………………..……………………………………….….…….
132
Figure 8.7 Panels (A–C) shows the effect of temperature (A), pH (B), storage
time (C), and recycle count (D) on relative activity of immobilized pectinase
and xylanase in comparison with free enzyme.………………………….…….
135
Figure 8.8 The left panel (a) shows the performance of different learning
algorithms (Incremental backpropagation algorithm, IBP; Batch
backpropagation algorithm, BBP; Quick propagation algorithm, QP; and
xvii
Levenberg-Marquardt algorithm, LM) for training data versus of the number of
neurons in the hidden layer for predicting the apple juice clarification (%) using
pectinase and xylanase immobilized onto alginate beads. The right panel (b),
shows schematic diagram of the optimal multi-layer, normal feed-forward
neural network architecture for apple juice clarification.………..……….…….
137
Figure 8.9 The scatter plots of the predicted juice clarification versus the
observed juice clarification for training dataset that shows the performed R2 and
RMSE of different learning algorithms at optimal neural network architecture
(4-3-1).…………….………………………………………………………...….
138
Figure 8.10 Importance of effective parameters on apple juice clarification….. 143
Figure 8.11 Three-dimensional surface plot of pectinase load and xylanase load
effects on apple juice clarification. The other variables (temperature and time)
were kept constant at the optimal values……………..…………….………..….
143
1
General introduction
Chapter 1
General Introduction
This chapter presents a brief explanation of the motivation behind the work, and a
summary of the principal objectives.
2
1.1 Motivation
Enzymes have long been known as biocatalysts, selectively catalysing thousands of
metabolic processes, and usually acting under milder reaction conditions than
traditional chemicals. They are used in numerous industries such as detergents, textile,
cosmetics, leather processing, pharmaceuticals, biodiesel production, and waste-water
treatment, and are a major tool in the production of modern food. In the food industry,
enzymes are used to extract, produce and enhance the quality and quantity of a range
of food products, spanning starch breakdown (α-amylase), reduction of proteins
(especially glutenins) in bread flour (protease), improvement of dough stability for
bread making (xylanase), cheese manufacturing (lipase, and lysozymes), viscosity
reduction and imparting food texture (cellulose, hemicellulose), wine production
(pectinase, urease), and meat tenderisation (bromelain, papain). The global enzyme
market was valued at USD 4.62 billion in 2015 (Figure 1.1) (Grand View Research,
2016). Furthermore, the application scope is expected to grow at a CAGR of over 7.0%
from 2016 to 2024 due to increasing use in the leather and bioethanol sectors.
Figure 1.1. Industrial enzyme market (USD Million), 2013-2024
(Source: www.grandviewresearch.com)
3
On its own, pectinase represents 70% of the global juices processing enzymes market
(Dataintelo, 2019). The latter market is estimated to reach USD 671 million by the
end of 2025, from USD 460.2 million in 2018, delivering a CAGR of 5.5% through
the forecast period (2019-2025). By application, the enzymes used in apples is account
for the largest share (30%), followed by oranges (20%), and three other main fruits
combined, namely: Peach, Pineapple and Pear (>10%).
Enzymes for industrial use are largely produced through fungal or bacterial
fermentation using expensive raw materials as a growth medium. Roughly, 30-40% of
the overall production cost of industrial enzymes is estimated to come from the cost
of the growth medium, which presents a major drawback for industrial enzyme
producers. In addition to the high upstream cost of enzyme production, aspects such
as purification, instability and low yields restricted the more widespread use of
enzymes in industry. Enzyme immobilisation is the process of attaching an enzyme
molecule to a solid support with the purposes of its reuse and easy product separation,
thereby providing a means of cost reduction . Immobilisation techniques may also help
conserve the beneficial properties of enzymes, thereby offering greater stability over
a wide range of pH and temperature. and often improving activity and selectivity in
reactions (Barbosa et al., 2014).
The use of lower-cost growth substrates for the production of enzymes may provide
favourable enzyme production economics. Waste materials from a wide range of agro-
industrial processes may be used as substrates for microbial growth, and subsequently
production of a range of high value products, including enzymes of interest. The Food
and Agriculture Organization of the United Nations (FAO) estimates that over 1.3
billion tonnes of lignocellulosic waste are generated from various food industries
operating throughout the world (FAO, 2012). Besides contributing to pollution, such
4
wastes also represent a loss of valuable biomass and nutrients, thereby making the
valorisation of food waste a priority topic for research. Each year the problem of food
waste costs over €2 billion to the Irish economy (EPA, 2019). With composting as the
only currently available solution, much of the food that still possesses certain essential
components goes to waste. Utilisation of these agro-residues in bioprocesses has the
dual advantage of providing alternative substrates for growth processes, as well as
solving the disposal problems.
Aim and Objectives:
This project aims to address the above-mentioned issues by utilisation of brewer’s
spent grain (BSG) as a growth medium component for production a novel and cost-
effective hydrolytic enzyme cocktails (pectinase and xylanase). This will be achieved
through the following objectives:
1. Investigation of the effects of novel technologies for pretreatment of
lignocellulose, such as microwave and ultrasound, on the chemical
composition of BSG.
2. Development of an optimal pretreatment strategy to provide maximum
fermentable sugars for the upstream processing.
3. Screening fungal isolates from environmental samples for pectinase and
xylanase production.
4. Investigation of the suitability of employing pretreated BSG as a fermentation
media for pectinase and xylanase production.
5. Optimisation of fermentation processes for the production of
xylanopectinolytic cocktails.
6. Investigation of the suitability of inorganic nanoscale particles (e.g. magnetic
nanoparticles) and a natural polymer (e.g. alginate beads) as immobilisation
matrices for enhancing reusability, and operational-storage stability of the
enzymes.
7. Reducing the total amount of the enzyme preparations used in the cost-
sensitive market of apple juice.
5
Organisation of Thesis
This thesis begins with general introduction (Chapter 1), followed by chapter 2 of
literature review that includes the current state and prospects of lignocellulosic
biorefineries in Europe, new technologies for pretreatment of lignocellulose, and the
valorisation of lignocellulose for enzyme production. Chapter 3 includes protocols of
various studied pretreatments using microwave and ultrasound, in addition to the
analytical and statistical methods employed in the experiments. Section A is focused
on pretreatment strategy of BSG and includes two chapters, chapters 4 and 5 which
discuss the effects of microwave and ultrasound pretreatments on BSG. Section B is
dedicated to the upstream processing and the use of pretreated BSG as a fermentation
medium for production of pectinases and xylanases by environmental isolates (chapter
6). Section C includes two chapters and investigates the efficacy of magnetic
nanoparticles (chapter 7) and alginate beads (chapter 8) as immobilisation carriers for
enhancing reusability, and operational-storage stability of enzymes. Chapter 8 also
deals with the subsequent utilisation of the immobilised enzymes for apple juice
clarification. The last chapter (9) summarises the findings of this study and details the
future recommendations based on the conclusions from the work carried out.
In conclusion, this thesis makes a focused and comprehensive effort for biorefining of
solid barley waste (BSG) generated as a by-product in brewery industry for the
production of industrially relevant enzymes. The biorefining process start with BSG
pretreatment, followed by the subsequent fermentation of BSG to produce pectinase
and xylanase. The enzymes produced were then successfully immobilised onto novel
magnetic nanoparticles and a natural hydrogel. Enzymes immobilised on the hydrogel
were investigated for potential application in apple juice clarification.
6
Chapter 2
Literature Review
This chapter is a literature review of the current state and prospects of lignocellulosic
biorefineries in Europe with insights into drivers, challenges, and opportunities of this
nascent industry. Furthermore, this chapter discusses the traditional and new
technologies for pretreatment of lignocellulose, in particular, microwave and
ultrasound. Additionally, this chapter provides a comprehensive review about the
valorisation of lignocellulose for enzyme production, with a focus on pectinase and
xylanase.
7
The information included in this chapter has been published as peer reviewed review
articles:
1. Hassan, SS., Williams, GA., Jaiswal, AK. (2019). Lignocellulosic
Biorefineries in Europe: Current State and Prospects. Trends in Biotechnology
37 (3), 231-234. https://doi.org/10.1016/j.tibtech.2018.07.002
2. Hassan, SS., Williams, GA., Jaiswal, AK. (2019). Moving towards the second
generation of lignocellulosic biorefineries in the EU: drivers, challenges, and
opportunities. Renewable and Sustainable Energy Reviews. 101, 590-599.
https://doi.org/10.1016/j.rser.2018.11.041
3. Hassan, SS., Williams, GA., Jaiswal, AK. (2018). Emerging technologies for
the pretreatment of lignocellulosic biomass. Bioresource Technology 262, 310-
318. https://doi.org/10.1016/j.biortech.2018.04.099
4. Ravindran, R., Hassan, SS., Williams, GA., Jaiswal, AK. (2018). A Review on
Bioconversion of Agro-industrial Wastes to Industrially Important Enzymes.
Bioengineering. 5 (4), 93. https://doi.org/10.3390/bioengineering5040093
8
2.1 Introduction
The future development of Europe now faces Unprecedented challenges, spanning
food security, climate change, and an over-dependence on non-renewable resources.
Simultaneously, it must balance strategies that harness renewable resources to
maintain environmental sustainability, while maintaining economic growth. To
achieve this, in 2012, the European Commission (EC) launched the European
bioeconomy strategy entitled “Innovating for sustainable growth: a bioeconomy for
Europe”. Within this strategy, the modern bioeconomy is defined centrally by the
production of biomass or the utilization of lignocelluosic wastes, with subsequent
conversion into value-added products, such as bio-energy, as well as novel bio-based
innovation. At the EU level, the current bioeconomy has an annual turnover of 2.3
trillion EURO and generates a total employment of 18.5 million people.
Biorefining is defined as the sustainable processing of biomass into a spectrum of
marketable products (food, feed, chemicals, and materials) and energy (fuels, power
and/or heat) (Muranaka et al., 2017). Representing a cornerstone of the bioeconomy,
the goal of fully unlocking the value potential of lignocellulosic plant biomass in a
cost-effective way remains elusive. Upstream aspects such as biomass type, transport
logistics and the downstream value proposition offered by conversion products must
be reconciled with the recalcitrance of the lignocellulosic structure: there is, as yet, no
fully scalable yet cost-effective extraction method to unlock valuable sugars and lignin
from this matrix, and this remains a key short-term research goal.
Lignocellulosic feedstock options for biorefinery use range from food/non-food crops
to primary residues/secondary wastes from agroforestry. The S2Biom project has
estimated that a total of 476 million tons of lignocellulosic biomass need to be secured
9
to fulfil demand for bio-based products by 2030 (S2Biom, 2016). The market for bio-
based products is expected to be worth 40 million EURO by 2020, increasing to about
50 billion EURO by 2030 (average annual growth rate of 4%). Research in industry
and academia has been galvanized to address the twin challenge of lignocellulosic
breakdown and conversion into viable products, and consequently a myriad of
publications featuring laboratory and pilot scale studies for pretreatment and
conversion of lignocellulosic biomass into bioenergy and bioproducts are published
each year (Meneses et al., 2020; Cheah et al., 2020; Mirmohamadsadeghi et al., 2021;
Rahmati et al., 2020).
2.2 Lignocellulosic feedstocks
Integral to the biorefinery concept is accessing suitable feedstocks which are amenable
to cost-effective processing. Biorefining is a capital-intensive industry with large
capital expenditure (CAPEX) and requires knowledge of the feedstock resource base
that is sustainably available at low cost to support a facility (Ulonska et al., 2018).
2.2.1 First generation
The first generation of feedstocks depended on easily accessible and edible fractions
of food crops, with the main product being biofuel. Bioethanol may be produced from
sugar (e.g. sugarcane, sugarbeet, and sweet sorghum) and starch (e.g. corn, and
cassava) crops, while biodiesel is produced from oil seed crops (e.g. soybean, oil palm,
rapeseed, and sunflower) (Khan et al., 2018). However, in recent years, serious
criticisms have been raised about competition in land use that has arisen as a direct
consequence of incentivizing energy and oil crops at the expense of food crops
(Wesseler and Drabik, 2016) .
10
2.2.2 Second generation
The growing controversy of ‘food versus fuel’, along with associated production
economics, biofuel policies and sustainability trends, promoted the rise of a second
generation of feedstocks based on lignocellulosic biomass. The latter include non-
food, short rotation grasses that have high yield and suitability to marginal lands or
poor soils (e.g. poplar, willow, eucalyptus, alfalfa, and grasses such as switch, reed
canary, Napier and Bermuda), agricultural residues (e.g. forest thinning, sawdust,
sugarcane bagasse, rice husk, rice bran, corn stover, wheat straw, and wheat bran), and
agro-industrial wastes (e.g. potato and , orange peel, spent coffee grounds, apple
pomace, ground nut oil and soybean oil cake) (Muranaka et al., 2017). Critically, the
latter are so-called negative cost waste materials from other industries, and so
theoretically the value proposition has heightened appeal. However, such materials are
also the most refractory to extraction of sugars (Figure 2.1).
Food industry by-products encompass wastes from various industries such as
sugarcane bagasse (from sugar milling), pomace (pressing of tomato), apple and
grapes (juice), olives (for oil), brewer's spent grain (BSG - from beer-brewing), spent
coffee grounds (coffee preparation), as well as citrus and potato peels. The global
production of some of these humble wastes is significant. For example, BSG
generated from beer-brewing has been estimated at 3.4 million tonnes annually in the
EU alone, and over 4.5 million tons in USA as the largest craft beer producer (Chen et
al., 2017).
11
Figure 2.1. Schematic diagram shows differences between lignocellulosic feedstocks from the first and second generation: sources,
valorisation processes, and end products.
12
2.3 Biobased products market
Examples of potential bio-based products include biofuels (e.g. bioethanol, biodiesel,
and biogas), biochemicals (e.g. industrial enzymes, and nutraceuticals), and
biomaterials (e.g. biodegradable plastics) (Ravindran and Jaiswal, 2016). However,
supported by specific EU policies, bioenergy and biofuels have received greater
attention. By the year 2030, the EU aims to provide 25% of its transportation energy
via biofuels derived from advanced biorefineries (2nd generation biorefineries). By this
time, it also intends to replace 30% of oil-based chemicals by bio-based chemicals and
supplant non-degradable materials with degradable materials. Interestingly, 80% of
the EU bio-based infrastructure will be in rural areas, which are expected to support
community development programs. However, developing sustainable markets for bio-
based products, and raising the public awareness of this area will still be a challenge.
Even so, it is expected that evolving market demand, combined with further EU
policies acting to spur public awareness, will accelerate product development and
encourage private sector investment.
The Bio-based consortium in the EU aims to replace 30% of overall chemical
production with biomass-derived biochemicals by 2030 (BIC, 2012). According to the
National Renewable Energy Laboratory in USA, the latter can be finished products or
intermediates that then become a feedstock for further processing (Biddy et al., 2016).
Biochemicals produced from the biorefining of lignocellulose include organic acids
(e.g. citric, acetic, benzoic, lactic and succinic), microbial enzymes (e.g. amylase,
cellulase, pectinase, xylanase, mannanase), and building blocks for bio-based
polymers (e.g. phenylpropanoids, polyhydroxyalkanoates) (Kawaguchi et al., 2016;
Kumar et al., 2016; Ravindran et al., 2018a). The projected production of some
13
lignocellulosic-based chemicals and materials in Europe (in 2020 and 2030) is
summarized in Figure 2.2 (S2biom, 2016).
Figure 2.2. Projected production of biobased chemicals and materials in Europe
2020/2030
The global market for industrial enzymes is expected to increase at a compounded
annual growth rate (CAGR) of 4.7% between 2016-2021 (growing from
approximately $5.0 billion in 2016 to $6.3 billion in 2021; (Dewan, 2014)). Despite
the tangible demand, enzymes are relatively expensive reagents, and this adds to the
operational cost of processes that utilize them. Critical analysis of process plant
economics for enzyme production reveals that almost 50% of the cost of production is
associated with capital investment, while the cost of raw materials accounts for almost
one third of such costs. Substitution or complementation of feedstocks with
lignocellulosic sources can result in an increased return on investment.
Biopolymers& bioplastics
Solvents SurfactantsBulk
aromaticsMethanol Ethylene
2030 233 141 195 450 387 185
2020 77 44 69 150 77 0
0
100
200
300
400
500
600
Pro
ject
ed
pro
du
ctio
n (
kt)
Lignocellulosic-based chemicals & materials in Europe 2020/2030
14
2.4 Enzyme Production Using Lignocellulosic Food Waste
2.4.1 Lignocellulose as a Raw Material
Lignocellulosic biomass is a complex matrix that is relatively refractory to
degradation. Sugars are locked in a recalcitrant structure that requires a pretreatment
step to release them. Many conventional methods (e.g. chemical, physical, and
biological methods) are used for pretreating lignocellulosic biomass. However,
achieving a workable balance between pretreatment efficacy, cost and environmental
sustainability is difficult (Hassan et al., 2019).
2.4.2 Structure of Lignocellulose
Plant biomass is composed mainly of polysaccharides (cellulose, hemicellulose) and
lignin. Polysaccharides are polymers of sugars and therefore a potential source of
fermentable sugars, while lignin can be used to produce chemicals. Generally, cereal
residues (e.g. rice straw, wheat straw, corn stover, and sugarcane bagasse) contain a
large fraction of lignocellulose substances and represent the favourite feedstock for
biorefineries, while grasses, fruit and vegetable wastes have less lignocellulosic
content.
The ECN Phyllis2 database (www.phyllis.nl) is an open literature facility which is
readily available to users and documents the composition of biomass and waste.
Furthermore, table 2.1 shows the chemical composition of different lignocellulosic
feedstocks based on recent literatures published in 2016, 2017 and 2018. Biomass on
a dry weight basis generally contains cellulose (50%), hemicellulose (10–30% in
woods, or 20–40% in herbaceous biomass) and lignin (20–40% in woods or 10–40%
in herbaceous biomass) (Sharma et al., 2015). However, these ratios between cellulose,
15
hemicellulose and lignin within a single plant will vary with different factors like age,
harvesting season and culture conditions.
Table 2.1 Chemical composition of different lignocellulosic feedstocks
(% dry basis)
Source Cellulose Hemicellulose Lignin References Hardwood
Eucalyptus 44.9 28.9 26.2 (Muranaka et al., 2017)
Oak 43.2 21.9 35.4 (Yu et al., 2017)
Rubber wood 39.56 28.42 27.58 (Khan et al., 2018)
Softwood
Spruce 47.1 22.3 29.2 (Yu et al., 2017)
Pine 45.6 24.0 26.8 (Yu et al., 2017)
Japanese cedar 52.7 13.8 33.5 (Muranaka et al., 2017)
Grasses
Bamboo 46.5 18.8 25.7 (Chen et al., 2017)
Amur silver-grass 42.00 30.15 7.00 (Raud et al., 2016)
Natural hay 44.9 31.4 12.0 (De Caprariis et al., 2017)
Hemp 53.86 10.60 8.76 (Raud et al., 2016)
Rye 42.83 27.86 6.51 (Raud et al., 2016)
Reed 49.40 31.50 8.74 (Raud et al., 2016)
Sunflower 34.06 5.18 7.72 (Raud et al., 2016)
Silage 39.27 25.96 9.02 (Raud et al., 2016)
Szarvasi-1 37.85 27.33 9.65 (Raud et al., 2016)
Agro-industrial
waste
Walnut shell 23.3 20.4 53.5 (De Caprariis et al., 2017)
Groundnut shell 37 18.7 28 (Álvarez et al., 2018)
Pistachio shell 15.2 38.2 29.4 (Subhedar et al., 2017)
Almond shell 27 30 36 (Álvarez et al., 2018)
Pine nutshell 31 25 38.0 (Álvarez et al., 2018)
Hazelnut shell 30 23 38.0 (Álvarez et al., 2018)
Coconut coir 44.2 22.1 32.8 (Subhedar et al., 2017)
Cotton stalk 67 16 13 (Kim et al., 2016)
Hemp stalk 52 25 17 (Kim et al., 2016)
Acacia pruning 49 13 32 (Kim et al., 2016)
Sugarcane peel 41.11 26.40 24.31 (Huang et al., 2016)
Rice husk 40 16 26 (Daza Serna et al., 2016)
Rice straw 38.14 31.12 26.35 (Huang et al., 2016)
Barley straw 35.4 28.7 13.1 (Liu et al., 2017)
Coffee grounds 33.10 30.03 24.52 (Huang et al., 2016)
Extracted olive
pomace
19 22 40.0 (Álvarez et al., 2018)
Palm oil frond 37.32 31.89 26.05 (Khan et al., 2018)
Corn stover 43.97 28.94 21.82 (Huang et al., 2016)
Bamboo leaves 34.14 25.55 35.03 (Huang et al., 2016)
Hazel branches 30.8 15.9 19.9 (Liu et al., 2017)
16
2.4.3 Pre-treatment of lignocellulose
Pretreatment of lignocellulosic biomass is a necessary step to convert biomass into
fermentable sugars and to enable enzymatic hydrolysis to break the lignin and
hemicellulose structures and to free the buried cellulose (Sun et al., 2016).
Pretreatment steps should be simple, eco-friendly, cost-effective and economically
feasible (Ravindran et al., 2018b). In addition, the pretreatment process should not give
rise to inhibitory compounds (affecting enzymatic hydrolysis and microbial
fermentation) or loss in the fraction of interest (polysaccharide or lignin). Moreover,
to date, there is no harmonised pretreatment strategy to suit all types of lignocellulosic
biomass, and the pretreatment process depends mostly on the type of lignocellulosic
biomass and the desired products. However, the use of a combination of two or more
pretreatment strategies can significantly increase the efficiency of the process and
represents an emerging approach in this field of study (Rahmati et al., 2020).
2.4.3.1 Conventional approaches for pretreatment of lignocellulosic biomass
Generally, each of the common pretreatment approaches that fall under the four
categories of physical, chemical, physio-chemical and biological methods work
differently to break the complex structure of the lignocellulosic material. As a result,
different products and yields can be obtained from each pretreatment approach, and
each method has its advantages and disadvantages that are summarized in Table 2.2.
While some of the methods listed have successfully made the transition from research
platform to the industrial stage, significant challenges remain (Rahmati et al., 2020),
including in some cases the generation of environmentally hazardous wastes and/or
high energy inputs; there is a pressing need for green technology solutions to this
challenge (Capolupo and Faraco, 2016).
17
Table 2.2. Major advantages and disadvantages of each of the common
pretreatment methods
Pretreatment
Method
Effects Advantage Disadvantage References
Mechanical
Milling
Reduce the particle
size and
crystallinity of
lignocellulosic
materials
Control of final
particle size Make
handling of material
easy.
High energy consumption (Devendra et al.,
2015)
Extrusion Shortening of fiber
and defibrillation
operate at high solids
loadings, low
production of
inhibitory compounds,
short time
High energy
consumption, effect is
limited when no chemical
agents are used, mostly
effective on herbaceous
type biomass
(Duque et al.,
2017)
Acid Hemicellulose and
lignin fractionation
Enzymatic hydrolysis
is sometimes not
required as the acid
itself may hydrolyses
the biomass to yield
fermentable sugars
High cost of the reactors,
chemicals are corrosive
and toxic, and formation
of inhibitory by-products
(Jönsson and
Martín, 2016)
Alkaline Lignin and
hemicelluloses
removal
Cause less sugar
degradation than acid
pretreatment
Generation of inhibitors
(Zhang et al.,
2016)
Organosolv Lignin removal and
hemicellulose
fractionation
Produce low residual
lignin substrates that
reduce unwanted
adsorption of enzymes
and allows their
recycling and reuse
High capital investment,
Handling of harsh organic
solvents, formation of
inhibitors
(Nitsos and Rova,
2017)
Oxidation Removal of lignin
and hemicelluloses
Lower production of
by products
Cellulose is partly
degraded, High cost
(Chandel and Da
Silva, 2013)
Ionic liquid Cellulose
crystallinity
reduction and
partial
hemicellulose and
lignin removal
low vapor pressure
designer solvent,
working under mild
reaction conditions
Costly, complexity of
synthesis and purification,
toxicity, poor
biodegradability, and
inhibitory effects on
enzyme activity
(Yoo et al., 2017)
Liquid Hot
Water
Removal of soluble
lignin and
Hemicellulose
The residual lignin
put a negative effect
on the subsequent
enzymatic hydrolysis
High water consumption
and energy input
(Zhuang et al.,
2016)
AFEX Lignin removal High efficiency and
selectivity for reaction
with lignin
It is much less effective
for softwood, Cost of
ammonia and its
environmental concerns
(Bajpai, 2016)
SPORL Lignin removal Effective against
hardwood and
softwood, and energy
efficient
Pretreatment is preceded
by biomass size-reduction
(Noparat et al.,
2017)
18
The harsh chemicals and high conventional heating methods used for biomass
pretreatment require extensive amounts of energy and are not environmentally
friendly. Furthermore, these pretreatment strategies lead to the formation of numerous
undesirable compounds, such as aliphatic acids, vanillic acid, uronic acid, 4-
hydroxybenzoic acid, phenol, furaldehydes, cinnamaldehyde, and formaldehyde,
which may all interfere with the growth of the fermentative microorganisms during
fermentation (Ravindran and Jaiswal, 2016). This encouraged the movement from
non-sustainable conventional pretreatments (e.g. chemical and physiochemical
pretreatments) to sustainable green pretreatments (e.g. biological pretreatments).
However, long treatment times, low yields and loss of carbohydrate during
pretreatment are considered to be the major challenges in biological pretreatment by
microorganisms (Saha et al., 2016). Furthermore, pretreatment processes can cost
more than 40% of the total processing cost and represent the most energy intensive
aspects in biomass conversion to value added products (Sindhu et al., 2016). Thus, the
challenge of low efficiency production associated with green pretreatments
encouraged the investigation of using large scale technologies that are now available
on the market as scalable green solutions to achieve sustainable and efficient
pretreatment process of lignocellulosic biomass.
2.4.3.2 Green approaches for pretreatment of lignocellulosic biomass
In recent years, the concept of “Green Chemistry” has gained increasing interest as a
possible approach to the challenge of developing a viable biorefinery concept. Central
to achieving this goal is the development of technology that uses raw materials more
efficiently, eliminates waste and avoids the use of toxic and hazardous materials.
Selected green methods currently being pursued for pretreatment of lignocellulosic
biomass are summarised in Table 2.3.
19
Table 2.3. Major advantages and disadvantages of selected green chemistry
pretreatment methods.
Pretreatment Methods
Effects Advantage Disadvantage References
Deep eutectic solvents
lignin removal and hemicellulose fractionation
Green solvent, biodegradable and biocompatible
Poor Stability under higher pretreatment temperatures,
(Zhang et al., 2016)
Steam Explosion lignin softening, particle size reduction
low capital investment, moderate energy requirements and low environmental impacts
It is much less effective for softwood
(Pielhop et al., 2016)
Supercritical fluids Cellulose crystallinity reduction and lignin removal
Green solvent is used, it does not cause degradation of sugars, method is suitable for mobile biomass processor
Total utilities costs are high
(Daza Serna et al., 2016)
Microbes Lignin and hemicellulose degradation
Environment friendly, selective degradation of lignin and hemicelluloses
Very long pretreatment time (several weeks) due to slow yield
(Sun et al., 2016)
Although these green methods are environmentally friendly, problems exist regarding
high production costs and poor efficiency, as well as lack of availability of commercial
equipment suited to industrial scale processing. However, the more widespread
adoption of such technology by the food industry, with anticipated decreases in initial
capital cost and increased scale of operation, may encourage uptake for pretreatment
of lignocellulosic biomass.
20
2.4.3.3 Emerging technologies for pretreatment of Lignocellulosic biomass
Chemical approaches conducted under extreme or non-classical conditions are
currently a dynamically developing area in minimal food processing. Microwaves, and
ultrasound are non-thermal food processing technologies that also being investigated
for pretreatment of lignocellulosic biomass (Aguilar-Reynosa et al., 2017; Subhedar
and Gogate, 2016).
2.4.3.3.1 Microwave Irradiation
Microwaves are an electromagnetic radiation with wavelengths ranging from 1 mm to
1 m. They are located between 300 and 300,000 MHz on the electromagnetic spectrum
and are a nonionizing radiation that transfers energy selectively to different substances
(Huang et al., 2016a). Microwaves have attracted renewed interest since the 1980s,
when Gedye et al. (1986) reported the increase of hydrolysis, oxidation, alkylation,
and esterification processes by energy efficient microwave heating. Researchers have
reported good lignocellulosic pretreatment performance using microwave radiation
over the past 30 years and have gradually moved from laboratory to pilot scale (Li et
al., 2016). Currently, microwave-assisted pretreatment technologies of lignocellulosic
biomass can be classified into two main groups: (a) Microwave-assisted solvolysis
under mild temperatures (<200 °C) that depolymerises the biomass to produce value-
added chemicals, and (b) Microwave-assisted pyrolysis of lignin without oxygen,
under high temperatures (>400 °C) to convert biomass to bio-oil or biogases. Each of
the two groups of technologies might be accomplished with catalysts. However,
microwave-assisted pyrolysis is favored, largely due to energy shortages and the
sustainability plans of most countries.
Compared with conventional heating, microwave radiation has significant advantages
such as: (a) fast processing rate, short reaction time, (b) selectivity and uniform
21
volumetric heating performance (c) easy operation and energy efficiency and (d) low
degradation or formation of side products. In addition, microwave hydrothermal
pretreatment removes more acetyl groups in hemicellulose, which may be raised from
the hot spot effect of microwave irradiation (Dai et al., 2017).
In the case of conventional heating, energy is transferred from the outside surface of
the material inwards to the core of the material by conduction. Thus, overheating can
occur on the outside surface whilst still maintaining a cooler inner region. Contrasting
with this, microwaves induce heat at the molecular level by direct conversion of the
electromagnetic energy into heat. Energy is therefore uniformly dissipated throughout
the material.
Materials can be grouped into three categories according to their response to
microwaves: insulators, absorbers, and conductors. Insulators are materials which are
transparent to microwaves (e.g., glass and ceramics), conductors are materials which
show high conductivity and thus reflect microwaves from the surface (e.g., metals),
while absorbers or dielectrics are materials that can absorb microwaves and convert
microwave energy into heat (Huang et al., 2016b). Most biomass is generally
considered as low lossy materials, and they need to be supported with materials that
achieve rapid heating, such as graphite, charcoal, activated carbons and pyrites.
Interestingly, Salema et al. (2017) studied the dielectric properties of different biomass
from agriculture and wood-based industries (including oil palm shell, empty fruit
bunch, coconut shell, rice husk, and sawdust) and reported that all were low loss
dielectric materials. Such materials do not absorb microwaves well during microwave-
assisted pyrolysis until the char is formed, and the microwave absorption will then be
significantly higher.
22
In conventional heating methods, the lignocellulosic biomass is ground into small
particles to prevent large temperature gradients and then heated by indirect heat
conduction or high-pressure steam injection up to 160–250 °C. Therefore, fermentable
sugar recovery and conversion might be affected by degradation of the hemicellulose
into furfural or humic acids (Li et al., 2016). Alternatively, microwave heating is
reported to enhance enzymatic saccharification through fibre swelling and
fragmentation (Diaz et al., 2015) because of the internal uniform and rapid heating of
large biomass particles. Almost no effect is observed in plant fibre material when
treated with microwaves under temperatures that are equal to or below 100 °C (Chen
et al., 2017).
The performance of microwaves depends on the dielectric properties of biomass which
represent the ability of the material to store electromagnetic energy and to convert this
energy into heat. Although, biomass usually is a low microwave absorber, the presence
of relatively high moisture and inorganic substances can improve the absorption
capacity of biomass (Li et al., 2016). The increasing commercial availability of flow-
through microwave systems may be of relevance to lignocellulosic pretreatment.
Choudhary et al. (2012) evaluated the pretreatment of sweet sorghum bagasse (SSB)
biomass through microwave radiation and reported that about 65% of maximal total
sugars were recovered when 1 g of SSB in 10 ml water was subjected to 1000 W for 4
minutes. Scanning electron microscope analysis of microwave-assisted pretreatment
of corn straw and rice husk in alkaline glycerol showed clear disruption of the plant
cell structure (Diaz et al., 2015). Recently, Ravindran et al. (2018b) reported that
microwave-assisted alkali pretreatment was the best pretreatment method for brewers’
spent grain (1g of BSG in 10 ml 0.5% NaOH was pretreated using 400 W for 60
seconds), as compared with dilute acid hydrolysis, steam explosion, ammonia fiber
23
explosion, organosolv and ferric chloride pretreatment. The authors found that BSG
after microwave-assisted alkali pretreatment yielded 228.25 mg of reducing sugar/g of
BSG which was 2.86-fold higher compared to untreated BSG (79.67 mg/g of BSG).
Others have also found that microwave radiation for lignocellulosic pretreatment
possesses the advantage of low capital cost, easy operation and significant energy
efficiency (Kostas et al., 2017).
2.4.3.3.2 Ultrasound
Over 90 years ago, Wood and Loomis (1927) reported the effects of the ultrasonic
treatment on cellular biomass, such as floc fragmentation, cell rupture and destruction.
Ultrasound in the range of 20 kHz to 1 MHz is used in chemical processing, while
higher frequencies are used in medical and diagnostic applications. Ultrasound
pretreatment of biomass results in alteration of the surface structure and production of
oxidizing radicals that chemically attack the lignocellulosic matrix (Luo et al., 2013).
Additionally, ultrasound can disrupt α-O-4 and β-O-4 linkages in lignin (Shirkavand
et al., 2016) which results in the splitting of structural polysaccharides and lignin
fractions by formation of small cavitation bubbles (Kumar and Sharma, 2017). The
bubbles formed grow to a certain critical size and then become unstable, collapsing
violently, and achieving pressures up to 1,800 atmospheres and temperatures of 2,000–
5,000 K (Kunaver et al., 2012). Hence, ultrasonic disruption may represent an effective
green technology for the pretreatment of lignocellulosic biomass.
Kunaver et al. (2012) studied the utilization of forest wood wastes to produce valuable
chemicals using high energy ultrasound at a power of 400 W and adjustable power
output ranging from 20% to 100% and reported shorter reaction times (by a factor of
up to nine). Sun et al. (2004) reported that ultrasound irradiated sugarcane bagasse
achieved 90% hemicellulose and lignin removal at an ultrasound power of 100 W and
24
sonication time for 2 hours in distilled water at 55° C. The ultrasound was found to
attack the integrity of cell walls, cleaving the ether linkages between lignin and
hemicelluloses, and increasing the accessibility and extractability of the
hemicelluloses. This is in agreement with another study for ultrasound-assisted
alkaline pretreatment of sugarcane bagasse (1 g/ 25ml) using 400 W microwave power
for 47.42 minutes in 2.89% NaOH and 70.15° C, where the theoretical reducing sugar
yield recovered was about 92% (Velmurugan and Muthukumar, 2012).
Ultrasound-assisted, alkali pretreatment can enhance lignin degradation and enzymatic
saccharification rates by breaking hydrogen bonds between molecules of
lignocellulosics and lowering its crystallinity. However, the ultrasonic vibration
energy is too low to change the surface conformation of the raw material biomass
particles (Zhang et al., 2008). Subhedar et al. (2017) recently investigated the
ultrasound-assisted delignification and enzymatic hydrolysis of three biomass types
(groundnut shells, pistachio shell, and coconut coir) and reported an approximate 80–
100% increase in delignification over conventional alkali treatments, where biomass
loading was 0.5%, ultrasound power was 100 W and duty cycle was 80% for 70
minutes. Additionally, reducing sugar yields in the case of ultrasound-assisted
enzymatic hydrolysis under optimised conditions of enzyme loading at 0.08% w/v,
substrate loading at 3.0% w/v, ultrasound power of 60 W and duty cycle of 70% for
6.5h, were 21.3, 18.4 and 23.9 g/L for groundnut shells, pistachio shells, and coconut
coir respectively, significantly more than that found for alkali hydrolysis (10.2, 8.1 and
12.1 g/L).
It was also reported that reducing sugar yield was increased by a factor of
approximately 2.4 by the application of ultrasound at a power of 60 W and duty cycle
25
of 70 % for pretreatment of lignocellulosic wastepaper at substrate loading of 3.0%
(w/v) and cellulase loading of 0.8% (w/v) for 6.5 hours (Subhedar et al., 2015).
Moreover, acoustic cavitation was found to successfully decrease the crystallinity of
the microcrystalline cellulose, enabling enhanced enzymatic digestibility (Madison et
al., 2017).
Combining ultrasound with ammonia pre-treatment of sugarcane bagasse (sonication
time of 45 minutes in 400 w power, 100% amplitude and 24 kHz frequency, biomass
loading of 1 g per 10 ml of 10% ammonia and temperature of 80° C) resulted in a
cellulose recovery of 95.78%, with 58.14% delignification (Ramadoss and
Muthukumar, 2014). Additionally, the synergistic effect of combining ammonia with
ultrasound reduced by-product formation, enabled the treatment to be conducted at
moderate temperature and reduced cellulose crystallinity. This is with an agreement
with recent work carried out on ultrasound-assisted dilute aqueous ammonia (2.0%
w/v aqueous ammonia) pretreatment of corn cob, corn stover and sorghum stalk using
ultrasound at 90 W power and 50 kHz frequency (Xu et al., 2017); the highest
enzymatic hydrolysis sugar yield was approximately 81% in corn cob (70° C, 4h), 66%
in corn stover (60° C, 2 h) and 57% in sorghum stalk (50° C, 4 h). Similarly,
pretreatment of spent coffee waste by ultrasound assisted potassium permanganate
(biomass loading of 1.0 g at 10 ml of 4% KMnO4 for 20 minutes, ultrasonic frequency
of 47 kHz and power of 310 W) resulted in 98% cellulose recovery and 46% lignin
removal (Ravindran et al., 2017).
2.4.3.3.3 Techno-economic feasibility
Equipment based on emerging technologies are available in the market and are used
mainly in food processing industry. Example of these equipment includes: continuous
26
flow microwaves (Advanced Microwave Technologies, United Kingdom), and
ultrasonic processors (Industrial Sonomechanics, United States).
Microwave use in chemical processing has been shown to be a technically and
economically feasible alternative to conventional heating. Hasna (2011) evaluated the
cost-benefit of using microwave drying in corrugated paperboard manufacturing as an
alternative to conventional steam platens. It was concluded that the microwave capital
cost ($7000 per kW) could be off-set against utilities and power savings (from $128.00
to $38.00 per hour), compared with conventional steam platens. Such savings were
achievable in less than one year with an assumption that operation hours are 6000 per
year. The author also reported additional benefits from using microwave drying in
corrugated paperboard manufacturing, such as improved quality, reduced wastage, and
minimum starch consumption. In a recent feasibility study on ginger processing to
oleoresin, an ultrasound pretreatment step was introduced as a novel method to
enhance extraction of chemical constituents from plant materials (Romis Consultants
Ltd, 2017); however, the study did not focus on economics related to ultrasound
specifically. Generally, the economic feasibility of emerging technologies is limited
by the high cost of capital investment for new equipment. For commercial application
of the emerging technologies in pretreatment of lignocellulosic biomass further
feasibility studies will be needed considering the complexities of biorefining process,
inter-dependence of pretreatment processes and the economics related to the market of
the finished product.
27
2.4.4 Agro-industry wastes as fermentation media
Recent studies involving enzyme production using agro-industry wastes have
primarily investigated the effect of pretreatment measures in influencing the enzymes
yield. An interesting study conducted by Salim et al. (2017) deployed a new strain of
Bacillus to study the production of four enzymes (α-amylase, protease, cellulase and
pectinase) using different agricultural residues as growth substrates (soybean meal,
sunflower meal, maize bran, maize pericarp, olive oil cake and wheat bran). Each
residue was subjected to different pretreatment measures (acid/alkali, ultrasound and
microwave treatments) to determine their effect on enzyme production. Chemical
pretreatments were found to be superior to other techniques, with highest yields of
protease and amylase achieved using alkali -treated corn pericarp. Another study
conducted by Leite et al. (2016) studied the application of ultrasonication as a potential
pretreatment for olive pomace to improve enzyme yield using solid state fermentation.
They reported a 3-fold increase in xylanase activity and a 1.2-fold increase in cellulase
activity employing Aspergillus niger as the producer microbe.
While media formulations based on agricultural wastes are heterogeneous by nature,
studies have been conducted on optimization to improve yield. Gustavo et al. (2017)
conducted an extensive study to optimize a solid-state fermentation process involving
different agro-industrial wastes and Rhizopus microsporus var. oligosporus as the
producer microbe. The application of four lignocellulosic substrates viz. wheat bran,
wheat flour type II, soybean meal and sugarcane bagasse were assessed in their study
as potential carbon sources for amylase production. Their findings suggested that the
best individual substrate for amylase production was wheat bran.
28
Along with advances in fermentation technologies, studies have been conducted at lab-
pilot scales employing different bacterial and fungal strains to determine the efficacy
of enzyme production using lignocellulosic biomass (Pandey, 2003). Several studies
have shown a rejuvenated interest in solid state fermentation for the production of
various enzymes. Many of the attributes of agro-industrial residues are especially
suited to this form of culture and can yield significant benefits in terms of
sustainability. The latter include environmental friendliness and significant reduction
in production costs.
2.4.4.1 Production of Xylanase
Xylanases (E. C. 3.2.1.8, 1, 4-β-xylanxylanohydrolase) are enzymes that break down
xylan which is an integral part of plant polysaccharide. Xylan is a complex
polysaccharide made of a xylose-residue backbone, with each subunit linked by a β-1,
4-glycosidic bond. The xylan backbone can be branched with D-glucuronic acid or D-
arabinofuranoside (Harris and Ramalingam, 2010). Xylanases are produced by several
bacterial and fungal species. Filamentous fungi that synthesise this enzyme are of
particular interest because they secrete it into the media in large quantities in
comparison to bacteria (Knob et al., 2013). Xylan, being a complex polysaccharide,
requires a consortium of enzymes for total hydrolysis. This enzyme system consists of
endoxylanases, β-xylosidases, ferulic acid esterase, p-coumaric acid esterase,
acetylxylan esterase and α-glucuronidase. Endoxylanases and β-xylosidases are the
most extensively studied components of this system (Polizeli et al., 2005). Xylanases
have wide applications in industries as diverse as food, biomedical, animal feed and
bioethanol (Harris and Ramalingam, 2010; Das et al., 2012; Goswami and Pathak,
2013).
29
The suitability of lignocellulosic food waste as a carbon source for the production of
commercial xylanases has been studied by many researchers. Lowe et al. (1987)
investigated wheat straw as a viable carbon source for the production of a xylanase
derived from an anaerobic rumen fungus and achieved high levels of activity (0.507
IU/ml). A. niger and A. terreus strains were employed on moistened wheat bran by
Gawande and Kamat (1999) to attain 74.5 IU ml-1 of xylanase activity. In another
study, grape pomace was used as the substrate for the production of xylanase using A.
awamori as the enzyme producer (Botella et al., 2007). Highest xylanase activity (40
U/g) was achieved after 24h of incubation time. Addition of 6% glucose as an
additional carbon source increased xylanase production significantly. Apple pomace,
melon peel and hazelnut shell were used by Seyis and Aksoz (2005) for xylanase
production from Trichoderma harzianum 1073 D3, achieving a maximum activity of
26.5 U/mg .
It is worth noting that Hg2+ might has an inhibitory effect on xylanase activity
regardless the source of the enzyme. Da Silva et al. (2015) reported that the ions
concentration of Hg2+ (2 mM), and Cu2+ (10 mM) were inhibitors for activity of
xylanase from Trichoderma inhamatum. Another research (Guan et al., 2016) found
that Hg2+ (2 mM), and Cu2+ (10 mM) inhibited the activity of xylanase from
Cladosporium oxysporum., while Mg2+ enhanced the xylanase activity by 2%.
Similarly, Khasin et al. (1993) reported that Zn2+, Cd2+, and Hg2+ were strong
inhibitors for xylanase from Bacillus stearothermophilus T-6, while authors reported
that the enzyme has no apparent requirement for cofactors.
30
2.4.4.2 Production of Pectinase
Pectinases are a class of enzymes that catalyse the disintegration of pectin-containing
compounds. Pectin compounds are an integral part of the plant cell wall. Pectinases
can be classified into two groups according to their mode of action. Pectin esterases
catalyse the de-esterification of methyl groups found in pectin to produce pectic acid.
Pectin depolymerase enzymes cleave the glycosidic bonds found in pectic acid to
release various simpler compounds based on their mode of enzyme action.
Protopectinases solubilize protopectin into highly polymerized forms of soluble pectin
(Sakai et al., 1993). Pectinases are used in the fruit juice industry and wine making for
clarification and removal of turbidity in the finished product. They also intensify the
color in the fruit extract, while aiding in stabilization and filtration (Servili et al., 1992).
Many agricultural residue varieties have been tried-and-tested for the production of
pectinases using different microbial species. Apple and grape pomace have been
extensively used in studies to determine their efficacy as suitable substrates for
pectinase production (Botella et al., 2007; Hours et al., 1988). A novel approach was
devised by Kashyap et al. (2003) in which they supplemented the fermentation media
with Neurobion® tablets (a multi-vitamin) and polygalacturonic acid, while producing
pectinase using wheat bran as the carbon source. Maximum enzyme activity was
reported to be 8,050 U/g dry substrate. A mixture of orange bagasse and wheat bran in
the ratio (1:1) was employed as media for pectinase production using the filamentous
fungus Penicillium viridicatum RFC3 and yielded highest enzyme activities of 0.7 and
8.33 U mL−1 for endo- and exo-polygalacturonase and 100 U mL−1 for pectin lyase
on performing the fermentation process in polypropylene packs (Silva et al., 2005).
Seedless sunflower heads and barley spent grain are lignocellulosic food processing
31
wastes have also been studied as potential media additives for pectinase production
(Almeida et al., 2003; Patil and Dayanand, 2006).
Pectinases have also been produced using a mixture of wastes. Biz et al. (2016)
developed an ingenious method to produce pectinases employing citrus peel and sugar
cane bagasse in solid state fermentation mode while avoiding overheating problems.
A pilot-scale packed bed reactor was used for this purpose. A uniform pectinase
activity of 34-41 U/ml was obtained throughout the bed. A combination of citrus peel
and bagasse ensured temperature control while avoiding problems such a bed
shrinkage and agglomerate formation since sugarcane bagasse is highly porous in
nature. Aspergillus oryzae was used as the fermentative microbe.
It is noteworthy that published studies show that metal ions have a different impact on
pectinase from different microorganisms. Anggraini et al. (2013) reported that the ions
concentration of Zn2+ (4 mM), Mg2+ (4 mM), and Ca2+ (6 mM) were inhibitor for
activity of pectinase from Aspergillus niger . Recently, Xu et al. (2020) found that
Ca2+, Cu2+, and Mn2+ inhibited the activity of pectinase from Aspergillus nidulans by
14.8%, 12.8%, and 10.2% separately. On the contrary, Hassan et al. (2013) reported
that Ca2+ and Mn2+ were the preferential cofactor of two pectinase enzymes belong to
different families of pectinase produced by Dickeya dadantii. However, Dey and
Banerjee (2014) found that there was no significant effect of 10 mM CaCl2 on the
activity of pectinase produced by Aspergillus awamori Nakazawa MTCC 6652.
2.5 Immobilisation of enzymes
Enzymes once suspended in an aqueous reaction medium are almost impossible to
retrieve or recycle. Additionally, due to their proteinaceous nature, enzymes are highly
unstable and require an aqueous environment to catalyse reactions (Joseph et al.,
32
2008). Enzyme immobilisation is the process of attaching an enzyme molecule to a
solid support with intentions for its reuse, and with the potential of minimizing loss in
their catalytic activity, thereby dramatically improving process economics. Although
theoretically promising, the practical experiences of enzyme immobilisation have not
always lived up to expectations.
2.5.1 Carrier materials
Carrier materials can be classified into organic and inorganic according to their
chemical composition. Organic enzyme carrier materials can be categorized further as
being natural and synthetic, according to their mode of origin. The choice of carrier
material for a particular enzyme molecule is often a question of trial-and-error but can
be improved by possessing a strategic knowledge of the surface properties and
functional groups borne on the enzyme. The same can be said about the choice of
immobilisation method.
The matrix chosen for enzyme immobilisation should ideally be affordable and eco-
friendly, as well as easily available. It should be inert to any chemical reactions and
should not interfere with the enzymatic process. It should be stable over a wide
temperature and pH range ,while protecting the attached enzyme. A carrier material
that can accommodate a large amount of enzyme is highly desirable. As is obvious,
all these properties are usually not observed in any single type of enzyme
immobilisation matrix. Table 2.4 represents some of the common matrices used for the
immobilisation of different enzymes.
33
Table 2.4 Enzyme support materials and their properties
Carrier type Carrier material Properties Enzyme Reference
Organic
(Natural)
Poly(3-
hydroxybutyrate-co-
hydroxyvalerate)
Biocompatibility, biodegradability,
strength, easy reabsorption, non-
toxicity, eco-friendliness
Candida rugosa
lipase
(Cabrera-
Padilla et al.,
2012)
Chitosan Inexpensive, natural
polyaminosaccharide, non-toxic,
biocompatible and biodegradable
β-Galactosidase (Biró et al.,
2008)
Collagen Formation of the porous structure,
permeable, hydrophilic and
biocompatibile
Alkaline
phosphatase
(Hanachi et
al., 2015)
Organic
(Synthetic)
Paper-based carriers Inexpensive, non-toxic,
biocompatible and biodegradable,
abundantly available
Glucose oxidase
biosensor
(Nery and
Kubota,
2016)
Inorganic
Zeolite Molecular sieves, microporous,
crystalline
Lysosyme,
papain, trypsin
(Díaz and
Balkus,
1996; Xing
et al., 2000)
Hydrotalcite Anion exchange properties Lipozyme-TL
IM
(Yagiz et al.,
2007)
Mesoporous silica Highly porous, adsorption,
immobilization influenced by
electrostatic interaction
Lysosyme (Carlsson et
al., 2014)
Gold nanoparticles Controllable particle size,
structure, non-toxic,
hydrophobic/hydrophilic and
biocompatible
Trypsin (Kalska-
Szostko et
al., 2012)
Organic–
inorganic
hybrid
Alginate/silica
biocomposites
Inorganic porous network, control
over diffusion properties
β-galactosidase (Coradin and
Livage,
2003)
The boom of nanotechnology has encouraged an increasing interest in magnetic
nanoparticles as supports for enzymes (Bilal etal., 2018; Bilal etal., 2019; Tavares et
al., 2015), due to their small size and large surface area. Furthermore, conventional
support materials can be coupled with magnetic nanoparticles to facilitate easier
product separation (Woo et al., 2015). However, biocompatibility problems may be
34
an issue when dealing with magnetic nanoparticles, and food applications have
largely favoured the use of natural polymers for enzyme immobilisation. In this
regard, alginate hydrogel is a popular polymer of natural polysaccharides that is widely
used as a simple and inexpensive carrier for enzymes. However, a problem faced with
this material is the leakage of enzyme in an aqueous media which in turn reduce the
activity of immobilized enzymes. This can be greatly reduced by addition of cross-
linking agents (of which the most common is glutaraldehyde and genipin) prior to
immobilisation (Tsai and Meyer, 2014).
2.5.1.1 Magnetic nanoparticles
Superparamagnetic nanoparticles have a size range of 10–20 nm and possess a high
magnetization value, resulting in an excellent separation efficacy when used in
enzyme immobilization ptocesses. Besides facile separation of superparamagnetic
nanoparticles using external magnetic fields, they have very large surface area-to-
volume ratios that increases the reactivity at the molecular level via enhanced enzyme
loading (Bilal et al., 2018). Therefore, magnetic nanoparticles are understandably
attracting substantial attention as promising enzyme carriers.
Consequently, a myriad of publications featuring laboratory-scale studies for
immobilisation of enzymes on magnetic nanoparticles have been published in recent
decades (Bilal et al., 2018; Bilal et al., 2019; Furtado et al., 2018; Seenuvasan et al.,
2018). Apart from the millilitre-scale bench studies, proof-of-concept pilot studies at
liter scale were attempted to explore a realistic enzymatic reactor with a magnetic
separation system that enables the recovery of enzymes immobilised on magnetic
nanoparticles. Such reactors were investigated at volumes of 1 L (Lindner et al., 2013),
20 L (Alftrén, 2014; Zlateski et al., 2014), and 100 L (Moldes-Diz et al., 2018). These
35
studies suggest that a separation efficiency of 99% is possible for the separation of
magnetic particles from reactors in a volume range of 1.0–100 L (Lindner et al.,2013;
Moldes-Diz et al., 2018). However, large scale manufacturers would definitely require
reactors at larger capacities that are not available nowadays.
Commercial availability of industrial reactor with magnetic separation system is not
the only challenge facing implementation of magnetically retrievable enzymes in food
manufacturing facilities. In fact, safety of consumers and consumer’s acceptance of
nano-enabled products are key challenges that need to be overcome by stakeholders
(enzyme producers, food manufacturers, and regulatory authorities).
A recent review of the literature on the toxic effects of magnetic nanoparticles both in
vitro (cell lines) and in vivo (Invertebrates/Vertebrates) by Malhotra et al. (2020)
found that magnetic nanoparticles tend to degrade in the body. The aforementioned
finding suggests that there is a need to understand not only the potential toxicity of the
nanoparticles but also the potential toxicity of the degraded products. Cellular uptake
of magnetic nanoparticles and their subsequent degradation can disturb the
intracellular levels of iron which in turn can influence cellular iron metabolism and
leading to the overproduction of reactive oxygen species (Mulens-Arias et al., 2020,
Fu et al., 2014). On the other hand, another review paper found no potential toxicity
associated with the oral administration of iron oxide nanoparticles in experimental
animals when surveyed published studies (McClements and Xiao, 2017). Hence, at
this juncture, predictions for the future of the magnetic nanoparticles in food sector
will carry a degree of uncertainty. It is worth mentioning that consumers may be
exposed to iron oxide nanoparticles unintentionally through the consumption of the
food pigment E172 as there are no specifications concerning the fraction of
nanoparticles in these pigments (Voss et al., 2020).
36
2.5.1.2 Alginate beads
Alginates are commercially available as sodium alginate from brown seaweeds,
including Laminaria hyperborea, Laminaria digitata, and Macrocystis pyrifera
(Abhilash and Thomas, 2017). Alginate can also be extracellularly produced by
bacteria, including Azotobacter vinelandii and Pseudomonas spp. (Urtuvia et al.,
2017).
Alginate is an ingredient used by the food industry as stabilizers, thickeners, and
emulsifiers (Brownlee et al., 2009); and it is sold in bulk for food applications at prices
as low as $5 kg-1 (Hay et al., 2013). Hence, alginate beads are widely studied as an
accessible, non-toxic, and inexpensive matrix for entrapment of industrially important
enzymes. However, enzyme entrapment can have significant drawbacks, including
enzyme leakage and substrate-product diffusion limitations (Liu, 2017). To overcome
these drawbacks, the enzymes can be bound to alginate covalently using a cross-linker
such as glutaraldehyde (Barbosa et al., 2014). In addition to glutaraldehyde, genipin
from the fruits of Gardenia jasminoides Ellis has been investigated as an efficient and
biocompatible crosslinker, and when combined with alginate provides a ‘food-
friendly’ matrix for enzyme immobilization (Tacias-Pascacio et al., 2019).
2.5.2 Enzyme Immobilization methods
The choice of best immobilisation method for a particular enzyme should be
determined based on certain characters that are specific to both enzyme and carrier
material. Generally, two methods are used for enzyme immobilization, namely:
entrapment and support binding. The latter can be physical (e.g. adsorption) or
chemical (e.g. covalent binding, and cross linking).
37
Neither physical adsorption nor entrapment involve strong bonding the enzyme to
the carrier material. Hence, adsorption and entrapment immobilisation methods share
the same disadvantage of leaching of the enzyme from the carrier material in an
aqueous reaction mixture (Freitas et al., 2012; Homaei et al., 2013). In the case of
adsorption immobilisation, bonding strength is weak, while the method of entrapment
immobilisation does not involve chemically bonding the enzyme to the carrier
material. Entrapment can be described as the engulfing of the enzyme inside a matrix,
such as within a gel or fibre. Hence, the diffusion of a macromolecular substrate to the
enzyme may present a serious limitation in the case of small matrix pore size.
Covalent binding basically fixes the enzyme molecule onto the carrier on a permanent
basis through strong covalent bonds. This minimizes the problem of leaching of the
enzyme found in adsorption-based biocatalysts when suspended in reaction mixtures.
However, owing to the strong covalent binding of the enzyme to the carrier material,
the conformation (shape) of the enzyme might be altered, resulting in a major loss of
activity (Costa et al., 2005).
Cross linking of enzymes is another innovative method to immobilise enzymes without
the help of a carrier material. Here, every enzyme molecule acts as a carrier for each
other. Cross linked enzyme aggregates (CLEAS) eliminate some of the disadvantages
associated with carrier materials and have been reported to possess very high loading
rate. However, CLEAs tend to have several disadvantages compared to other
immobilisation strategies. They are not mechanically strong and depending on the
process may not be deemed fit for industrial application (Cui et al., 2015).
38
2.5.3 Application of Immobilised Enzymes in Food and Beverage Industry
Apart from the pharmaceutical industry and some chemical industries, the food sector
is targeting production of cost sensitive products in large quantities and tends to use
packed bed column reactors rather than stirred batch reactors (Basso and Serban,
2019). Hence, continuous flow operations are typically preferred over batchwise
processing mode, whenever applicable. A successful example of immobilized
enzymes used at industrial scale in continuous process is the use of D-glucose/xylose
isomerase for production of high fructose corn syrup. In fact, immobilized glucose
isomerase was a game changer for the market of industrial enzymes, accounting for
20% of the total industrial enzyme sales in 1990 (Robinson, 2015). Alongside glucose
isomerase, other immobilised enzymes such as β-galactosidase, and lipase have been
successfully commercialized. Despite the steady growth that industrial enzymes
witnessed since 2000, few new immobilized enzymes have been successfully
introduced.
2.6 Enzymatic treatments in fruit juice production
Depectinization is an essential part of fruit juice production to increase the efficacy of
fruit pulp extraction, and juice clarification (Dey and Banerjee, 2014). However,
different enzymatic treatments are required for each fruit juice according to consumer
preferences. For instance, intensive depectinization is required for clarifying juices
from apples, berries, or grapes to achieve complete juice clarification. On the other
hand, only mild enzymatic treatment is enough to improve fruit pulp extraction while
maintaining cloudy juices from orange and pineapple, which are considered more
desirable consumer products. Commercial enzymes are currently used in fruit juice
production as enzymatic cocktails (Dal Magro et al., 2020). In combination with
pectinase, cellulase is usually used in enzymatic liquefaction and fruit pulp extraction
39
prior to juice clarification (Sharma et al., 2017; Will et al., 2000). Moreover,
destarching is also essential when unripe starch-rich fruits (e.g. apples) are used for
juice processing (Dey and Banerjee, 2014). Hence, α-amylase is required to eliminate
the additional haziness of juice due to the presence of starch.
Fruits such as apples, and pineapples contain xylan‐rich hemicellulose, and hence the
treatment of the corresponding juices with xylanase is also needed for juice clarity
(Bhardwaj et al., 2019). In the case of citrus fruits (e.g. grapefruit), debittering by
naringinase is required to remove the perceptible bitter taste to consumers
(Bodakowska-Boczniewicz and Garncarek, 2019). Glucose oxidase may be used for
preventing changes in color and taste during storage of fruit juice (Fernandes, 2018).
To the best of the author's knowledge, both powder and liquid commercial
preparations of juice extraction and clarification enzymes (e.g. pectinase, xylanase)
are based on free enzymes, and immobilized forms have only been explored in
laboratory scale studies. The food industry segment dominates the market of industrial
enzymes by 35% of total market share (major producers being Novozymes and
Dupont) and is expected to expand at a compounded annual growth rate (CAGR) of
more than 6% between 2020 and 2025 (Research and Markets, 2020).
40
Chapter 3
Materials & Methods
This section of the thesis gives a comprehensive account of the scientific procedures
and materials utilized for accomplishing the experimental work.
41
3.1 General
3.1.1 Commercial sources of common reagents
▪ Agar: Fluka
▪ Bovine serum albumin: Sigma-
Aldrich
▪ Calcium Chloride: Sigma-Aldrich
▪ Cellulase from Trichoderma
reesei: Sigma-Aldrich
▪ Citric acid: Sigma-Aldrich
▪ Congo red: Sigma-Aldrich
▪ Coomassie G-250: Sigma-Aldrich
▪ D(+)-Glucose: Sigma-Aldrich
▪ D(+)-Xylose: Fluka
▪ D -Galacturonic acid: Sigma-
Aldrich
▪ Dinitrosalicylic acid: Sigma
Aldrich
▪ Genipin: Sigma Aldrich
▪ Glutaraldehyde: Sigma Aldrich
▪ Hemicellulase from Aspergillus
niger: Sigma Aldrich
▪ Iron(II) chloride: Sigma-Aldrich
▪ Iron(III) chloride: Sigma-Aldrich
▪ Lactophenol blue solution:
Sigma-Aldrich
▪ Pectin from citrus peel: Sigma-
Aldrich
▪ Potato Dextrose Agar: Sigma
Aldrich
▪ Rochelle salt: Sigma Aldrich
▪ Sodium Acetate: Sigma-Aldrich
▪ Sodium Alginate: BDH
Chemicals
▪ Sodium citrate: Scharlau Chemie
▪ Sodium Hydroxide: Sigma-
Aldrich
▪ Xylan from beech wood: Sigma-
Aldrich
All reagents and chemicals used for the study were of analytical grade.
3.1.2 Instruments
▪ Field Emission Scanning Electron Microscopy (Hitachi SU-70, Japan).
▪ Perkin Elmer Spectrum GX FT-IR (UATR) Microscope, USA.
42
▪ Siemens’s BN D-500 X-ray diffractometer, Germany.
▪ Microwave oven: Sharp, Model R 244; Sharp electronics, UK.
▪ Ultrasonic bath: Transonic TI-H-10, Fisher Bio-block Scientific, UK.
▪ UV-1800 UV-VIS spectrophotometer (Shimadzu Scientific Instruments, USA).
▪ Quava Mini Ultrasonic System: Kaijo, USA.
▪ X-Max silicon drift detector (Oxford Instruments, UK).
3.1.3 Software
▪ Microsoft Office: Microsoft, Inc., USA.
▪ NeuralPower® (CPC-X Software, version 2.5, USA).
▪ Origin Pro 8 software (Microcal Software Inc., USA).
▪ STATGRAPHICS Centurion XV: StatPoint Technologies, Inc., Warrenton, VA.
3.1.4 Analytical Techniques
3.1.4.1 Compositional analysis
Compositional analysis of the pretreated and native lignocellulose samples were
performed by two stage acid hydrolysis according to National Renewable Energy
Laboratory (NREL) protocol (Sluiter et al., 2008). The samples were subjected to acid
hydrolysis using 72% H2SO4 at 30°C for 60 min. The mixture was then diluted to 4%
H2SO4 concentration by adding deionised water and autoclaved for 60 min. The solids
were filtered using a filtration crucible and dried at 105°C for 48h to remove all the
moisture content or until constant weight was achieved. The dried solids were then
burned in a blast furnace for 24h at 595°C to obtain acid insoluble lignin. The acid
soluble lignin content in the liquid was determined using spectrophotometry at 205
nm.
43
3.1.4.2 Reducing sugar analysis
The reducing sugar concentration in the hydrolysate was estimated by the
dinitrosalicylic acid (DNS) method (Miller, 1959). Briefly, each hydrolysate (1 mL)
was diluted 1:10 in dH2O before addition of dinitrosalicylic acid reagent (1 mL) and
dH2O (2 mL). The mixture was submerged in a water bath for 5 min at 100 °C. The
volume was made up to 10 mL by the addition of dH2O (6 mL). Absorbance was
measured at 540 nm.
3.1.4.3 Enzymatic hydrolysis
To perform the hydrolysis, 1 g of BSG, 158.76 µl of cellulose (77 FPU/ml), and 153.3
µl hemicellulase (72 U/ ml) were mixed in sodium citrate buffer (0.05 M) and distilled
water at pH 5.4 (total volume 10mL) and heated to 50°C for 120 h (Ravindran et al.,
2018b). After treatment, the hydrolysate liquors were collected, and stored at 4°C for
further compositional analysis.
3.1.4.4 Assay of Total Protein
Total protein concentration was determined by the Bradford assay using the
absorbance values of the dye at 595 nm using bovine serum albumin (BSA) as the
standard (Bradford, 1976).
3.1.4.5 Assay of enzyme activity
Xylanase, and pectinase activities were assayed by determining the liberated reducing
sugars (xylose and D-galacturonic acid, respectively) resulting from the enzymatic
hydrolysis of the corresponding substrates (xylan, and pectin, respectively) using 3,5-
dinitrosalicylic acid reagent (Miller, 1959). The supernatant from the culture broth was
served as the source of the enzyme), a standard curve was graphed for each sugar
44
(xylose, and D-galacturonic acid), and the absorbance of the solution was measured at
540 nm. One unit of enzyme activity (U) was defined as the amount of enzyme that
produces 1.0 μmol of reducing sugar per ml per minute (μmol min−1) under the assay
conditions.
3.1.4.6 Fourier Transform Infra-Red Spectroscopy analysis
FTIR spectroscopy is a mid-infrared spectroscopic analysis is a rapid and non-
destructive technique for the qualitative and quantitative determination of biomass
components in the mid-infrared region. The high infrared background absorbance of
water is an obstacle when FTIR is employed in the analysis of wet, solid biomass.
However, ATR-FTIR allows attenuation of incident radiation and provides infrared
spectra without water background absorbance. Sample preparation is critical because
FTIR works well with individual components extracted from plant cell wall. FTIR
provides information about certain components in the plant cell wall through
absorbance bands.
FTIR spectroscopy studies were performed to observe any changes in the composition
as a reflection of the variations in the functional groups in pretreated BSG at the
backdrop of its raw counterpart. A Perkin Elmer Spectrum GX FT-IR (UATR)
Microscope (USA) was employed for this study The FTIR spectra was recorded from
4000 to 400 cm-1 with 32 scans at a resolution of 0.3 cm-1 in transmission mode
(Raghavi et al., 2016).
3.1.5 Molecular Identification of Fungi
Initially, the genomic DNA was extracted using PrepMan® Ultra Sample
Preparation Reagent following the instructions of the manufacturer (Thermo Fisher
Scientific Inc., Waltham, MA, USA). The internal transcribed spacer (ITS) regions of
45
ribosomal DNA were then amplified using universal primers: ITS1 (forward, 5’-
CTTGGTCATTTAGAGGAAGTAA-3’), ITS4 (reverse, 5’-TCCTCCGCTTA-
TTGATATGC-3’), and MyTaq™ DNA Polymerase Kit (Bioline, London, UK)
(M’barek et al., 2019). The PCR products were then purified using ExoSAP-IT®
(Thermo Fisher Scientific Inc., Waltham, MA, USA), followed by Sanger sequencing
(LGC Genomics GmbH, Berlin, Germany). Finally, the resulting nucleotide sequences
were processed with the software Chromas (V 2.6.6, Technelysium, Brisbane,
Australia) and compared using a Basic Local Alignment Search Tool (BLAST) with
sequences deposited in the National Center for Biotechnology Information (NCBI)
Nucleotide database (blast.ncbi.nlm.nih.gov).
3.1.6 Statistical analysis
All the experiments were carried out in triplicate and repeated twice unless stated.
Significant differences were computed by employing analysis of variance (ANOVA)
and multiple comparisons (Fischer’s least significant difference test) by employing
STATGRAPHICS Centurion XV software (StatPoint Technologies Inc. Warrenton,
VA, USA). Value of p < 0.05 was considered as significant value.
46
Section I
Pretreatment of Lignocellulose
Pretreatment of lignocellulosic biomass to overcome its intrinsic recalcitrant nature
prior to the production of valuable chemicals has been studied for nearly 200 years.
Research has targeted eco-friendly, economical, and time-effective solutions,
together with a simplified large-scale operational approach. Commonly used
pretreatment methods, such as chemical, physico-chemical, and biological
techniques are still insufficient to meet optimal industrial production requirements
in a sustainable way. Recently, advances in applied chemistry approaches
conducted under extreme and non-classical conditions has led to possible
commercial solutions in the marketplace (e.g. Microwave, and Ultrasound
technologies). These new industrial technologies are promising candidates as
sustainable green pretreatment solutions for lignocellulosic biomass utilization in a
large scale biorefinery.
47
Chapter 4
The application of microwave and ultrasound
pretreatments for enhancing saccharification
of Brewers' Spent Grain (BSG)
In this chapter, the potential of microwave and ultrasound was evaluated for energy
efficiency and environmental compatibility in the pretreatment of BSG prior to
fermentation. The highest sugar yield was obtained (64.4±7 mg) when 1 g of biomass
was pretreated with microwave energy of 600 w for 90 s, with an energy input
amounting to 0.001 Kwh of electricity. FTIR spectra of the pretreated substrates was
studied to analyse the various changes incurred as opposed to native BSG.
Work described in this chapter has been submitted as a peer review article:
Bioprocessing of brewers’ spent grain for production of Xylanopectinolytic enzymes
by Mucor sp. Bioresource Technology Reports, (2019) 9, 100371.
https://doi.org/10.1016/j.biteb.2019.100371
48
4.1 Introduction
Globally, beer is the primary consumed alcoholic beverage, and the fifth most
consumed beverage overall. Brewer's spent grain (BSG) is the solid barley waste
produced during mashing of malted barley and is separated from the wort in beer
brewing. The average global by-production of BSG is 39 million tonnes annually, with
approximately 3.4 million tonnes of BSG produced annually in Europe alone (Lynch,
2016). However, the BSG has a high moisture content (up to 80%) and polysaccharides
(40–50 % on dry weight basis) which make it susceptible to microbial growth and
spoilage after few days of storage. Thus, it is used immediately as an animal feed or is
disposed in landfill sites.
This notwithstanding, since BSG is rich in sugars, it holds potential as a cheap
feedstock option for biorefinery applications (Hassan et al., 2018a). However,
achieving enzymatic hydrolysis of sugars in BSG requires energy-intensive
pretreatment processes to fractionate the lignocelluosic complex matrix and improve
digestibility (Hassan et al., 2018b). Interestingly, both ultrasound and microwave have
potential as energy efficient green technologies for pretreatment of lignocelluosic
biomass prior to further conversions.
Microwave-assisted pretreatment of lignocellulose has been reported to be a time and
energy efficient process that does not lead to generation of inhibitors (affecting
enzymatic hydrolysis and/or microbial fermentation) or superficial overheating of
biomass (Aguilar-Reynosa et al., 2017). The efficiency of microwave-assisted
pretreatment of lignocellulose may be attributed to the energy absorbing properties of
such carbon materials, and the heating properties of the microwave electromagnetic
fields. Ultrasound technology has also been investigated for pretreatment of
49
lignocellulose, with advantages of reduction in duration of treatment and chemicals
required (Baruah et al., 2018). Cavitation bubbles are formed during the ultrasound-
assisted pretreatment that leads to fractionation of lignocellulose, and thus, an increase
in the accessibility to hydrolytic enzymes. Therefore, this study aimed to compare the
effectiveness of microwave and ultrasound-assisted pretreatments of BSG for further
use as a microbial growth substrate. Microwave-assisted pretreatment of BSG was
carried out at varying power levels (100, 250, 440, 600, and 1000 W) for 30, 60, and
90 s. Additionally, BSG was pretreated using different ultrasound frequencies (25, 35,
45, 130, and 950 kHz) for 20, 40, and 60 min.
4.2 Methodology
Brewer’s spent grain (BSG) was generously donated by a local brewery in Dublin,
Ireland. Once received, BSG was dried at 60 °C for 48 h, then milled, and sorted using
a 350-µm sieve. It was then maintained at ambient temperature in a dry place for
further experiments. All chemicals required for experimentation were purchased from
Sigma Aldrich (Ireland), and were of analytical grade. Beechwood xylan and pectin
from citrus peel were used to assay xylanase and pectionase activities, respectively.
The cellulase from Trichoderma reesei (aqueous solution, ≥700 units/g) showed 77
FPU/ml when assessed by a protocol developed by NERL National Renewable Energy
Laboratory (Adney and Baker, 1996). Hemicellulase was from Aspergillus niger
(powder, 0.3-3.0 unit/mg solid). A hemicellulase stock solution at a concentration of
10 g/l was prepared by dissolving hemicellulose powder in sodium acetate buffer (pH
4.8, 500 mM), and then the protocol developed by Rickard and Laughlin (1980) was
used to assay activity (72 U/ ml).
50
4.2.1 Pretreatment of substrate (BSG)
4.2.1.1 Microwave pretreatment
A domestic microwave oven with a frequency setting at 50 Hz and adjustable power
outputs was used. Fixed solid loading experiments were carried out by immersing five
grams of biomass in 50 ml of deionized water in a 200-mL flask. The flask was placed
at the center of a rotating circular plate in the microwave oven. Pretreatments were
carried out at 100, 250, 400 and 600 W, for 30, 60, and 90 seconds. The mixture was
then centrifuged, oven-dried, and the biomass stored for further analysis.
4.2.1.2 Ultrasound pretreatment
Three different ultrasound baths were used: (a) a multi-frequency ultrasonic bath with
selectable frequencies of 25 and 45 kHz (Elma Schmidbauer, Transonic TI-H-10,
output 550 W), (b) a multi-frequency ultrasonic bath with selectable frequencies of 35
and 130 kHz (Fisher Bio-block Scientific, Transonic TI-H-10, output 750 W), and (c)
an ultrasonic tank with a frequency of 950 kHz (Kaijo, Quava Mini Ultrasonic System,
output 100 W). Fixed solid loading experiments were carried out by immersing five
grams of biomass in 50 ml of deionized water in a 200-mL flask. Pretreatments were
carried out at 25, 35, 45, 130 and 950 kHz for 20, 40, and 60 minutes. The mixture
then centrifuged, oven-dried and the biomass stored for further analysis.
4.2.2 Enzymatic hydrolysis
The enzymatic hydrolysis of BSG was performed according to the procedure outlined
in section 3.1.4.3.
51
4.2.3 Total reducing sugars
The reducing sugar concentration in the hydrolysate was estimated following the
protocol described in section 3.1.4.2.
4.2.4 Fourier Transform Infrared Spectroscopy analysis
The changes in functional groups caused by pretreating the biomass was assessed using
FTIR spectroscopy according to section 3.1.4.6.
4.3 Results and Discussion
4.3.1 Pretreatment of BSG for recovery of fermentable sugars
4.3.1.1 Microwave pretreatment
Microwave heating can replace the conventional heating in disruption of the complex
structures of lignocellulosic biomass and achieve higher production, minimal energy
loss, and lower cost (Hassan et al., 2018a). Thus, microwave-assisted pretreatments
were carried out at 100, 250, 400, 600 and 100 W, for 30, 60, and 90 seconds. Table
4.1 shows the effect of microwave power on sugar yield from pretreated BSG. The
maximum sugar yield (64.4 ± 7.0 mg/g of BSG) was obtained at 600 w after 90 sec,
as compared to 24.5± 0.3 mg of reducing sugar obtained from every 1g of native BSG
before pretreatment.
Analysis of variance and Fischer’s Least Significant Difference (LSD) were performed
to determine whether the changes in fermentable sugars derived from BSG brought
about by microwave-assisted pretreatments at different power levels were significantly
different to each other.
52
Table 4.1: Effect of microwave power on sugar yield from BSG
Time (s)
Reducing Sugar Yield (mg/g of biomass) at different microwave power (w)
100 250 440 600 1000
Mean ±SE Mean ±SE Mean ±SE Mean ±SE Mean ±SE
30 24.4 0.2 27.8 8.5 37.2 1.2 42.1 3.7 50.9 0.7
60 37.9 1.2 41.4 8.2 46.4 1.0 64.4 6.3 51.2 1.6
90 38.2 0.2 41.3 0.7 46.3 4.7 64.4 7.0 51.1 2.1
* Where reducing sugar yields for native BSG = 24.5± 0.3 mg/g of biomass.
* Only means underlined or/and bolded are significantly different at different power levels
or/and different treatment time intervals, respectively.
ANOVA revealed that all the microwave-assisted pretreatments were significantly
different (P<0.05) at different power levels. However, not all the microwave-assisted
pretreatments were significantly different when comparing fermentable sugars derived
from BSG pretreated by microwave at different time intervals but constant power
level. For example, the microwave-assisted pretreatments for BSG at 100 W for 60,
and 90s were statistically similar as far as fermentable sugars were concerned. The
same was observed for the microwave-assisted pretreatments for BSG at 440 W for
60, and 90s.
Nevertheless, the results from microwave-assisted pretreatments for BSG at higher
power (600 or 1000 W) for short time (30s) were significantly different when
compared to their counterparts for longer time (60, and 90s). The same was observed
for the microwave-assisted pretreatments for BSG at 250 W. Furthermore, the
microwave-assisted pretreatment at 440 W was found to be statistically similar to
pretreatment at 600 W if pretreatment time was 60 or 90s.
Overall, the statistical analysis suggested that microwave power has significant impact
on pretreatment process as compared to pretreatment time in most cases. Increasing
microwave power had a positive effect on fermentable sugar yield; pretreatment time
53
can be reduced by increasing microwave power. However, for longer pretreatments
(60, 90s) the highest fermentable sugar yield was produced at 600 W power level, and
increasing microwave power level to 1000 W had a negative effect on fermentable
sugar yield.
This agrees with results obtained by Binod et al. (2012) who evaluated the microwave-
assisted pretreatments of sugarcane bagasse at different power levels (100, 180, 300,
450 , 600 and 850 W). The authors reported that the highest sugar yield was obtained
from sugarcane bagasse at 600 W microwave power using either microwave-alkali
(MAL) pretreatment or microwave-alkali followed by acid (MAA) pretreatment.
Furthermore, Mithra et al. (2017) recommended the same microwave power (600 W)
after optimization of microwave-assisted dilute acid pretreatment for peels of root
crops and vegetables. The authors found that microwave power had the greatest
influence in fermentable sugar yield as compared to other factors studied, such as
dilute sulphuric acid concentration, and irradiation time. Similar results that found 600
W microwave power to be optimal were reported by Kuittinen et al. (2016) who
studied the effect of two microwave power levels (600 and 1200 W) on pretreatment
of Norway spruce under high pressure- temperature. Recently, Tiwari et al. (2019)
investigated the application of microwave-alkali pretreatment at different microwave
power levels (100 to 600 W) and times (1 to 6 min) on banana peel waste to achieve
optimal fermentable sugars. Interestingly, the study found that the maximum reducing
sugar (0.561 g/g of biomass) was obtained after microwave-assisted alkali
pretreatment at 600 W for 2 min. It is worth noting that most studies integrate chemical
preatreatment with microwave pretreatment to improve process efficiency, in spite of
disadvantages of chemical pretreatments. In this study, microwave was investigated as
54
the sole pretreatment technology for lignocellulose to achieve an energy-efficient and
chemical-free process.
Microwave electromagnetic fields target the polar part of the biomass selectively,
leading to a rise in temperature in the internal parts of lignocellulose. Increasing
temperature also affects the degree of lignocellulose polymerization and crystallization
so that biomass becomes more accessible to hydrolytic enzymes. However, excessive
heating (resulting from high microwave power or long exposure time) can lead to
formation of “hot spots”, degradation of useful components like glucose and the
formation of unwanted components such as furfural, formaldehyde, acetaldehyde, and
dihydroxyacetone. Thereby, it is essential to find a proper microwave power and
exposure time to achieve efficient pretreatment.
4.3.1.2 Ultrasound pretreatment
Ultrasound pretreatments were carried out at varying frequencies (and ultrasound
power): 25 (550 W), 35 (750 W), 45 (550 W), 130 (750 W) and 950 kHz (100 W) for
20, 40, and 60 minutes. Table 4.2 shows the effect of ultrasound frequency on sugar
yield from pretreated BSG. The maximum sugar yield (39.9 ± 6.1 mg/g of BSG) was
obtained at low ultrasound frequency (25 kHz, 550 W) after 60 min. (compared to
24.5± 0.3 mg of reduced sugar obtained from every 1g of native BSG before
pretreatments).
Analysis of variance and Fischer’s Least Significant Difference (LSD) were performed
to determine whether the changes in fermentable sugars derived from BSG brought
about by ultrasound-assisted pretreatments at different power levels were significantly
different to each other. ANOVA revealed that all the ultrasound-assisted pretreatments
were significantly different (P<0.05) at different frequencies of each power levels.
55
Moreover, most of the ultrasound-assisted pretreatments were significantly different
(P<0.05) at different frequencies and different power levels.
Table 4.2: Effect of ultrasound frequency on sugar yield from BSG
Time
(min)
Reducing Sugar Yield (mg/ g of biomass)
US Power 550 750 100
US
Frequency
25 45 35 130 950
Mean ±SE Mean ±SE Mean ±SE Mean ±SE Mean ±SE
20 32.1 7.0 24.5 0.3 25.2 3.5 24.5 0.7 24.4 0.5
40 33.6 0.0 25.0 4.9 28.4 1.7 24.6 0.5 24.6 0.2
60 39.9 6.1 31.8 1.6 31.8 1.6 27.9 3.8 25.0 1.7
* Where reducing sugar yields for native BSG = 24.5± 0.3 mg/g of biomass.
* * Only means underlined or/and bolded are significantly different at different ultrasound
frequencies or/and different treatment time intervals, respectively.
However, some pretreatments at different ultrasound frequencies were insignificantly
different at a constant pretreatment time. For example, the ultrasound pretreatment at
a frequency of 25 KHz (550 W) did not feature any significant difference in
fermentable sugars compared to ultrasound pretreatment at frequencies of 130 KHz
(750 W) and 950 KHz (100 W) after 20 min of pretreatment. Likewise, the ultrasound
pretreatments at frequencies of 45 KHz (550 W) and 130 (750 W) did not feature any
significant difference in fermentable sugars compared to ultrasound pretreatments at
frequencies of 35 KHz (750 W) and 950 KHz (100 W) after 40 min. The same was
observed for the ultrasound-assisted pretreatments after 60 min.
Ultrasound-assisted pretreatment at 35 KHz (750 W) was statistically similar at
different time point. The same was observed for the ultrasound-assisted pretreatment
after 40 and 60 min at an ultrasound frequency of 25 KHz (550 W) and 45 KHz (550
W), and after 20 and 40 min at ultrasound frequencies of 130 KHz (750 W) and 950
KHz (100 W). Overall, the statistical analysis suggested that ultrasound pretreatment
at a frequency as low as 25 KHz is always significantly different from pretreatments
56
at higher frequencies (regardless of the ultrasound power levels) at a given
pretreatment time. Additionally, pretreatment time can be reduced by decreasing the
ultrasound frequency. The literature is dominated by pretreatments at low-ultrasound
frequencies (<50 kHz) because of the short acoustic cycle which allow the growth,
radial motion and collapse of bubbles. However, considering the longer required
pretreatment time and lower reducing sugar yield of ultrasound pretreatment, as
compared to microwave pretreatment, the latter method was selected for all subsequent
studies.
Ultrasound used for pretreatment of lignocellulosic biomass disrupted the complex
structures of lignocellulosic biomass due to the cavitation effect. Cavitation bubbles
are produced in a liquid medium as a result of ultrasound waves, and then collapse to
produce a strong mechanical effect on lignocellulosic solid biomass, as well as
producing free radicals such as H• and HO• from the liquid medium. Moreover, the
“hot spots” that develop on the surface of the solid biomass (as a result of the heat
generated during the collapse of the cavitation bubbles) can introduce chemical
changes in the lignocellulosic complex structure. It has previously been reported that
longer ultrasound pretreatment times may lead to greater sugar recovery (Rehman et
al., 2014). However, exposure to long times of ultrasound pretreatment may cause
adverse effects due to collision and aggregation between the particles, and this also
increases energy consumption during the process. Therefore, it is essential to find a
proper ultrasound power and frequency that may achieve an efficient process within
the shortest exposure time.
57
4.3.2 FTIR characterization of pretreated BSG
The chemical changes in the functional groups of the BSG were studied based on FTIR
analysis (Figure 4.1). The microwave pretreated BSG displayed significant decreases
in band intensities at characteristic peaks for cellulose, hemicellulose and lignin (Table
4.3) as compared to ultrasound-pretreated and native BSG.
Figure 4.1. FTIR spectra of native, microwave pretreated, and ultrasound
pretreated BSG.
Although all BSG samples (pretreated and native alike) exhibited a band at 1033 cm−1
which is characteristic of cellulose content in BSG (Silbir et al., 2019) in their
respective spectra, microwave pretreated BSG showed the highest transmittance (i.e.,
the lowest absorption intensity). Similar to the peak at 1033 cm−1, microwave
pretreated BSG showed the highest transmittance at different bands that represent
different functional groups. These results are related to the fact that microwave
pretreatment was more efficient in changing the chemical composition of BSG.
58
Table 4.3. FTIR peak assignments for lignocellulose
Wave number
(Cm-1)
Assignment References
1000 C–O stretching, C–C stretching of cellulose (Szymanska-Chargot
and Zdunek, 2013)
1015 C–O stretching, C–C stretching of pectin (Szymanska-Chargot
and Zdunek, 2013)
1020 C-C, C-OH, C-H ring and side group vibrations (Fan et al., 2012)
1033 C–O stretching, C–C stretching of cellulose (Szymanska-Chargot
and Zdunek, 2013)
1040 C–O stretching, C–C stretching of xyloglucan (Szymanska-Chargot
and Zdunek, 2013)
1049 C-OH bending (Adapa et al., 2011)
1058 C=O stretching of hemicellulose and lignin (Wang et al., 2018)
1063 C–O–C asymmetrical stretching of cellulose,
hemicellulose
(Sills and Gossett,
2012)
1072 C–O stretching, C–C stretching of xyloglucan (Szymanska-Chargot
and Zdunek, 2013)
3010 O-H stretching vibration (Abidi et al., 2011)
3020 O-H stretching vibration (Abidi et al., 2011)
3070 O-H stretching vibration (Abidi et al., 2011)
Similar results were also reported by other researchers (Silbir et al., 2019) who used
FTIR to identify the functional groups present in BSG samples. Moreover, the results
were consistent with reported data in the literature (Hou et al., 2019) for other
lignocellulosic biomass pretreated with microwave.
4.4 Conclusions
Microwave power has a significant impact on pretreatment of BSG as compared to
pretreatment time, and pretreatment time can be reduced by increasing microwave
power. However, for longer pretreatments (60, 90s) the highest fermentable sugar yield
was produced at 600 W power level and increasing microwave power level to 1000 W
59
had a negative effect on fermentable sugar yield. On the other hand, ultrasound
pretreatment of BSG at a low frequency of 25 KHz achieved the maximum
fermentable sugar yield compared with that obtained after ultrasound pretreatments at
higher frequencies and at an optimum pretreatment time of 60 minutes.
However, microwave pretreatment of BSG at the optimum conditions (600 w for 90
s) achieved higher fermentable sugar yield (64.4±7 mg / 1 g of BSG) as compared to
ultrasound pretreatment of BSG (39.9±6.1 mg / 1 g of BSG) at the optimum conditions
(frequency of 25 kHz, power of 550 W, and time of 60 min.). Where reducing sugar
yields for native BSG before pretreatment was 24.5± 0.3 mg/g of biomass.
Ultrasound achieved a significant increase in fermentable sugar yield as compared to
the native BSG. However, the long pretreatment time (1 hour) is a critical limiting
factor in industrial application. Thus, response surface methodology (RSM) is used in
the next chapter for optimization of the multiple variables of ultrasound pretreatment
of BSG and to investigate the ability to reduce pretreatment time. Moreover, chemical
composition, morphological structure and thermal behavior of BSG before and after
pretreatment was studied and compared.
60
Chapter 5
Optimisation of ultrasound pretreatment
of Brewers' Spent Grain (BSG)
This chapter deals with the optimization of the ultrasound (US) pretreatment of
brewer’s spent grain (BSG) using response surface methodology (RSM). In this study,
the influence of ultrasound (US) power (%), time, temperature and biomass loading
on fermentable sugar yield from BSG was studied at the optimum conditions
(frequency of 25 kHz, power of 550 W. The optimal conditions were found to be 20%
US power, 60 min, 26.3°C, and 17.3% w/v of biomass in water. Under these
conditions, an approximate 2.1-fold increase in reducing sugar yield (19.1± 0.4 mg/g
of biomass) was achieved, relative to native BSG (8.9±0.6 mg/g of biomass). Since
only water was used, there was no need for neutralization after US pretreatment for
the recovery of sugars. HPLC, FTIR, SEM and DSC were performed to analyze and
characterize the native and pretreated BSG.
Work described in this chapter has been submitted as a peer review article:
Evaluation of Ultrasonication as an Energy-Efficient Pretreatment for Enhancing
Saccharification of Brewers' Spent Grain. Waste Management (2019) 105, 240-247.
https://doi.org/10.1016/j.wasman.2020.02.012
61
5.1 Introduction
According to a Brewers of Europe report published in 2017, Europe is the second
largest beer producer in the world after China, with over 8,490 breweries producing
over 400,168 million hectoliters of beer in 2016 (BOE, 2017). Brewers' spent grain
(BSG) is the most plentiful agro-industrial waste generated from the beer-brewing
process, and about 3.4 million tonnes of BSG are produced annually in the EU
(McCarthy et al., 2013).Typically, spent grain is the insoluble part of the barley grain,
separated during the mashing process before fermentation of the soluble liquid wort
(Lynch et al., 2016). BSG represents about 85% of the total by-products, and about 2
Kg of BSG are generated per 10 liters of beer. BSG composition offers considerable
options for developing value-added products, but the complex structure of lignin and
high crystallinity of cellulose reduces its accessibility to hydrolytic enzymes. Thus, a
pretreatment step is essential prior to enzymatic hydrolysis. However, the common
conventional methods for pretreatment have major drawbacks, including high energy
consumption, the necessity to use harsh chemicals, production of inhibitors or low
efficiency in large scale production (Hassan et al., 2018a).
The application of ultrasound technology for pretreatment of lignocellulosic biomass
holds some potential for large scale processes. Ultrasonic pretreatment of plant
material in liquid media results in cavitation phenomena, whereby microbubbles are
formed which grow and then violently collapse at a critical size, converting sonic into
mechanical energy. Such cavitation effects generate high temperature, pressure and
violent shear forces, which lead to the formation of the ‘hot-spot’ effect in the liquid,
and the generation of free radicals (Bundhoo and Mohee, 2017). Such effects may be
postulated to aid in disruption of the lignocellulosic complex structure, decreasing the
62
degree of crystallinity of the cellulose, and increasing the accessible surface area to
improve enzymatic hydrolysis (Suresh et al., 2014).
Recently, chemical pretreatment methods combined with ultrasonic technique have
received some attention (Wang et al., 2018). However, very few reports discuss the
ultrasound pretreatment of lignocellulosic material using water as solvent, and
optimization of such processes has not been attempted. He et al. (2017) compared the
ultrasound pretreatment (300 W, frequency of 28 kHz) of wood in soda solution, acetic
solution, and distilled water. The authors reported an increase in sample crystallinity
up to 35.3% in the case of soda solution, and up to 35.5% in the case of distilled water
or acetic solution, which was due to physiochemical structure changes. Water is safe
to handle and is a sustainable green solvent. Moreover, using only water as an
alternative for the conventional acid and alkaline pretreatment conditions will mitigate
the risk of recovery and effluent treatment of these harsh chemicals.
The critical parameters for optimization of an ultrasound-mediated process for biomass
pretreatment include ultrasonic power (Song et al., 2015), biomass loading (Sasmal et
al., 2012), treatment time, (Sasmal et al., 2012; Rehman et al., 2014; Song et al., 2015)
and temperature (Rehman et al., 2014). Optimization of the pretreatment process by
one-factor at a time in a multifactorial process is time consuming and does not consider
the interactive effects between the variables. Thus, Response Surface Methodology
(RSM) is widely used for the statistical modeling and optimization of the multiple
variables of pretreatment processes (Flores-Gómez et al., 2018).
63
The aim of the present study is to investigate the effect of ultrasonic (US) pretreatment
on Brewers' spent grain (BSG) in an aqueous medium under optimized conditions to
achieve the highest cellulose (glucose) and hemicellulose (xylose) recovery for
bioethanol production, in addition to maximizing lignin recovery for use in the sugar-
lignin platform biorefinery (Hassan et al., 2019, 2018b).
5.2 Methodology
Brewer’s spent grain (BSG) was generously donated by a local brewery in Dublin,
Ireland. Cellulase from Trichoderma reesei (aqueous solution, ≥700 units/g), and
hemicellulase from Aspergillus niger (powder, 0.3-3.0 unit/mg solid) were purchased
from Sigma Aldrich (Ireland), and were of analytical grade.
5.2.1 Pretreatment of BSG
5.2.1.1 Experimental design
For experimental design and optimization of ultrasound pretreatment of BSG, a
Central Composite Design (CCD) of Response Surface Methodology (RSM) were
employed (Manorach et al., 2015). The effects of four pretreatment variables (biomass
loading, temperature, ultrasound power, time) each at five levels (Table 5.1) on total
sugar yield from BSG after saccharification were studied; using a CCD with five
replicates at the center points, requiring 30 experiments. Experimental runs were
randomized in order and carried out in triplicate. These factors were further optimized
using RSM. Experimental design was carried out using STATGRAPHICS Centurion
XV software (StatPoint Technologies Inc. Warrenton, VA, USA). A total of 30
experiments as given by the experimental design were carried out to investigate the
effect of varying conditions of ultrasound (Fisher Scientific TI-H-10 Ultrasonic bath)
pretreatment on the enzymic hydrolysis of BSG. After pretreatment, the biomass was
64
collected, air-dried, and stored for further enzymatic hydrolysis, compositional
analysis and characterization.
Table 5.1. Experimental range and levels of the independent variables
Code Variables Range and levels
-2 -1 0 +1 +2
A Solids/Liquids ratio (%) 5 10 15 20 25
B Temperature (°C) 20 30 40 50 60
C Ultrasound Power (%) 20 40 60 80 100
D Time (min) 20 30 40 50 60
5.2.1.2 Model development and optimization
After conducting the experiments, a second-order polynomial regression model, as
given by Eq. (1), was generated and analysed by the statistical software
STATGRAPHICS Centurion XV software (StatPoint Technologies Inc. Warrenton,
VA, USA) to define the response in terms of the independent variables.
Y = β0 + Σ βi Xi + Σ βii Xi2 + Σ βij Xi Xj (1)
Where Y, β0, βi, βj, βij, X represent process response (total sugar yield), linear
coefficients, quadratic coefficients, interaction coefficients and coded independent
variables (biomass loading, temperature, ultrasound power, and time), respectively.
The regressors (β0, βi, βj, and βij) provide a quantitative measure of the significance
of linear effects, quadratic of factors and interactions between factors. The significant
differences between each pretreatment with respect to the components of BSG were
analyzed by performing analysis of variance (ANOVA) and multiple comparisons
(Fischer’s least significant difference test); Values of p < 0.05 were considered as
significant.
65
5.2.2 Enzymatic hydrolysis
The enzymatic hydrolysis of BSG was performed according to the procedure
mentioned in section 3.1.4.3.
5.2.3 Characterization of raw and pre-treated substrate
5.2.3.1 Compositional analysis
Compositional analysis of pretreated and untreated BSG was performed according to
the protocol described in section 3.1.4.1.
5.2.3.2 Total reducing sugars
The reducing sugar concentration in the hydrolysate was estimated following the
protocol described in section 3.1.4.2.
5.2.3.3 Fourier Transform Infra-Red Spectroscopy analysis
The changes in functional groups caused by pretreating the biomass was assessed using
FTIR spectroscopy according to section 3.1.4.6.
5.2.3.4 Scanning electron microscopy
The morphological structure of BSG before and after pretreatment was observed by
performing field emission-scanning electron microscopy FE-SEM (Hitachi SU-70).
Dried samples of the untreated and pretreated BSG were subjected to FE-SEM at an
electron beam energy of 0.5 keV (Raghavi et al., 2016).
5.2.3.5 Thermal behavior
The thermal behavior of BSG before and after pretreatment was studied and compared
using differential scanning calorimetry (DSC). The thermal analysis instrument
(Shimadzu DSC-60) was controlled by TA-60WS software. For carrying out thermal
66
analysis, 55 mg of each BSG sample was placed in an aluminium pan, and an empty
pan was used as a reference. The measurements were carried out between 25°C and
500°C, with a linear increase of 10°C per min (Ballesteros et al., 2014).
5.3 Results and discussion
5.3.1 Effect of pretreatment on composition of BSG
5.3.1.1 Composition of BSG
Polysaccharides represented as much as 53% of the dry weight of BSG. BSG contained
xylan (38%), glucan (15%), and lignin (10%). This is largely in agreement with the
values previously reported in the literature for this biomass type (Lynch et al., 2016).
The hemicellulose and cellulose content of BSG as described in the literature varies
between 19-40 % and 9-25% per dry matter, respectively (Xiros et al., 2008). The
chemical composition of BSG is known to vary with barley cultivars, cultivation
conditions, harvest time, malting practices, mashing regime, the presence of hops or
adjuncts, and brewing conditions associated with lager and ale fermentations
(Mussatto, 2014).
5.3.1.2 Modelling and optimization of ultrasound pretreatment
The total sugar yield from enzymatic hydrolysis was used in Response Surface
Methodology to optimize the ultrasound-assisted pretreatment. The model adequacy
was confirmed based on the coefficient of determination (R2) and adjusted coefficient
of determination (R2-adj). Precision range for R2 is from 0 to 1.0, considering that a
value closer to 1.0 means that the model is more accurate. A value of 98.28 % R2 was
observed in the present work, while R2-adj was 96.68 %, illustrating that the model
adequately fits the data.
67
The polynomial equation (2) describing the total sugar yield behaviour is:
Reducing sugar (mg/ml) = 2.18129 - 0.1184 * A - 0.009875 * B - 0.0658167 * C +
0.135458 * D - 0.00243 * A2 + 0.0003025 * A * B + 0.0012275 * A * C + 0.002905
* A * D - 0.00053875 * B2 + 0.00132438 * B * C - 0.00117125 * B * D + 0.000285937
* C2 - 0.00109875 * C * D - 0.0008125 * D2 (2)
The significance of the coefficients of the model was determined by ANOVA. The
ANOVA table (Table 5.2) showed that 12 effects have P-values less than 0.05,
indicating that they are significantly different from zero at the 95.0% confidence level,
and implying a considerable effect of these coefficients on reducing sugar yield. The
predicted levels of sugar yield in pretreated BSG using the equation (2) are given in
Table 5.3, along with experimental data.
Table 5.2. ANOVA table for the quadratic model of ultrasound pretreatment
Source Sum of Squared DF Mean square F-Ratio P-Value
A:SL ratio 0.0680535 1 0.0680535 13.43 0.0023
B:Temp 0.601034 1 0.601034 118.65 0
C:Power 0.158763 1 0.158763 31.34 0.0001
D:Time 0.00380017 1 0.00380017 0.75 0.4001
AA 0.101227 1 0.101227 19.98 0.0004
AB 0.00366025 1 0.00366025 0.72 0.4087
AC 0.241081 1 0.241081 47.59 0
AD 0.337561 1 0.337561 66.64 0
BB 0.0796119 1 0.0796119 15.72 0.0012
BC 1.12254 1 1.12254 221.6 0
BD 0.219492 1 0.219492 43.33 0
CC 0.358811 1 0.358811 70.83 0
CD 0.772641 1 0.772641 152.52 0
DD 0.181071 1 0.181071 35.74 0
Total error 0.0759854 15 0.00506569
Total (corr.) 4.4242 29 0.0680535
68
Table 5.3. Experimental conditions and results of central composite design
Run
Variables Response
Factor
A
Factor
B
Factor
C
Factor
D Reducing Sugar (mg/ml)
SL ratio
(%)
Temp
(°C)
Ultrasound
power (%)
Time
(min) Experimental Predicted
1 20 30 80 50 139 144
2 5 40 60 40 97 115
3 15 40 60 40 150 150
4 15 40 60 60 118 120
5 20 30 40 30 132 134
6 10 30 40 30 185 180
7 15 60 60 40 93 97
8 15 40 60 40 150 150
9 15 40 60 40 150 150
10 10 50 40 30 123 116
11 15 40 100 40 177 179
12 15 40 20 40 208 212
13 10 30 80 50 89 83
14 15 40 60 20 111 115
15 15 40 60 40 150 150
16 10 50 80 50 82 78
17 15 40 60 40 150 150
18 10 50 40 50 115 110
19 10 50 80 30 177 172
20 20 50 40 50 126 128
21 25 40 60 40 147 136
22 20 50 80 30 179 181
23 20 30 80 30 133 133
24 10 30 40 50 225 221
25 20 50 40 30 74 76
26 10 30 80 30 135 130
27 20 50 80 50 145 145
28 20 30 40 50 233 233
29 15 40 60 40 150 150
30 15 20 60 40 158 160
69
Fig
ure
5.1
: R
esp
on
se s
urf
ace
plo
ts r
epre
sen
tin
g t
he
effe
ct
of
ind
epen
den
t vari
ab
les
on
red
uci
ng s
ugar
yie
ld:
(a)
Eff
ect
of
soli
d-t
o-l
iqu
id
rati
o a
nd
tem
per
atu
re o
n r
edu
cin
g s
ugar
yie
ld w
hen
th
e re
spon
se s
urf
ace
is
fixed
at
ult
raso
un
d p
ow
er =
60%
an
d t
ime
= 4
0 m
in;
(b)
Eff
ect
of
soli
d-t
o-l
iqu
id r
ati
o a
nd
ult
raso
un
d p
ow
er o
n r
edu
cin
g s
ugar
yie
ld w
hen
th
e re
spon
se s
urf
ace
is
fixed
at
tem
per
atu
re =
40°C
an
d t
ime
= 4
0 m
in;
(c)
Eff
ect
of
soli
d-t
o-l
iqu
id r
ati
o a
nd
tim
e on
red
uci
ng s
ugar
yie
ld w
hen
th
e re
spon
se s
urf
ace
is
fix
ed a
t te
mp
eratu
re
= 4
0°C
an
d u
ltra
sou
nd
pow
er =
60%
; (d
) E
ffect
of
tem
per
atu
re
an
d u
ltra
sou
nd
pow
er o
n r
edu
cin
g s
ugar
yie
ld w
hen
th
e re
spon
se s
urf
ace
is f
ixed
at
Soli
d-t
o-l
iqu
id r
ati
o =
15
% (
w/v
) an
d t
ime
= 4
0 m
in;
(e)
Eff
ect
of
tem
pera
ture
an
d t
ime
on
red
uci
ng s
ugar
yie
ld w
hen
th
e
resp
on
se s
urf
ace
is
fixed
at
soli
d-t
o-l
iqu
id r
ati
o =
15%
(w
/v)
an
d u
ltra
sou
nd
pow
er =
60%
; an
d (
f) E
ffec
t o
f u
ltra
sou
nd
pow
er a
nd
tim
e
on
red
uci
ng s
ugar
yie
ld w
hen
th
e re
spon
se s
urf
ace
is
fixed
at
soli
d-t
o-l
iqu
id r
ati
o =
15 %
(w
/v)
an
d t
emp
eratu
re =
40°C
.
70
Additionally, response surface plots were generated as a function of two factors at a
time in order to understand the main and interaction effects of different variables, and
to determine the optimal level of each variable for maximum response (Figure 5.1). A
maximum reducing yield was observed with low ultrasound power (20%) and
temperature (26°C); as well as high biomass loading (17g/100 mL), and long
pretreatment time (60 min). Under optimum conditions, the model predicted the
maximum sugar yield to be 388 mg/mL of reaction volume.
After the optimized pretreatment of the native BSG, and following saccharification,
74% of sugars in BSG were recovered, while no degradation of lignin was observed
(remaining as 10.6 g per 100 g of raw BSG). Improvement in saccharification of BSG
without lignin degradation may be attributed to the type of solvent used in this
pretreatment step (water, with no chemicals), and the mechano-acoustic (physical)
effects of ultrasound that increase surface erosion and pore size, and therefore the
accessibility of biomass to hydrolytic enzymes (Bussemaker and Zhang, 2013).
Obtaining high saccharification yields from lignocellulose, while maintaining a high
amount of lignin after the pretreatment available for further valorization, can maximize
the utilization of lignocellulose.
For the validation of the model, a confirmation experiment was conducted using the
optimized parameters, and the experimentally obtained values of total reducing sugar
amounted to 325 ± 1.0 mg/mL. Thus, since the experimentally obtained values of total
reducing sugar from native BSG amounted to 151 ± 0.6 mg/mL, this model provided
a 2.1-fold higher reducing sugar yield.
Ultrasonic frequency has a significant effect on ultrasound pretreatment, due to its
influence on the critical size of the cavitation bubble. Lower frequency ultrasound (20–
71
100 kHz) can produce more violent cavitation, resulting in higher localized
temperatures and pressures at the cavitation site, as well as more effective shock waves
(Hilares et al., 2018) ; water molecules dissociate into oxidative radicals that oxidize
and degrade organic molecules. Therefore, low frequency ultrasound is commonly
used in biomass processing for intense physical effects, such as cell disruption (Tang
and Sivakumar, 2015).
Using similar low frequency ultrasound for pretreatment of biomass was reported by
other researchers (Rehman et al., 2014) who used ultrasonication at an operating
frequency of 20 kHz (power 750 W) and employing 20 % of amplitude for enhancing
sulfuric acid pretreatment of rice straw. Moreover, the results were consistent with
reported data in the literature (Subhedar and Gogate, 2013) for newspaper pretreated
with ultrasound (at frequency of 20 KHz) combined with alkali (NaOH) solution
(concentration of 1 N). Eblaghi et al. (2016) also employed ultrasonic irradiation at a
frequency of 35 kHz combined with alkaline solution (3% NaOH concentration) for
pretreatment of sugarcane bagasse.
The ultrasonic treatment is also known to be influenced by solvent properties
(Bussemaker and Zhang, 2013): water can produce more favorable conditions for
cavitation than solvents with higher viscosity, surface tension or density. According to
the literature, increases in biomass loading, temperature, sonication time, or the US
power applied to the reaction mixture results in an increased rate of the reaction only
up to a certain point; further increases in these parameters do not result in greater
disruption effects (Bussemaker and Zhang, 2013; Karimi et al., 2014).
72
5.3.1.3 FTIR analysis
The chemical changes in the functional groups of the BSG were studied based on FTIR
analysis (Figure 5.2). The pretreated BSG displayed significant decreases in band
intensities at characteristic peaks for cellulose (3290 Cm-1), hemicellulose (1030
Cm-1) and lignin (1240 Cm-1) (Table 5.4). Similar reduction in intensity of peaks
representing functional groups of BSG after ultrasound-assisted pretreatment of
lignocellulose were reported by Ravindran et al. (2017). These observations indicate
that ultrasound treatment disturbed the lignocellulosic structure of BSG, increasing the
yield of the reducing sugar.
Figure 5.2: FTIR spectra of native and pretreated BSG.
73
Table 5.4. FTIR peak assignments for lignocellulose
Wave
number
(Cm-1)
Assignment References
1030 C-O stretching vibration of hemicelluloses (Peng et al., 2015)
1160 C-O stretching vibration of acetyl xylan (Peng et al., 2015)
1240 C−O stretching of syringyl lignin (Kalia, 2018)
1380 phenolic OH and aliphatic C–H in methyl groups (Coletti et al., 2013)
1460 C-H bending vibration of chitosan (Peng et al., 2015)
1540 vibration of C=O (Karta et al., 2016)
1640 conjugated C=O stretch (Khanna et al., 2017)
1740 C=O stretching vibration of acetyl xylan (Peng et al., 2015)
2080 vibration of C≡N (Kartel and Galysh, 2017)
2360 stretching of C=O (Kalia, 2018)
2850 stretch of C-H (Khanna et al., 2017)
2920 stretching of C=O (Kalia, 2018)
3290 vibration of OH group of cellulose (Jawaid et al., 2017)
5.3.1.3 Scanning electron microscopy
SEM analysis of the raw (control) and pretreated BSG revealed surface modifications
in response to pretreatment (Figure 5.3). The untreated BSG had an undulated and
crumbled surface as a result of the brewing process. The ultrasound pretreated BSG
showed a porous surface of uneven and non-uniform cavities. This may be due to the
modifications in the external fibers arising from the effect of the cavitation due to
ultrasound waves (Ravindran et al., 2017). Gabhane et al. (2014) compared alkali-
microwave and alkali-sonication pretreatment of lignocellulosic biomass and reported
that ultrasound pretreatment resulted only in fibrillation of the cell wall of the
pretreated biomass; however, microwave pretreatment led to complete tissue collapse.
74
Ultrasound pretreatment increased the total surface area of BSG that was exposed to
enzyme activity and enhanced the enzyme accessibility.
Figure 5.3: Micrographs by scanning electron microscopy (SEM) of native (on
Left), and the modified structure of the pretreated BSG (on Right).
Magnification, 1000-fold.
5.3.1.3 Differential scanning calorimetry
Differential scanning calorimetry determines the difference in the heat flow associated
with heating, cooling, or isothermal conditions of the sample, compared with a
reference, and as a function of temperature. The DSC thermogram (Figure 5.4)
represented the thermal characteristics of the native and the pretreated BSG between
20°C and 500°C, which were obtained at a heating rate of 10°C/min. Both native and
pretreated BSG exhibited a similar trend in their thermo-gram profile, suggesting that
they were approximately similar in their composition. An exothermic event was
evident between a temperature range of 20–300°C. This temperature range was
associated with several processes which gave rise to compounds such as carbon
monoxide, carbon dioxide and other pyrolysis products. In addition, an endothermic
event was observed between a temperature range of 300–500°C. Both native and
75
pretreated BSG exhibited a thermal behaviour that included a crystallization peak at
the temperature of 370°C.
Figure 5.4: DSC thermogram of native and pretreated BSG.
5.4 Conclusion
The optimal conditions for ultrasound-assisted pretreatment of BSG were found to be
20% US power, 60 min, 26.3°C, and 17.3% w/v of biomass in water. BSG pretreatment
under the optimal ultrasonication conditions resulted in a 2.1-fold higher reducing
sugar yield, relative to native BSG. These results suggest that performing a design of
experiments (DOE) to optimise ultrasound pretreatment of BSG achieved higher
fermentable sugar yield (2.1-fold, relative to native BSG) as compared to the one-
factor-at-a-time (OFAT) approach used in previous chapter for optimisation of
ultrasound pretreatment that achieved 1.6-fold increase in fermentable sugar yield,
relative to native BSG.
76
However, the same long pretreatment time (60 min.) is required in both of the design
of experiments (DOE) or the one-factor-at-a-time (OFAT) approach to achieve
maximum fermentable sugar yield from BSG using ultrasound pretreatment.
Moreover, exposure to long times of ultrasound pretreatment may cause adverse
effects due to collision and aggregation between the particles, and also increase energy
consumption during the process. Therefore, considering the longer required
pretreatment time of ultrasound pretreatment, as compared to microwave pretreatment,
the latter method was selected as an energy efficient and effective pretreatment step
for BSG for all subsequent fermentation studies.
77
Section II
Enzyme Production using
Lignocellulosic Food Waste
Xylanases (E. C. 3.2.1.8, 1, 4-β-xylanxylanohydrolase) are enzymes that break down
xylan, while pectinases are a class of enzymes that break down pectin. Both xylan and
pectin are integral parts of the plant cell wall. Thus, pectinases and xylanases are used
in fruit juice and other numerous biotechnological processes. Several studies have
shown a rejuvenated interest in solid state fermentation for the production of these
microbial enzymes. Many of the attributes of agro-industrial residues are especially
suited to this form of culture and can yield significant benefits in terms of
sustainability. The latter include environmental friendliness and significant reduction
in production costs.
78
Chapter 6
Pectinase and xylanase production by fungi
isolated from spoiled fruit
In this chapter, the utility of brewer’s spent grain (BSG) as substrate for production
of pectinase and xylanase using fungi was investigated. Fungi were isolated from
nineteen (19) spoiled fruits belonging to different varieties and screened for
production of Xylanopectinolytic enzymes. Out of twenty-nine (29) isolates recovered,
Mucor hiemalis isolated from Bramley apple (Malus domestica) produced
xylanopectinolytic enzymes with higher specific activity and were selected for
production studies. The highest enzyme activity (137 U/g, and 67 U/g BSG, for
pectinase and xylanase, respectively) was achieved in a medium that contained 15 g
of BSG, at pH 6.0, temperature of 30°C, and supplemented with 1.0 % xylan or pectin
for inducing the production of xylanase or pectinase, respectively. The partially
purified and concentrated pectinase and xylanase were optimally active at 60°C and
pH 5.0 (1602 U/ml of pectinase, and 839 U/ml of xylanase, respectively). Thus, the
present study suggests that BSG is an effective substrate for production of microbial
enzymes such as pectinase and xylanase.
Work described in this chapter has been submitted as a peer review article:
Bioprocessing of brewers’ spent grain for production of Xylanopectinolytic enzymes
by Mucor sp. Bioresource Technology Reports, (2019) 9, 100371.
https://doi.org/10.1016/j.biteb.2019.100371
79
6.1 Introduction
Although the large majority of industries still rely on chemical catalysts, there is an
increasing demand for biocatalysts since their introduction in the 1960′s. The global
market for biocatalysts increased from approximately $2 billion dollars in 2004
(Joseph et al., 2008) to $3.3 billion dollars in 2010 (Sarrouh et al., 2012), and
increasing to 7 billion dollars by 2017: it is expected to reach $10.5 billion dollars by
2024 (Editorial, 2018).
Xylanases (E. C. 3.2.1.8, 1, 4-β-xylanxylanohydrolase) are enzymes that break down
xylan, which is the second most abundant polysaccharide that constitutes an integral
part of plant cell wall. While xylanases are usually used in pulp and paper industries
for bleaching of cellulose pulp as an alternative to chlorine treatment (Walia et al.,
2017), xylanases have also found other wide ranging commercial applications in areas
such as animal feed, bakery processing, food and drinks, and lignocellulose
degradation (Malhotra and Chapadgaonkar, 2018). Many countries including Canada,
Denmark, Finland, Germany, Japan, Ireland and USA are involved in the industrial
production of commercial xylanases (Polizeli et al., 2005).
Pectinases are a class of enzymes that catalyse the disintegration of pectin-containing
compounds. Pectin compounds are an integral part of the plant cell wall. Pectinases
are used in the fruit juice industry and wine making for clarification and removal of
turbidity in the finished product. While pectinases are usually used in fruit and
vegetable processing for treatment of pulp to produce juices with higher yield, low
viscosity and high clarity (Verma et al., 2018), xylanases have found other uses in
animal feed, tea and coffee processing, extraction of vegetable oil, and lignocellulose
degradation (Garg et al., 2016).
80
Commercial applications require cheaper enzymes. Interestingly, waste materials from
a wide range of agro-industrial processes may be used as substrates for microbial
growth, and subsequently the production of a range of high value products, including
enzymes of commercial interest (such as pectinases and xylanases). Such an approach
may significantly reduce the cost of enzyme production. In addition, utilization of
these agro-residues in bioprocesses has the dual advantage of providing alternative
substrates, as well as solving their disposal problems.
Brewers' spent grain (BSG) is the main by-product of the brewing industry and
represents an abundant agro-industrial waste. Many cereal brans with similar chemical
composition and structure to BSG have been used for the production of commercial
enzymes in solid-state-fermentation. This encouraged the investigation of BSG as
alternative substrate for production of enzymes (Anal, 2017).
Although researchers investigated the utilisation of BSG for production of xylanase,
this work represents the first study on utilisation of BSG for pectinase production. BSG
has been used for production of xylanases by fungi such as F. oxysporum (Xiros and
Christakopoulos, 2009), Penicillium glabrum (Knob et al., 2013), Penicillium
janczewskii (Terrasan and Carmona, 2015), yeast such as Moesziomyces spp. (Faria et
al., 2019) and bacteria such as: Anoxybacillus sp. (Alves et al., 2016), B. cereus (Łaba
et al., 2017), B. subtilis (Moteshafi et al., 2016), and Bacillus amyloliquefaciens
XR44A (Amore et al., 2015).
Fungi represent the main producers for most of the industrial lignocellulose-degrading
enzymes, and screening in the present study focused exclusively on this group.
Therefore, this chapter aims to investigate the effectiveness of pretreated BSG as a
growth substrate in the production of xylanopectinolytic enzymes by fungi.
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6.2 Methodology
Brewer’s spent grain was generously donated by a brewery in Dublin. The BSG was
dried at 60°C for 48h and thereafter ground and sorted using a 350 μm sieve. It was
then stored at room temperature in a cool and dry place for further experiments. All
the chemicals such as cellulase from Trichoderma reesei, hemicellulase from
Aspergillus niger, conc. H2SO4, alkaline potassium permanganate, ethanol, ferric
chloride, and other chemicals required for experimentation were purchased from
Sigma Aldrich, Ireland. Cellulase activity was assayed by following laboratory
analytical procedures for the measurement of cellulase activity devised by National
Renewable Energy Laboratory (Adney and Baker, 1996). Meanwhile hemicellulase
activity was assayed followed protocols described by Rickard and Laughlin (1980).
Cellulase enzyme registered an enzyme activity of 77 FPU/ml while hemicellulase
showed 72 U/ml enzyme activity.
6.2.1 Pretreatment of substrate (BSG)
Microwave-assisted pretreatment of BSG was performed according to the optimum
conditions described in section 3.2.1.1.
6.2.2 Isolation and screening of xylanopectinolytic enzyme-producing fungi
6.2.2.1 Isolation of fruit-rotting fungi
Samples of nineteen different spoiled fruits (Pome, Stone, and Soft fruits) were
randomly collected in January and February 2019 from a commercial pack-house
facility in Dublin, Ireland. The type of fruits collected was dependent upon availability;
fruits (and varieties) namely: apple (Bramley, and Gala), blackberry (Tupi), blueberry
(Emerald, and Duke), grape (Crimson, Melody, Tawny, Sugraone, and Sugarthirteen,
82
Starlight), raspberry (Kweli, Imara, and Framboises), strawberry (Calinda, Rociera,
Sensation, and Marquis), and cherry (Regina). The fruits were collected in separate
sterile polythene bags and transferred to the laboratory for the study. Suspected fungal
growth visible on the fruits was aseptically transferred to potato dextrose agar (PDA)
plates and incubated at 25°C until fungal growth developed on the medium surface.
Isolates were serially sub-cultured until pure and transferred to PDA slants for storage
at 5°C. Isolates were identified to genus level based on macroscopic appearance and
microscopic characteristics under x40 objective lens of a light microscope (after
staining with a lactophenol cotton blue dye), and compared with a standard
mycological atlas (Watanabe, 2010).
6.2.2.2 Molecular Identification
The best xylan and pectin degrader among all the fungi was identified to the species
level by molecular techniques according to the procedure mentioned in section 3.1.5.
6.2.2.2 Qualitative screening of xylanopectinolytic enzyme-producing fungi
The presence of xylanases and pectinases was detected as described earlier (Félix et
al., 2016) using differential, substrate-containing media. Briefly, the various substrates
[0.5% (w/v) xylan, and 0.5% (w/v) pectin, respectively] were independently added to
a medium containing 0.5% (w/v) malt extract and 1.5% (w/v) agar. Each isolate was
inoculated individually on enzyme assay plates and incubated at 25°C for 7 days. The
activities were detected by the formation of a halo around the mycelium after flooding
the plates with Congo red (1.0 mg/ml) for 15 min, discarding the solution, and washing
cultures with 1.0 M NaCl. The appearance of hydrolysis zones (halo) around the fungal
growth was observed against the red background and considered as positive result. All
83
fungal isolates that showed a positive result were further assessed for their enzymatic
activities via a quantitative assay.
6.2.2.3 Quantitative determination of xylanopectinolytic enzymes produced by
fungi
Following qualitative screening, the fungal isolates were further screened for yield
potential by quantitative determination in submerged fermentation using basal liquid
media (Mandels and Weber, 1969) supplemented with 1.0 % (w/v) of either xylan or
pectin for determination of xylanase and pectinase, respectively (Deep et al., 2014).
For each experiment, 50ml of basal liquid media was poured into a 250ml Erlenmeyer
conical flask and then was autoclaved at 15 psi and 121°C for 20 minutes. After
cooling, each flask was inoculated with a PDA agar plug of growth (0.5 cm) which
had previously been cultivated for 7-days. Flasks were incubated in a rotary incubator
(150 rpm) at 30 ∘C for seven days. After incubation, the supernatants were collected
separately by filtration using Whatman filter paper (No.1) followed by centrifugation
at 10,000 rpm at 4°C for 10 minutes. The supernatants were used for protein
estimation and enzyme activity assay as crude enzyme.
6.2.3 Production of xylanopectinolytic enzymes using BSG under different
conditions
The best xylan and pectin degrader among all the fungi tested was selected for solid-
state fermentation (SSF) using BSG as a fermentation medium. Pretreated BSG was
used as the main carbon source (5.0 g), moisturized (77.5 %) with basal medium
(Mandels and Weber, 1969) as described by Gautam et al. (2018) and supplemented
with 1.0% xylan or pectin for inducing the production of xylanase or pectinase,
respectively. The approach of ‘‘one-factor-at-one-time’’ was employed for
84
optimization of different cultural parameters for enzyme production, where during
each run a single factor was varied and other factors were kept at a constant level. The
factors studied were BSG concentration (5.0, 10, 15, 20 and 25 % w/v), pH (4.0, 5.0,
6.0, 7.0 and 8.0), and temperature (20, 25, 30 and 37 °C). The optimized culture
conditions derived from these experiments were then used for production of enzyme.
Following media sterilization by autoclaving at 121 °C for 30 min, five 7-mm-diameter
agar plugs of actively growing fungi were used as inoculum. Flasks were incubated at
30 °C (except for temperature studies) for 7 days, and then 50 ml of distilled water was
added to each flask. Substrate in flasks was crushed with a glass rod, and then shaking
was applied at 150 rpm for 60 min in an incubator shaker. Following shaking, the
flasks were harvested by squeezing the BSG, and the supernatant was further
centrifuged at 10,000 rpm at 5.0°C for 15 minutes. The cell-free supernatants were
treated as crude enzyme and were used for protein estimation and enzyme activity
assay.
6.2.3.1 Partial purification and concentration of pectinases and xylanases
Crude enzymes were centrifuged at 12,000 g for 15 min. The resulting cell-free
supernatants were fractionated by ammonium sulfate between 50–80% saturation to
find the optimum saturation. Extracts were further purified by centrifugal
ultrafiltration using a 10-kDa MW cut-off membrane.
6.2.3.1 Determination of pH and temperature profiles
In order to determinate the optimal pH for enzyme activity, the xylanase and pectinase
were studied over a pH range of 2.0-11.0 at 50 °C with 1.0% xylan and 1.0% pectin,
85
respectively. To determinate the optimal temperature for activity, the enzymes were
tested at different temperatures between 20 and 80 °C (More et al., 2011), and at the
optimum pH of each enzyme.
6.2.4 Protein and enzyme assays
6.2.4.1 Assay of Total Protein
Protein concentration was estimated following the protocol described in section
3.1.4.4.
6.2.4.2 Assay of enzyme activity
Enzyme activity was estimated following the protocol described in section 3.1.4.5.
6.3 Results and discussion
6.3.1 Isolation and screening of xylanopectinolytic enzyme-producing fungi
6.3.1.1 Isolation xylanopectinolytic enzyme-producing fungi
Twenty-nine fungal isolates were obtained from spoiled-fruit samples of 19 different
varieties (Table 6.1). The isolates were distributed in three genera, namely: Mucor spp.
(19 isolates), Pencillium spp. (6 isolates), and Aspergillus spp. (4 isolates). All isolates
were selected for subsequent pectinase and xylanase production screening.
The low pH of fruit gives fungi a competitive advantage over bacteria. Moreover, fungi
can spread on commodities in cold storage (Moss, 2008). Some fungi have the ability
to produce pectinolytic enzymes to soften the plant tissue causing rot, and others infect
through wounds caused during handling and packing of the fruit. Aspergillus spp.,
Pencillium spp., and Mucor spp. are among the commonly encountered species
causing spoilage of fruit (Moss, 2008).
86
Table 6.1: List of fruit-spoilage fungi isolated from infected fruits
Code Fungal Isolate Fruit Fruit Variety
AB1 Mucor sp. Apple Bramely
AB2 Mucor sp. Apple Bramely
AG1 Penicillium sp. Apple Gala
GC1 Mucor sp. Grape Crimson
GM1 Aspergillus sp. Grape Melody
GM2 Mucor sp. Grape Melody
GM3 Penicillium sp. Grape Melody
GT1 Aspergillus sp. Grape Tawny
GSO1 Mucor sp. Grape Sugraone
GS1 Penicillium sp. Grape Starlight
GS2 Penicillium sp. Grape Starlight
GS3 Mucor sp. Grape Starlight
GST1 Aspergillus sp. Grape Sugarthirteen
BT1 Mucor sp. Blackberry Tupi
BE1 Mucor sp. Blueberry Emerald
BD1 Mucor sp. Blueberry Duke
BD2 Mucor sp. Blueberry Duke
RI1 Penicillium sp. Raspberry Imara
RI2 Mucor sp. Raspberry Imara
RK1 Mucor sp. Raspberry Kweli
RK2 Penicillium sp. Raspberry Kweli
RF1 Mucor sp. Raspberry Framboises
SC1 Mucor sp. Strawberry Calinda
SC2 Mucor sp. Strawberry Calinda
SR1 Mucor sp. Strawberry Rociera
SR2 Aspergillus sp. Strawberry Rociera
SS1 Mucor sp. Strawberry Sensation
SM1 Mucor sp. Strawberry Marquis
CR1 Mucor sp. Cherry Regina
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6.3.1.2 Screening of xylanopectinolytic enzyme-producing fungi
In the current study, three candidates were obtained from qualitative screening as
pectinase producers (Mucor sp. AB1, Penicillium sp. GS1 and GS2) and three
candidates as xylanase producers (Penicillium sp. GS1, Mucor sp. AB1 and RI2,).
Among these, Mucor sp. coded AB1 displayed the highest extracellular pectinase and
xylanase activity after the quantitative determinations (Table 6.2) and was, therefore,
retained for all subsequent studies. In a previous study, Mucor sp. has been reported
as a higher polygalacturonase producer than Aspergillus sp. (Thakur and Gupta, 2012)
when authors compared the activity of polygalacturonase produced by Aspergillus sp.,
Byssochlamys sp., and Mucor sp. However, the literature is dominated by studies that
employ Aspergillus sp. for production of xylanopectolytic enzymes (Ravindran et al.,
2018a).
Table 6.2: Secondary evaluation of xylanases and pectinases production
by fruit-spoilage fungi
There are relatively few publications on production of xylanase and pectinase by fungi
that belong to the species of the genus Mucor. Daling et al. (2008) reported in their
patent the production of xylanases by Mucor rouxii under aerobic fermentation in
media containing xylan as inducer. Behnam et al. (2016) reported the production of
xylanases by Mucor indicus (43.1 U/g of dry substrate) and Mucor hiemalis (43.8 U/g
of dry substrate) on wheat bran as fermentation medium; these authors found that the
Code Fungal Isolate
Xylan-supplemented medium Pectin-supplemented medium
Total Cell-
free Protein
(mg/ml)
Xylanase
activity (u/ml)
Total Cell-
free Protein
(mg/ml)
Pectinase
activity (u/ml)
AB1 Mucor sp. 28.1 ± 0.7 9.8 ± 0.2 14.5 ± 0.1 14.4 ± 0.6
GS1 Penicillium sp. 23.3 ± 0.4 7.4 ± 0.1 11.0 ± 0.1 7.7 ± 0.8
GS2 Penicillium sp. - - 10.4 ± 0.3 0.4 ± 0.0
RI2 Mucor sp. 21.4 ± 0.6 7.2 ± 0.1 - -
88
optimum fermentation conditions were a temperature of 40 and 43.4 °C, moisture
percent of 49.8 and 54.2 %, and incubation time of 51.3 and 53.2 h for Mucor indicus,
and Mucor hiemalis, respectively.
The pectinolytic enzyme (polygalacturonase) was found to be optimally produced by
Mucor circinelloides ITCC 6025 after 48 h of fermentation at 30°C and pH 4.0 with
pectin methyl ester (1% w/v) as carbon source (Thakur et al., 2010). Moreover, Sharma
et al. (2013) reported the production of polygalacturonase by the same isolate of Mucor
circinelloides ITCC 6025 on medium containing pectin (1% w/v) as sole carbon source
after fermentation for 48 h at 30°C and pH 4.5.
6.3.1.3 Molecular Identification of Mucor sp. AB1
Mucor sp. AB1 isolate used in the current study was identified to the species level
by analyzing the sequences of the ITS region of ribosomal DNA (ITS1-5.8S-ITS2)
and pairwise sequence alignment using the BLAST algorithm. The resultant nucleotide
sequence in this study (Table 6.3) matched 100% with the published sequence of
Mucor hiemalis (Accession No. JQ912672.1) available in GenBank.
Table 6.3. Nucleotide sequences of Mucor hiemalis
11 TAGAGGAAGT AAAAGTCGTA ACAAGGTTTC CGTAGGTGAA CCTGCGGAAG GATCATTAAA
70 TAATTTAGAT GGCCTCCTAG AACTTGTTCT AGTTAGGTTC ATTCTTTTTT ACTGTGAACT
130 GTTTTAATTT TCAGCGTTTG AGGAATGTCT TTTAGTCATA GGGATAGACT ATTAGAATGT
190 TAACCGAGCT GAAGTCAGGT TTAGACCTGG TATCCTATTA ATTATTTACC AAAAGAATTC
250 AGTATTATTA TTGTAACATA AGCGTAAAAA ACTTATAAAA CAACTTTTAA CAACGGATCT
310 CTTGGTTCTC GCATCGATGA AGAACGTAGC AAAGTGCGAT AACTAGTGTG AATTGCATAT
370 TCAGTGAATC ATCGAGTCTT TGAACGCAAC TTGCGCTCAA TTGAGCACGC CTGTTTCAGT
430 ATCAAAAACA CCCCACATTC ATAATTTTGT TGTGAATGGA ACTGAGAATT TCGACTTGTT
490 TGAATTCTTT AAACTTATTA GGCCTGAACT ATTGTTCTTT CTGCCTGAAC ATTTTTTTAA
550 TATAAAGGAA TGCTCTAGTA TTAAGACTAT CTCTGGGGCC TCCCAAATAA AACTTTCTTA
610 AATTTGATCT GAAATCAGGC GGGATTACCC GCTGAACTTA AGCATAT
89
6.3.2 Production of Xylanopectinolytic Enzymes using BSG
6.3.2.1 Optimization of production
Microwave pretreated BSG at 600 w for 90 s was used for solid-state fermentation by
Mucor hiemalis . Various process parameters influencing pectinases and xylanases
production viz., substrate loading (5, 10, 15, 20, and 25 g), incubation temperature (20,
25, 30, and 37°), and pH (4, 5, 6, 7, and 8) were studied.
Figure 6.1 shows the effect of substrate loading, incubation temperature, and pH on
pectinase and xylanase production by Mucor hiemalis . Enzyme activities of 137 U/g,
and 67 U/g BSG, for pectinase and xylanase, respectively were achieved in medium
that contained 15 g of BSG, at pH 6.0, and at a temperature of 30°C.
Temperature and pH play an important role in the synthesis of microbial enzymes. The
optimum temperature for production of pectinase and xylanase by Mucor hiemalis was
30° C. Temperature beyond 30°C led to a decrease in enzyme specific activity. Most
fungi are mesophiles with optimum growth temperatures between 25 and 30°C (Dix
and Webster, 1995).
The same optimum temperature was reported by Thakur et al. (2010) for production
of polygalacturonase by Mucor circinelloides ITCC 6025 and was also reported by
Ahmad et al. (2009) for production of Xylanase by Aspergillus niger. Similar
temperatures were reported for optimal production of xylanase, ranging from 28⁰ C
by Aspergillus niger (Bhushan et al., 2012) to 32.5⁰ C by Fusarium sp. BVKT R2
(Ramanjaneyulu and Reddy, 2016).
90
Figure 6.1. Effect of temperature, substrate, and pH on pectinases and xylanases
production by Mucor hiemalis .
Furthermore, increasing pH from 4.0 to 6.0 increased the production of pectinase and
xylanase. However, enzyme specific activity then decreased until pH 8.0. This can be
attributed to the fact that most fungi grow optimally at a low pH between 5.0 and 6.0.
In earlier studies, Bhushan et al. (2012) and Anthony et al. (2003) reported similar
91
results while, according to Ramanjaneyulu and Reddy (2016) and Terrone et al. (2018)
a pH value of 5.0 was an optimum.
The optimum biomass loading for production of pectinases and xylanases by Mucor
hiemalis was 15 g per 250-ml conical flask, with any further increases suppressing
enzyme activity. In addition to the availability of more nutrients, more inducers are
available to the fungus at the optimum substrate concentration that in turn enhance the
production of enzymes (Ahmed et al., 2016). In an earlier study, Kavya and
Padmavathi (2009) found that maintaining the substrate concentration (wheat bran) at
10g/250 ml conical flask yielded the maximum production of xylanase by Aspergillus
niger. However, the optimum substrate concentration for enzyme production varies
widely with varying substrate type, and the water absorbed onto the substrate surface
(Pandey, 1992).
6.3.2.2 Partial purification and concentration
Following removal of biomass by centrifugation, the pectinase and xylanase produced
by Mucor hiemalis were purified by ammonium sulphate precipitation. An ammonium
sulphate fraction at 80% saturation resulted in a 95% and 76% yield for pectinase and
xylanase, respectively. The ammonium sulfate-enriched fraction of pectinase and
xylanase were further concentrated to 14-fold and 12-fold respectively by
ultrafiltration (Table 6.4).
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Table 6.4. Summary of purification of pectinases and xylanases
* Where: PE = Pectinases, and XY = Xylanases.
6.3.2.3 Effect of temperature and pH on enzyme activity
Figure 6.2 depicts the effect of different temperatures and pH on the enzyme activity
of the enriched pectinase and xylanase from Mucor hiemalis .
Figure 6.2. Effect of temperature, and pH on pectinase and xylanase activity.
The pectinase activity steadily increased with an increase in the temperature up to 50°C
and pH up to 5.0, with no significant increase at 60°C and pH 6.0, before declining at
70°C and pH 7.0 or higher. Contrasting with this, the xylanase activity steadily
increased with an increase in the temperature up to 60°C and pH up to 5.0 before
Purification
Step
Total Protein
(mg)
Total Activity
(U)
Specific Activity
(U/mg)
Yield
(%)
Purification
(fold)
PE XY PE XY PE XY PE XY PE XY
Crude 289.51 214.14 2057.72 996.25 4.65 4.65 100 100 1 1
(NH4)2SO4 230.07 136.83 1943.62 760.21 8.45 5.56 94.46 76.31 1.19 1.18
Ultrafiltration 15.62 12.51 1496.48 687.15 95.82 54.93 72.73 68.97 13.48 11.81
93
declining at 70°C and pH 6 or higher. This temperature-optima at 60°C is similar to
that reported for xylanase obtained from Penicillium glabrum (Knob et al., 2013) and
Trichoderma harzianum (Ahmed et al., 2012), as well as the pectinase obtained from
Aspergillus fumigatus (Okonji et al., 2019). However, slightly higher optimal
temperatures have been reported for other xylanases, such as 70°C for the purified
xylanase from Rhizomucor pusillus (Robledo et al., 2016) and Chaetomium
thermophilum (Ahmed et al., 2012). Moreover, slightly lower optimal temperatures
have been reported for other pectinases, such as 55°C for the purified
polygalacturonase from Rhizomucor pusillus (Siddiqui et al., 2012) and 50°C for the
purified polygalacturonase from Aspergillus niger MCAS2 (Khatri et al., 2015).
These results are in accord with that reported by Gautam et al. (2018) that maximum
activity of the xylanase from Chizophyllum commune ARC-11 was at pH 5. Similar
results have been reported for polygalacturonase from Rhizomucor pusillus (Siddiqui
et al., 2012), as well as xylanases by Fusarium sp. BVKT R2, and Trichoderma
harzianum (Khatri et al., 2015). However, slightly higher optimal pH values have been
reported for other pectinases and xylanases, such as pH 6.0 for pectinase from
Neurospora crassa (Polizeli et al., 1991), or xylanase from Chaetomium
thermophilum (Ahmed et al., 2012) and Rhizomucor pusillus SOC-4A (Robledo et al.,
2016). Moreover, pH values of up to 6.5 have been reported as the optimum pH for
xylanase (Terrone et al., 2018), and pectinase (Rasheedha Banu et al., 2010) from
Penicillium chrysogenum. On the other hand, lower optimal pH has been reported for
other xylanases, such as pH 3.0 for the xylanase from Penicillium glabrum (Knob et
al., 2013).
94
6.4 Conclusion
When the pretreated BSG was used for xylano-pectolytic enzyme production by
Mucor hiemalis under optimized conditions (15g biomass/250ml conical flask, pH 6.0,
and temperature of 30°C), the enzyme activity was 137 U/g, and 67 U/g BSG, for
pectinases and xylanases, respectively. These results suggest that microwave
pretreatment is a viable technology for increased valorisation of BSG, and Mucor
hiemalis was a potential producer of pectinases and xylanases using BSG fermentation.
The partially purified and concentrated pectinase and xylanase were optimally active
at 60°C and pH 5.0 (1602 U/ml of pectinase, and 839 U/ml of xylanase, respectively).
Further work on immobilisation of the produced enzymes is discussed in the following
chapter.
95
General conclusions and future work plan
Section III
Immobilization of Enzymes
Enzymes once suspended in an aqueous reaction medium are almost impossible to
retrieve or recycle. Enzyme immobilisation is the process of attaching an enzyme
molecule to a solid support with intentions of its reuse, production, and purification
without loss in their catalytic activity thereby dramatically improving process
economy. The binding to a support material can be temporary or permanent
depending upon the chemical bond formed between the enzyme and the support.
Covalent binding method basically fixes the enzyme molecule on to the substrate on
a permanent basis through strong covalent bonds. This solves the problem of
leaching of the enzyme found in adsorption-based biocatalysts when suspended in
aqueous media. With the problem leaching solved, covalently immobilised enzymes
can be used to great effect in reaction mixtures that are aqueous in nature. Proper
immobilisation of an enzyme on to a support is dependent on the properties of the
enzyme and carrier material. This section investigates the use of different carriers
for enzyme immobilisation.
96
Chapter 7
Magnetic nanoparticles as carrier
for enzymes immobilization
In this chapter, superparamagnetic iron oxide nanoparticles (MNPs) were synthesized
via exposure of fungal cell filtrate from Aspergillus flavus to aqueous iron ions. The
morphology of MNPs was found to be flakes-like, as confirmed by Field Emission
Scanning Electron Microscopy (FESEM), while the average crystallite size was ~16
nm, as determined by X-ray diffraction (XRD). Energy dispersive X-ray (EDX)
analysis was performed to confirm the presence of elemental Fe in the sample.
Pectinase and xylanase were covalently immobilized on MNPs with efficiencies of
~84% and 77%, respectively. Furthermore, the residual activity of the immobilized
enzymes was about 56% for pectinase and 52% for xylanase, after four and three
consecutive use cycles, respectively.
97
7.1 Introduction
Nanoparticles (1–100 nm in size) have gained prominence in the modern era, as they
constitute the building blocks for tailored nanocomposites that can be exploited in a
wide range of novel applications. In 2017, nanocomposites had a market value
amounting to nearly $2 billion (USD) worldwide, and this is projected to increase to
over $7 billion (USD) by 2022 at a compounded annual growth rate (CAGR) of 29.5%
during the forecast period of 2017 to 2022 (BCC Research, 2019). Driven by global
environmental challenges, the industrial revolution in the 21st century is likely to be
based on renewable biological resources. In this regard, bio-nanotechnology has
emerged as a promising field of research that builds on nanotechnology and
biotechnology. Interestingly, the 2016 Nobel Prize in chemistry went to Stoddart,
Sauvage and Feringa for design and synthesis of nanoscale machines (The Royal
Swedish Academy of Sciences, 2016), which exemplifies the high relevance of this
field.
Immobilized enzymes are of prime importance for some commercial processes, with
the key advantage of biocatalyst recycling often being complemented by higher
operational stability, and a lower risk of product contamination with enzyme (Reis et
al., 2019). However, depending on the immobilization method employed, such
advantages can be offset by additional process costs, up to 25 times that of the free
enzyme (Rasor, 2008).Therefore, there is considerable interest in exploring the
potential of nanoparticles as a low-cost platform for enzyme immobilization. Of all
the nanoparticles studied so far, magnetic nanoparticles (MNPs) are of interest for
enzyme immobilization owing to their ease of separation in a reaction and chemical
inertness (Bilal et al., 2018).
98
Moreover, the advent of microbially-produced MNPs has created a paradigm shift in
enzyme immobilization technology, providing favorable process economics and
offering the additional advantage of mild reaction conditions for their production
(Cueva and Horsfall, 2017; Moon et al., 2010). Therefore, microbial production of
nanoparticles is rapidly becoming a favoured approach in this field (Verma et al.,
2019). In this respect, filamentous fungi have been used chiefly in the synthesis of
metal nanoparticles due to their wide array of extracellular enzymes and high metal
tolerance (Silva et al., 2016).
A review of the literature in this field indicates a degree of heterogeneity in terms of
fungal species being investigated in the biosynthesis of magnetic nanoparticles (e.g.
Fusarium oxysporum (Bharde et al., 2006), Fusarium incarnatum (Mahanty et al.,
2019a), Trichoderma asperellum (Mahanty et al., 2019a), Phialemoniopsis ocularis
(Mahanty et al., 2019a), Verticillium sp. (Bharde et al., 2006), Aspergillus niger
(Abdeen et al., 2016; Chatterjee et al., 2020), Aspergillus oryzae (Tarafdar and Raliya,
2013), and Aspergillus japonicus (Bhargava et al., 2013)). However, to the best
knowledge of the authors, no work in this field has yet been reported using Aspergillus
flavus for synthesis of MNPs. Therefore, the present study investigates the use of A.
flavus to produce MNPs and explores their application in enzyme immobilization. To
date, MNPs have been extensively studied for immobilization of pectinase (Dal Magro
et al., 2019; Fang et al., 2016; Kharazmi et al., 2020; Mosafa et al., 2014; Nadar and
Rathod, 2018; Sojitra et al., 2017) and xylanase (Amaro-Reyes et al., 2019; Hamzah
et al., 2019; Kumar et al., 2018; S. Kumar et al., 2016; Kumari et al., 2018; Landarani-
Isfahani et al., 2015; Liu et al., 2014; Mehnati-Najafabadi et al., 2019; Singh et al.,
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2018) for easy separation and reuse. Therefore, pectinase and xylanase were used as
model enzymes in this study for immobilization on MNPs.
7.2 Methodology
Pectinase (912 U/ml) and xylanase (455 U/ml) were produced from Mucor hiemalis
AB1 (GenBank accession number: JQ912672.1) as described in chapter 6. Enzyme
activity was determined by the dinitrosalicylic acid (DNS) method of Miller (1959)
using pectin (citrus peel) or xylan (beechwood) as standard. Unless stated, all other
chemicals including pectin, xylan, ferric chloride, ferrous chloride, glutaraldehyde
solution, Sabouraud dextrose broth and agar were purchased from Sigma Aldrich
(Ireland), and were of analytical grade.
7.2.1 Isolation of Aspergillus sp. SR2
The fungus, Aspergillus sp. SR2, was isolated from spoiled strawberry (variety:
Rociera) as descripted in chapter 6. In the latter, the isolated strain was preliminarily
identified to the genus level based on macroscopic morphology and microscopic
features. The isolate was stored at 4°C on SDA slopes (Sabouraud dextrose agar; 40
g/l dextrose, 10 g/l mycological peptone, and 15 g/l agar) for further study.
7.2.2 Molecular Identification of Aspergillus sp. SR2
The molecular identification of the isolate was performed according to the procedure
mentioned in section 3.1.5.
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7.2.3 Biosynthesis of magnetic nanoparticles
The protocol reported by Mahanty et al. (2019) was followed with minor
modifications. Briefly, the fungal isolate was grown in potato dextrose broth (PDB;
250 ml Erlenmeyer flask with a working volume of 20%) with orbital shaking (120
rpm, 25 ◦C). After 14 days of incubation, the culture fluid was separated from fungal
biomass by filtration through Whatman filter paper no. 1, followed by centrifugation
at 5,000 rpm for 5 min. The final filtrate (10 ml) was later utilized for the reduction of
iron salts in aqueous solution (5 ml) consisting of 2 mM Iron (III) and 1 mM Iron (II)
chloride mixture (2:1). The reaction mixture was agitated for 5 minutes at room
temperature, and color changes were observed and considered as a positive result for
the development of MNPs. Next, the MNPs were collected by centrifugation at 12,000
rpm for 15 min and washed three times with deionized water. Finally, MNPs were
obtained after magnetic separation and immediately re-dispersed in deionized water
and sonicated (Fisher Scientific TI-H-10 Ultrasonic bath, output 750 W) for 1 hour to
separate the MNPs into individual particles. The culture filtrate (without iron salts) and
salt solution (without culture filtrate) were considered as positive and negative
controls, respectively.
7.2.4 Characterization of MNPs
The absorption spectra of MNPs were measured in the wavelength range from 200 to
800 nm using a UV-1800 UV-VIS spectrophotometer (Shimadzu Scientific
Instruments, Columbia, USA). Powder X-Ray diffraction (XRD) analysis was carried
out with a Siemens D500 diffractometer (Berlin, Germany) to study the crystal
structure of the MNPs. XRD data was analyzed using the Origin Pro 8 software (Trial
version, Microcal Software Inc., USA), and the average crystallite size of the MNPs
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was estimated using the Scherrer equation (Scherrer, 1918). The morphology (particle
shape) of the MNPs were observed using a Hitachi SU 70 (Hitachi High Technologies,
Japan) field emission scanning electron microscope (FESEM). The FESEM system
was equipped with an X-Max silicon drift detector (Oxford Instruments, UK)
for energy dispersive X-ray (EDX) analysis to confirm the presence of iron in the
particles, as well as to characterize the other elementary composition of the particles.
7.2.5 Immobilization of biocatalysts on MNPs
A two-step procedure was followed to immobilize pectinase and xylanase on MNPs
adopted from previously reported methods, with minor modifications (Ghadi et al.,
2015; Kaur et al., 2020). The first step was the activation of the MNPs (50 mg) by
suspension in glutaraldehyde solution (1.0 M, 5 ml) for 2 h at 37°C under constant
mechanical stirring. The next step involved the covalent binding of the glutaraldehyde-
activated MNPs (50 mg, MNPs@GLU) to pectinase or xylanase (1 mL) by incubating
the suspension for 24 h at 37°C under constant mechanical stirring. Finally, enzyme-
functionalized MNPs (MNPs@GLU-ENZ) were obtained after magnetic separation
and washed with deionized water to remove loosely or unbound proteins. The
immobilization yield (Kumari et al., 2018), and activity recovery (Zhou et al., 2013)
of immobilized pectinase or xylanase, were calculated as follows:
Immobilization yield (%) =(C𝑖𝑛𝑖𝑡 − C𝑓𝑖𝑛)
C𝑖𝑛𝑖𝑡 x 100
where Cfin is the amount of protein in the supernatant (enzyme solution) after magnetic
separation and Cinit is the total protein available for immobilization, as determined by
the Bradford assay (Bradford, 1976).
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Activity recovery (%) =𝐴𝑖𝑚𝑚𝑜𝑏
A𝑖𝑛𝑖𝑡 x 100
where Aimmob is the immobilized enzyme activity and Ainit is the initial (free) enzyme
activity as determined by the Miller DNS assay (Miller, 1959).
7.2.6 Evaluation of the nano-immobilized enzymes (MNPs@GLU-ENZ)
The optimum reaction pH of MNPs@GLU-ENZ was determined over the range
between 2.0 and 11.0 using glycine-HCL buffer (0.1 M, pH 2.0), citrate buffer (0.1 M,
pH 3.0-6.0), phosphate buffer (0.1 M, pH 7–8) and glycine-NaOH buffer (0.1 M, pH
9–11);While the optimum temperature was investigated between 20 °C and 80 °C. To
evaluate the storage stability, MNPs@GLU-ENZ were held for 30 days at 4 °C (Xiao
et al., 2016). For a reusability assessment, MNPs@GLU-ENZ were recovered with
magnetic separation after each cycle of use, washed with deionized water, and then a
new cycle was run under the same conditions for a total of 6 cycles (Mosafa et al.,
2014). The enzyme activity in the first cycle was assigned a relative activity of 100%
(at temperature of 60 °C and pH of 5), and relative activity was calculated for the
successive cycles. All experiments were performed in triplicate.
7.3 Results and discussion
7.3.1 Molecular Identification of Aspergillus sp. SR2
Aspergillus isolate used in the current study was previously isolated in our lab from
spoiled strawberry (variety: Rociera). Molecular identification of fungal isolate was
carried out by analyzing the sequences of the ITS region of ribosomal DNA (ITS1-
5.8S-ITS2) and pairwise sequence alignment using the BLAST algorithm. The
resultant nucleotide sequence in this study (Table 7.1) matched 100% with the
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published sequence of Aspergillus flavus (Accession No. MN238861.1) available in
GenBank. The aforementioned taxon is one of the most common fungi that are found
on strawberry fruit, as previously reported (Saleem, 2017).
Table 7.1. Nucleotide sequences of Aspergillus flavus SR2
1 GAGGAAGTAA AAGTCGTAAC AAGGTTTCCG TAGGTGAACC TGCGGAAGGA TCATTACCGA
61 GTGTAGGGTT CCTAGCGAGC CCAACCTCCC ACCCGTGTTT ACTGTACCTT AGTTGCTTCG
121 GCGGGCCCGC CATTCATGGC CGCCGGGGGC TCTCAGCCCC GGGCCCGCGC CCGCCGGAGA
181 CACCACGAAC TCTGTCTGAT CTAGTGAAGT CTGAGTTGAT TGTATCGCAA TCAGTTAAAA
241 CTTTCAACAA TGGATCTCTT GGTTCCGGCA TCGATGAAGA ACGCAGCGAA ATGCGATAAC
301 TAGTGTGAAT TGCAGAATTC CGTGAATCAT CGAGTCTTTG AACGCACATT GCGCCCCCTG
361 GTATTCCGGG GGGCATGCCT GTCCGAGCGT CATTGCTGCC CATCAAGCAC GGCTTGTGTG
421 TTGGGTCGTC GTCCCCTCTC CGGGGGGGAC GGGCCCCAAA GGCAGCGGCG GCACCGCGTC
481 CGATCCTCGA GCGTATGGGG CTTTGTCACC CGCTCTGTAG GCCCGGCCGG CGCTTGCCGA
541 ACGCAAATCA ATCTTTTTCC AGGTTGACCT CGGATCAGGT AGGGATACCC GCTGAACTTA
601 AGCATAT
7.3.2 Biosynthesis of magnetic nanoparticles
Biosynthesis of MNPs using culture filtrate of A. flavus was initiated rapidly after
treatment with iron salts, which was visualized by observing the change in color of the
reaction mixture from yellow to black (Fig. 7.1), while no visible color change was
observed for either positive or negative controls.
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Figure 7.1. Shows the culture filtrate of A. flavus (A), iron salts solution (B),
black coloration due to mixing of solutions A+B for 5 min (C), separation of
MNPs from reaction mixture using an external magnet (D) , and the magnetic
behavior of dried MNPs in the presence of a magnet (E).
Few studies to date have investigated the capability of fungi for the production of
magnetic nanoparticles, and most of these studies have focused on Aspergillus species,
such as Aspergillus niger (Abdeen et al., 2016; Chatterjee et al., 2020), Aspergillus
oryzae (Tarafdar and Raliya, 2013), and Aspergillus japonicus (Bhargava et al., 2013).
In the aforementioned studies on Aspergillus species, different iron salts were utilized,
such as potassium ferricyanide and potassium ferrocyanide salts solution (Bhargava et
al., 2013), ferrous sulfate and ferric chloride salts solution (Abdeen et al., 2016;
Chatterjee et al., 2020), and ferric chloride solution (Tarafdar and Raliya, 2013). Thus,
our study brings a novel approach for the biosynthesis of MNPs by Aspergillus species
where filtrate of A. flavus reduced the ferrous chloride and ferric chloride salts
solution. According to the available literature, the exact mechanism for the
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biosynthesis of metal oxide NPs by fungi has not yet been elucidated. However, the
biomineralization process could have occurred via reducing iron metal ions by
extracellular redox enzymes from A. flavus (Dorcheh and Vahabi, 2016).
7.3.3 Characterization of MNPs
The detailed characterization of the mycosynthesized MNPs was performed using
UV–vis spectrophotometry, Field Emission Scanning Electron Microscopy (FESEM),
Energy dispersive X-ray (EDX) and X-ray diffraction (XRD). The UV-vis absorption
spectra (Fig. 7.2) show a characteristic absorption band at 310 nm, confirming the
formation of MNPs (Saranya et al., 2017).
Figure 7.2. UV-vis spectrum of MNPs.
Furthermore, the EDX spectrum and elemental mapping as shown in Fig. 7.3 detected
the presence of Fe and O, which indicated the stoichiometric formation of Fe3O4
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nanoparticles. The Fe and O element mapping further confirmed that the two
constituent elements were well-distributed over the sample. However, the spectrum
also revealed small traces of impurities which presumably originated from the iron
salts and the A. flavus filtrate used for the synthesis of MNPs (Rahman et al., 2017).
Figure 7.3. MNPs observed in EDX spectrum (A), electron micrograph region
at 200 µm (B), distribution of Fe in elemental mapping (C), and distribution of
O in elemental mapping (D).
The SEM images (Fig. 7.4) of MNPs show clusters of flakes-like morphology. Similar
morphology was also observed by Chatterjee et al. (2020), who studied MNPs
produced by Aspergillus niger BSC-1. Bharde et al. (2006) also reported the
biosynthesis of irregular MNPs using Fusarium oxysporum. The observed flakes-like
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morphology could be due to the binding activity of biomolecules of A. flavus on the
growing particles of Fe3O4 (Kavitha et al., 2017). Additionally, there is a possibility
of improper drying of the MNPs using the vacuum drying technique (GeneVac EZ-2
Plus evaporator, Genevac Ltd, Ipswich, UK). Thus, one possible solution would be to
use vacuum freeze-drying of liquid nanoparticle dispersions to produce lyophilized
MNP powder which may minimize aggregate formation, that could that lead to high
or modified particle size distribution. Above all, MNPs tends to agglomerate into
larger clusters in order to reduce their surface energy (Campos et al., 2015), in a
phenomenon known as Ostwald ripening (Huang et al., 2014). Thus, XRD was used
to estimate the average crystallite size of individual (primary) particles .
Figure 7.4. SEM micrograph shows irregular flakes-like clusters.
Fig. 7.5 shows the XRD patterns for MNPs with six characteristic peaks for Fe3O4
marked by their indices (220), (311), (400), (422), (511), and (440). The results of
phase analysis agree with the reference magnetite Fe3O4 pattern from the Joint
Committee on Powder Diffraction Standards (JCPDS card number 19-0629), and
published literature (Fatima et al., 2018; Yew et al., 2017). Due to the size of the
small crystallites, only one major peak at 35.3° corresponding to the (311) plane was
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detected, while other peaks for magnetite were less distinctive (Loh et al., 2008).
Therefore, the full width at half maximum (FWHM) of the (311) peak was used to
calculate the crystallite size. The average crystallite size of the MNPs was ~16 nm, as
estimated by the Scherrer equation. Comparisons with the literature revealed that
MNPs derived from fungi were in the size range of 10 to 40 nm (Bhargava et al., 2014;
Chatterjee et al., 2020). MNPs smaller than the critical size of 20 nm are known to be
superparamagnetic, with a high magnetization value (Namanga et al., 2013). The
aforementioned superparamagnetic iron oxide nanoparticles are of industrial interest
due to their low toxicity, and good biocompatibility (Xu et al., 2014). Thus,
superparamagnetic iron oxide nanoparticles are versatile carriers for realizing enzyme
immobilization that facilitates catalyst separation.
Figure 7.5. XRD spectrum of MNPs.
7.3.4 Immobilization of biocatalysts on MNPs
Direct immobilization of pectinase and xylanase on MNPs is not effective because of
surface oxidation and rejection (Masi et al., 2018). Thus, MNPs were subjected to
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glutaraldehyde treatment for the activation of MNPs which facilitates the formation of
the Schiff base linkage between the aldehyde group of glutaraldehyde and the terminal
amino group of enzymes (Costa et al., 2016). Finally, the activated MNPs
(MNPs@GLU) were treated with pectinase or xylanase individually to produce
enzyme-functionalized MNPs (MNPs@GLU-ENZ). Immobilization yields of
pectinase and xylanase functionalized MNPs were about 84% and 77%, respectively.
For pectinase, the immobilization yield of this work (84%) was much higher than that
(51%) previously reported by Mosafa et al. (2014) using silica-coated magnetite
nanoparticles via a sol–gel approach. The use of the latter may result in composite
particles with an ill-defined structure due to the formation of aggregates (Chung et al.,
2009), and this may affect the enzyme immobilisation yield. Another reason could be
that these enzymes are of different origin, and therefore are most likely to be
structurally different. It follows that the immobilisation yield would most probably be
different. This may also be the reason why a previous study reported a higher yield
(92%) of xylanase from Bacillus sp. NG-27 when immobilized on glutaraldehyde
activated magnetic nanoparticles (Kumari et al., 2018). It is known that a wide number
of factors can affect the immobilization yield using this method, including surface area
activation, stirring speed, enzyme load, MNP proportion, and enzyme-MNP coupling
time (Costa et al., 2016).
The recovery of enzyme activity remaining in immobilized pectinase and xylanase
were about 74% and 68%, respectively. For pectinase, the activity recovery of this
work (74%) was much lower than that (92%) previously reported by Nadar and Rathod
(2018) using amino-activated magnetite nanoparticles, and the 85% activity recovery
reported by Muley et al. (2018) using glutaraldehyde-activated magnetic
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nanoparticles. Also, the latter study reported higher activity recovery of xylanase
(76%) compared to the activity recovery of xylanase (68%) in the present work.
However, Perwez et al. (2017) reported similar xylanase activity (69%) when
immobilized on 3-aminopropyl triethoxysilane-activated magnetic nanoparticles. The
use of 3-Aminopropyl triethoxysilane (APTES) for particle functionalization prior to
glutaraldehyde cross-linking (Muley et al., 2018; Nadar and Rathod, 2018) has
previously proved to be a viable approach to achieve improved enzyme
immobilisation. However, we tried to minimize the use of toxic compounds to achieve
a ‘greener’ immobilisation process, and hence silanization by APTES was avoided. It
is worth noting that covalent immobilization may lead to changes in the native
structure of the enzymes, causing serious loss of enzymatic activity (Cen et al., 2019).
Moreover, improving the covalent immobilization method would not only ultimately
increase the activity recovery, but also suppress the enzyme leaching from the surface
of MNPs (Rehm et al., 2016).
7.3.5 Evaluation of the nano-immobilized enzymes (MNPs@GLU-ENZ)
The effect of temperature on the activity of free and immobilized enzymes was
evaluated by assaying the enzyme activity at different temperatures (20 - 80°C) and
pH 5.0. The results shown in Fig. 7.6a indicated that immobilization did not change
the optimal temperature of pectinase and xylanase (50°C and 60°C, respectively). This
agrees with the typical temperatures for optimum activation of commercial pectinase
(50 °C) and xylanolytic thermostable enzymes (60 °C). Additionally, the immobilized
pectinase and xylanase retained 80% activity over a wider temperature range of 30-
80°C and 40-80°C, respectively, compared to the free enzymes (retained more than
80% activity over range of 30-70°C and 50-70°C, respectively). The improved
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retained enzyme activity of the immobilized enzymes above the optimum temperature,
with no shift in the optimum temperature, might be ascribed to the covalent bonding
to MNPs, which in turn prevented the conformational change of enzymes at high
temperature (Muley et al., 2018).
Figure 7.6. Panels (A–C) shows the effect of temperature (A), pH (B), storage time
(C), and recycle count (D) on relative activity of immobilized pectinase and
xylanase in comparison with free enzyme.
The influence of pH on the activity of free and immobilized enzymes was studied by
assaying at varying pH values ranging from 2.0 to 11.0 at 50ºC. The results shown
in Fig. 7.6b indicated that immobilization did not change the optimal pH (5.0) of
pectinase and xylanase. This agrees with the typical pH (5) for optimum activation of
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commercial pectinase and xylanase. Moreover, the pH activity scope of the
immobilized pectinase was expanded, retaining more than 80% activity over a wider
pH range of 4.0–8.0, compared with that of the free form (pH range of 5.0-7.0). One
possible reason for this could be the intact conformational structure of enzyme in the
polymeric network of nanocarrier (Kharazmi et al., 2020). On the other hand, the
optimal pH of immobilized xylanase was slightly shifted toward the neutral range
compared to the free form. Immobilized xylanase retained more than 80% activity in
a pH range of 4.0–7.0 compared with that of the free form (pH range of 3.0-6.0). The
slight shift of pH activity for immobilized xylanase might be attributed to partitioning
effects of polyionic matrices (Liu et al., 2014).
Storage stability of the free and immobilized enzymes was evaluated at 5-day intervals
by storing them at 6 °C for 30 days. As shown in Fig. 7.6c, the immobilization with
magnetic nanoparticles improved the stability of enzymes. The immobilized pectinase
and xylanase retained about 56% and 53% of their initial activity after 30 days and 25
days, respectively. This compares with the free pectinase and xylanase, which retained
about 43% and 49% of their initial activity after 25 days and 20 days, respectively.
These results indicated that the immobilization with magnetic nanoparticles improved
conservation and stabilization of the structural conformation of enzymes which
preventing possible distortion effects on the active sites of enzyme (Sojitra et al.,
2017).
From a process economics perspective, the reusability aspect of immobilized enzymes
is a key consideration for industrial applicability. Thus, the useful lifetime of
immobilized enzymes in the present work was assessed by subjecting the preparations
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to 6 successive process cycles (Fig. 7.6d). After one cycle was over, the immobilized
enzymes were removed from the reaction medium with an external magnetic field and
reused again to check durability. The residual activity of immobilized enzymes was
56% for pectinase and 52% for xylanase, after four and three consecutive cycles,
respectively. The activity loss of immobilized enzyme probably could be due to
enzyme denaturation due to recurrent encountering of substrate to the active site of
immobilized enzyme after each successive use (Sahu et al., 2016). Besides, the leakage
of enzymes from the MNPs, or probably the weak magnetization of MNPs due to the
high temperature or being coated by components from the reaction mixture (Mahanty
et al., 2019b). In fact, previous studies have shown that the activity of hydrolytic
enzymes immobilized on MNPs tends to drop down to 20% after the second reuse
(Amaro-Reyes et al., 2019). One solution could be encapsulation of MNPs by chitosan
not only for functionalization of MNPs but also to minimize leaching of MNPs into
the reaction medium (Parandhaman et al., 2017). In such a case, enzymes can be
immobilized on the surface of chitosan and achieve the magnetic separation and
recycling of the immobilized enzymes from the reaction medium.
7.4 Conclusion
Superparamagnetic magnetite nanoparticles were biosynthesized using A. flavus. The
particles were found to be flakes-like, with average crystallite size of ~16 nm.
Pectinase and xylanase were individually immobilized on the surface of MNPs. The
immobilized enzymes were more stable than the free ones at wider ranges of pH and
temperatures. Furthermore, reusability and storage stability of the enzymes were
improved by immobilization. Despite these promising results, application of MNPs
nowadays in the food industry is challenged by the current controversy around
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potential toxicity and possible health effects due to the risk of product contamination
with MNPs. However, considering the promising results in this chapter, the next
chapter investigates safer alternatives from natural resources to replace MNPs (herein
used a carrier) and glutaraldehyde (herein used a cross linking agent). Such approach
opens route for numerous potential applications in the food industry, in particular for
fruit juice clarification .
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Chapter 8
Alginate hydrogel beads as carrier
for enzymes immobilization
In this chapter, immobilization of pectinase and xylanase from m. hiemalis on genipin-
activated alginate beads was optimised, followed by the application of immobilised
enzymes in apple juice clarification. Optimum activity recovery obtained were about
81 % and 83 % for pectinase and xylanase, respectively. Optimum enzyme load and
genipin concentration were found to be 50 U/ml, and 12 % (w/v), respectively. Using
a coupling time up to 120 min, agitation rate of 213 rpm for pectinase and 250 rpm
for xylanase, maximum activity recovery was observed. Beads prepared at optimum
immobilization conditions were suitable for up to 5 repeated uses, losing only about
45% (for immobilized pectinase) and 49% (for immobilized xylanase) of their initial
activity. The maximum clarity of apple juice (%T660, 84%) was found to be taking 100
min when pectinase (50 U/ml of juice) and xylanase (20 U/ml of juice) were used in
combination at 57°C.
Work described in this chapter has been submitted as a peer review article:
Computational modelling approach for the optimization of apple juice clarification
using immobilized pectinase and xylanase enzymes. Current Research in Food
Science, (2020). https://doi.org/10.1016/j.crfs.2020.09.003).
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8.1 Introduction
Unlike animal counterparts, plant cells are surrounded by an extracellular matrix
known as the cell wall which comprises polysaccharide and protein polymers. Protein
accounts for only 5-10% of this structure, whereas polysaccharides constitute 90-95%
of the cell wall (Jacq et al., 2017). Such polysaccharides are predominantly pectin,
hemicellulose, and cellulose (Broxterman and Schols, 2018). In a typical cell of
hardwood, such as apple, the wall possesses high pectin levels, and the predominant
hemicellulose fraction is xylan (Donev et al., 2018). However, structural non-
cellulosic polysaccharides, such as pectin and xylan, are indigestible by human
digestive enzymes in the upper gastrointestinal tract (Tappia et al., 2020).
Additionally, the presence of colloidal particles of pectin and xylan results in an
undesirable cloudiness in apple juice (Kuddus, 2018). Hence, pectinase and xylanase
enzymes have been commonly applied to clarification in the apple juice industry
(Garg et al., 2016; Nagar et al., 2012; Sharma et al., 2017).
A key concern with the use of enzymes in cost-sensitive food processing operations is
the relative expense of such biocatalysts. In this context, immobilization technology
affords the key advantage of enzyme re-use, and can also enhance their operational
and/or storage stability (Cao, 2011). Among various immobilization techniques
available, while the use of covalent binding to solid insoluble carriers has extensively
appeared in the literature (Novick and Rozzell, 2005), its use within the food sector is
relatively under-developed.
The main advantage of the covalent approach is the strength of enzyme binding to a
solid phase, theoretically minimizing in-process enzyme leachate from the carrier
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(Mohy Eldin et al., 2011). Natural polymers such as alginate beads have received
considerable attention due to their potential applications in the food and
pharmaceutical industries (Martău et al., 2019). The continued search for adoption of
natural materials in food processing has also pointed to the exploitation of genipin
(from gardenia fruit Gardenia jasminoides) as a potentially safer alternative to the
conventional crosslinker, glutaraldehyde, for activation of alginate beads prior to
covalent binding to enzymes (Tacias-Pascacio et al., 2019). To the best of our
knowledge, enzyme immobilization with genipin-activated alginate beads for juice
clarification has received little attention. Thus, this work investigates the covalent
coupling of pectinase and xylanase to genipin-activated alginate beads for application
in apple juice clarification. The immobilized enzyme preparations were subsequently
evaluated in terms of reusability and operational-storage stability.
An artificial neural network was employed to achieve the maximum activity recovery
(%) of pectinase and xylanase, as well as maximum apple juice clarification using the
immobilized enzymes. Multiple studies have demonstrated that computational
modeling (e.g. artificial neural network) is more accurate than statistical modeling (e.g.
response surface methodology) in enzyme immobilization and juice clarification
processes (Talib et al., 2019; Youssefi et al., 2009). For instance, trained ANN models
have been successfully employed to optimize the immobilization process of lipase
from Candida rugosa on Amberjet® 4200-Cl (Fatiha et al., 2013), and cellulase from
Trichoderma viride on Eudragit® L-100 (Zhang et al., 2012). Moreover, trained ANN
models have been successfully employed to optimize apple juice clarification by
ultrafiltration (Gökmen et al., 2009), suggesting that incorporation into the present
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study could be beneficial, the results of the algorithms were compared by minimized
root mean squared error (RMSE) and maximized coefficient of determination (R2).
8.2 Methodology
8.2.1 Enzymes
Pectinase (912 U/ml) and xylanase (455 U/ml) were produced from Mucor hiemalis
AB1 (GenBank accession number: JQ912672.1) as described in chapter 6. Enzyme
activity was determined by the dinitrosalicylic acid (DNS) method of Miller (1959)
using pectin (citrus peel) or xylan (beechwood) as standards. Unless stated, all the
chemicals used in this work were commercial products of analytical grade (Sigma-
Aldrich, Ireland).
8.2.2 Enzyme immobilisation
8.2.2.1 Experimental design and data acquisition for ANN modelling
Optimizations were based on the protocol established by Khairudin et al. (2015).
Experimental design was carried out using STATGRAPHICS Centurion XV software
(StatPoint Technologies Inc. Warrenton, VA, USA). A four-factor-five-level central
composite rotatable design (CCRD) was used to evaluate the enzyme activity recovery
(%). The selected CCRD model consisted of four factors, viz. genipin concentration
(%) for alginate bead activation, enzyme loading (U/ml), coupling time (min), and
agitation rate (rpm). The factors and their levels were obtained through preliminary
tests and based on previous results from the literature (Pal and Khanum, 2011a). Table
8.1 summarizes the range and levels of the four factors.
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Table 8.1. Independence factors and corresponding levels for enzyme
immobilization
Code Factors Units levels
-α -1 0 +1 +α
A Genipin %, w/v 0 3 6 9 12
B Enzyme load U/ml 50 150 250 350 450
C Coupling time min 30 60 90 120 150
D Agitation rate rpm 50 100 150 200 250
The CCD contained 16 factorial points, 8 axial points, and 6 central points with α value
fixed at 2.0 for a total of 30 experiments, as shown in the model matrix (Section 8.3,
Table 8.3). Once the experiments were performed, the experimental dataset (30
experiments) were randomly divided into two sets - training set and testing set -
whereas experimental values at predicted optimum conditions were used as the
validating set.
8.2.2.2 Covalent immobilization of pectinase and xylanase
Initially, alginate beads were prepared by manually dropping sodium alginate solution
(3%, w/v) into the hardening solution (calcium chloride, 0.2 M) using a peristaltic
pump (Bhushan et al., 2015). The beads were collected using a filter funnel through a
Whatman® (No. 1) paper after 3 hours of gentle stirring on a magnetic stirrer, and
maintained in the gelling solution (CaCl2, 0.02 M) overnight at 4°C to harden.
Afterwards, the beads were washed with deionized water and further activated by
mixing with genipin solutions of varying concentrations, ranging from 3 to 12% (w/v)
in citrate buffer (0.05 M, pH 5.0), and gently stirred to ensure a homogeneous coating
of cross-linker. Finally, the beads were removed by filtration and washed with distilled
water to remove the unbound genipin.
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The resulting activated beads were used as carrier in enzyme immobilization
experiments. The immobilization of pectinase and xylanase was performed by orbital
mixing (50–250 rpm) of an equal volume (1:1 ratio) of enzyme solution (50–450
IU/ml) with activated beads for different durations (30–150 min). The beads were
removed by filtration and washed with distilled water until no enzyme activity could
be detected in the washings. The enzyme activity recovery (AR, %) was calculated
using the following equation (Zhou et al., 2013):
Enzyme activity recovery (%) = (A ÷ Ainit) x 100
where A is the activity of immobilized enzyme on beads and Ainit is the initial (free)
enzyme activity.
8.2.2.3 Evaluation of the immobilized enzymes
The optimum reaction pH of the immobilized enzymes was measured in the range
between 2.0 and 11.0 using glycine-HCl buffer (0.1 M, pH 2.0), citrate buffer (0.1 M,
pH 3.0-6.0), phosphate buffer (0.1 M, pH 7.0–8.0) and glycine-NaOH buffer (0.1 M,
pH 9.0–11.0); and the optimum temperature was measured in the range between 20 °C
and 80 °C. To evaluate the storage stability, immobilized enzymes were held for 30
days at 4 °C. For a reusability assessment, immobilized enzymes were recovered by
magnetic separation after each cycle of use and washed with deionized water, and then
a new cycle was run under the same conditions for a total of 6 cycles. The enzyme
activity in the first cycle was assigned a value of 100%, and relative activity was
calculated for the successive cycles. All experiments were performed in triplicate.
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8.2.3 Enzymatic treatment of apple juice
8.2.3.1 Experimental design and data acquisition for ANN modelling
A four-factor-five-level central composite rotatable design (CCRD) that required 30
experiments was used to evaluate the juice clarification (%). The selected CCRD
model consisted of four factors, viz. pectinase loading (U/ml of apple juice), xylanase
load (U/ml of apple juice), holding time (min), and temperature (°C). The factors and
their levels were obtained through preliminary tests and based on previous results from
the literature (Ravindran et al., 2019). Table 8.2 summarizes the range and levels of
the four factors.
Table 8.2. Independence factors and corresponding levels for clarification of
apple juice
Code Variables Units levels
-α -1 0 +1 +α
A Pectinase load U/ml of apple juice 10 20 30 40 50
B Xylanase load U/ml of apple juice 10 20 30 40 50
C Holding time min 40 60 80 100 120
D Temperature °C 40 45 50 55 60
The CCRD contained 16 factorial points, 8 axial points, and 6 central points, with α
value fixed at 2.0 for a total of 30 experiments, as shown in the model matrix (in
Section 8.3, Table 8.6). Once the experiments were performed, the experimental
dataset (30 experiments) were randomly divided into two sets - training set and testing
set - while experimental values at predicted optimum conditions were used as the
validating set.
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8.2.3.2 Clarification of apple juice using immobilized enzymes
Fresh apple fruits (Malus domestica) of Royal Gala variety (without any visual defects)
at commercial maturity were purchased from a local market (Dublin, Ireland). The
apples were washed with tap water, chopped into small pieces, and later macerated in
a domestic blender, without addition of water, until a homogenous juice was obtained.
The concentrated juice was then pasteurized for 1 hour at 60°C (Padma et al., 2017).
The filtered juice (pH 5.0) was used for the clarification studies.
Figure 8.1 illustrates the laboratory scale set up of a packed-bed reactor using a glass
column for enzymatic clarification of apple juice using the immobilized enzymes
(pectinase and xylanase) on alginate beads.
Figure 8.1. Schematic diagram of the experimental set-up
for clarification of apple juice
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The enzymatic clarification experiments were performed by subjecting 25 ml of apple
juice to different concentrations of pectinase and xylanase (10-50 U/ml of juice) for
varying duration of holding times (40–120 min) within the range of temperatures
between 40 °C and 60 °C. Finally, the enzyme beads were removed, and treated apple
juice centrifuged (10,000 rpm, 15 min), followed by filtration using Whatman no 1
filter paper, and this juice filtrate was used for further analysis. The clarity of juice was
expressed as percentage transmission (%T) that was determined using a UV-1800 UV-
VIS spectrophotometer (Shimadzu Scientific Instruments, Columbia, USA) at a
wavelength of 660 nm, and using distilled water as a reference (Dey and Banerjee,
2014).
8.2.4. Artificial Neural Network (ANN) modelling and analysis
The commercial artificial intelligence software, NeuralPower® (CPC-X Software,
version 2.5, Carnegie, PA, USA) was employed for ANN modelling and analysis. The
networks were trained in a supervised learning environment by different learning
algorithms (incremental back propagation, IBP; batch back propagation, BBP;
quickprob, QP; and Levenberg-Marquardt algorithm, LM). Multilayer normal feed-
forward was used to predict the response and the hyperbolic tangent function (a.k.a.
tanh) used as transfer function in the hidden and output layers. To determine the
optimal network topology, only one hidden layer with varying number of neurons was
used to develop different networks. The comparison between the models were assessed
using root mean square error (RMSE) and correlation coefficient (R2). The lower the
RMSE and the closer R2 is to 1, the more precise the model. Models were further
assessed using a testing dataset to predict the unseen data (data not used for ANN
training).
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For process optimization, three different optimization algorithms were employed,
namely rotation inherit optimization (RIO), particle swarm optimization (PSO), and
genetic algorithm (GA). After determination of optimum conditions, experimental
validation was carried out to calculate the percentage error between the experimentally
measured values and the ANN predicted value using the formula (Zhang et al., 2020)
as follows:
Error (%) = [(P’-P)/P] *100
where, P’ is the enzyme activity recovery predicted using ANN, and P is the
observed enzyme activity recovery measured in the experiment.
8.3 Results and discussion
8.3.1 Enzyme immobilisation
8.3.1.1 The ANN model training
A neural network with optimal number of neurons is required to avoid over- or
undertraining of the training dataset. If neurons are lower than the optimum range,
undertraining would result in a poor fit to the training dataset. On the other hand,
increasing the number of hidden neurons above the optimum range may lead to
overfitting, as the network may end up memorizing the training data. Although this
would result in very good fit to the training dataset, the model would have poor
generalization ability to handle testing and unseen datasets.
The larger subset (n=25) comprising more than 80% of the available experimental data
was used for the ANN training and model building. To determine the optimal topology
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for the networks, the number of neurons in the hidden layer was varied from 1 to 7.
Subsequently, the decision on the optimum topology was based on the minimum
RMSE (and the closer R2 to 1) of testing set values. Figure 8.2 illustrates the
performance of the network for training data versus of the number of neurons in the
hidden layer using different learning algorithms.
Figure 8.2. The performance of different learning algorithms (Incremental
backpropagation algorithm, IBP; Batch backpropagation algorithm, BBP; Quick
propagation algorithm, QP; and Levenberg-Marquardt algorithm, LM) for
training data versus of the number of neurons in the hidden layer for predicting
the activity recovery of pectinase (A) and xylanase (B) onto alginate beads.
According to the RMSE, the network with 3 hidden neurons produced the optimum
performance when any of the four algorithms (IBP, BBP, QP and LM) was employed.
Therefore, the optimum topology of the networks (Figure 8.3) was 4-3-1 (four neurons
in the input layer, three neurons in the hidden layer and one neuron in the output layer).
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Figure 8.3. The illustration of multilayer normal feed-forward neural network.
The neural network having three inputs of variables (genipin, enzyme load,
coupling time, and agitation rate), one hidden layer with three neurons (nodes)
and one output of response (enzyme activity recovery).
8.3.1.2 Selection neural network model
The model architecture of 4-3-1 was selected as the best topology for the four learning
algorithms. Moreover, as shown in figure 8.4, LM and QP were at maximum R2, while
its RMSE were at the lowest value in comparison with the other algorithms for
predicting the activity recovery (%) of pectinase and xylanase, respectively.
Figure 8.4. Comparison of different learning algorithms (Incremental
backpropagation algorithm, IBP; Batch backpropagation algorithm, BBP; Quick
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propagation algorithm, QP; and Levenberg-Marquardt algorithm, LM) with 6
neurons in the hidden layer for predicting the activity recovery of pectinase and
xylanase onto alginate beads.
Backpropagation is an extensively used family of supervised training algorithms based
on the error-correction learning rule and can be implemented in either incremental or
batch mode (Kahraman, 2012). Backpropagation algorithm has been improved for a
faster training process (‘quick propagation- QP- algorithm’; Awolusi et al., 2018), and
enhanced performance ( ‘Levenberg Marquardt – LM - algorithm’; Reddy et al., 2018).
It was reported that QP gave the best performance in modeling the enzymatic synthesis
of betulinic acid ester when compared with IBP, BBP, and LM (Moghaddam et al.,
2010). On the other hand, Adnani et al. (2011) employed the LM algorithm for lipase-
catalyzed synthesis of sugar alcohol ester. It is worth noting that there is no ideal
algorithm per se that will give the best results in the training of any dataset, and the
result of training is highly dependent on the architecture of the network, the training
algorithm, the size of training dataset and data noise levels (Jacobsson, 1998).
Table 8.3 displays the ANN predicted values for the training datasets using LM-4-3-1
for pectinase activity recovery and QP-4-3-1 for xylanase activity recovery. The results
revealed the close correlation between the experimental and the predicted values. The
R2 and RMSE metrics were used to evaluate the developed models. The R2 value was
0.99 for both models, where RMSE values were 1.37 and 1.48 for pectinase and
xylanase immobilization models, respectively. The obtained R2 of the two models is
very close to 1, indicating a good adjustment between the observed and predicted
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values. Moreover, the obtained low RMSE values of the two models did not show
significant disparity, indicating relatively similar performance.
Table 8.3. Experimental design showing the observed and predicted values of
enzyme (pectinase or xylanase) activity recovery (%) as output for training
dataset.
Run
Independent Variables Response
A B C D
Enzyme activity recovery (%)
Pectinase Xylanase
Observed Predicted Observed Predicted
Training Data
1 3 150 60 100 18.66 16.96 16.86 16.77
2 3 150 120 200 62.25 63.77 61.51 61.52
3 6 250 90 150 31.04 31.93 30.81 31.95
4 6 50 90 150 60.44 60.37 61.12 61.30
5 12 250 90 150 42.95 42.95 41.16 42.49
6 9 350 120 200 25.49 25.85 26.24 26.08
7 9 350 60 100 36.24 36.04 34.66 35.14
8 3 350 120 200 31.89 31.55 29.22 28.00
9 3 150 60 200 26.98 25.53 26.99 26.28
10 9 350 120 100 36.49 36.21 35.91 34.26
11 6 250 30 150 30.64 30.67 28.48 25.97
12 3 350 120 100 20.56 20.56 21.94 21.50
13 9 150 60 100 42.5 44.18 44.35 46.67
14 6 450 90 150 18.83 18.83 16.3 16.85
15 0 250 90 150 10.41 12.34 12.00 13.50
16 6 250 90 150 32.01 31.93 31.27 31.95
17 3 350 60 100 15.92 13.37 17.28 20.27
18 6 250 90 50 41.81 41.84 40.68 37.55
19 6 250 90 150 31.92 31.93 31.23 31.95
20 9 150 120 200 83.26 80.23 80.53 79.01
21 6 250 90 150 33.19 31.93 31.83 31.95
22 3 350 60 200 15.85 18.87 16.04 14.55
23 9 150 60 200 51.61 52.33 48.53 46.75
24 6 250 90 150 31.74 31.93 31.14 31.95
25 6 250 90 250 43.75 42.32 42.86 43.83
R2 0.99 0.99
RMSE 1.37 1.48
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A subset (n=5) comprising just above 15% of the available experimental data was used
for ANN testing to predict the unseen data (data not used for ANN training). Hence,
the trained ANNs was tested against the corresponding testing datasets to assess the
predictive power of the developed ANN models. Table 8.4 displays the ANN predicted
values for the testing datasets using LM-4-3-1 for pectinase immobilization and QP-
4-3-1 for xylanase immobilization.
Table 8.4. Experimental design showing the observed and predicted values of
enzyme (pectinase or xylanase) activity recovery (%) as output for testing dataset.
Run
Independent Variables Response
A B C D
Enzyme activity recovery (%)
Pectinase Xylanase
Observed Predicted Observed Predicted
Testing Data
1 3 150 120 100 46.07 45.78 44.02 42.33
2 0 250 250 150 64.49 62.56 63.45 61.40
3 6 250 90 150 32.41 31.93 31.46 32.01
4 9 150 120 100 75.91 73.89 74.56 72.35
5 9 350 60 200 15.92 13.62 17.45 18.68
R2 0.99 0.99
RMSE 1.73 1.86
The R2 value was 0.99 for both models, where RMSE values were 1.73 and 1.86 for
pectinase and xylanase immobilization models, respectively. The obtained R2
indicated that the regression predictions perfectly fit the data. In addition, the obtained
RMSE values showed a small difference between training and testing datasets (0.36
for pectinase immobilization model, and 0.38 for xylanase immobilization model),
indicating good generalization capability and accuracy of the trained ANN models.
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8.3.1.3 Optimization of enzyme immobilization using trained ANNs
The optimum conditions for enzyme (pectinase and xylanase) immobilization were
determined by comparing three different algorithms, which were rotation inherit
optimization (RIO), particle swarm optimization (PSO), and genetic algorithm (GA).
However, there was no significant difference in values of enzyme immobilization (%)
predicted by the three different algorithms. The predicted conditions for optimum
pectinase activity recovery (82.56 %) were 50 U/ml xylanase with 12% of genipin
crosslinker, with a coupling time of 120 min and agitation rate of 213 rpm. Similarly,
the predicted conditions for optimum xylanase activity recovery (83.89 %) were also
50 U/ml xylanase with 12% of genipin crosslinker and coupling time of 120 min, but
with an agitation rate of 250 rpm. The grid color charts of pectinase and xylanase
activity recovery (%) are shown in figures 8.5 and 8.6, respectively.
Pal and Khanum (2011) reported a slightly higher RSM-predicted activity recovery of
xylanase (89.5%) on alginate beads compared to our ANN-predicted values (84%)
using 8.31% glutaraldehyde crosslinker, 250 U/ml of xylanase from Aspergillus niger,
coupling time of 120 min and an agitation rate of 200 rpm. On the other hand, Abdel
Wahab et al. (2018) reported a slightly lower RSM-predicted activity recovery for
pectinase (80.43%) comparing to our ANN-predicted values (83%) using 5%
polyethyleneimine (PEI), 1.5% glutaraldehyde, 15 U/ml of pectinase from Aspergillus
awamori, and a coupling time of 6 h.
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Figure 8.5. Grid color charts representing the effect of independent variables on
pectinase activity recovery (%): (a) Effect of genipin concentration and pectinase
load on activity recovery when coupling time and agitation rate are fixed at 120
min and 213 rpm, respectively; (b) Effect of genipin concentration and coupling
time on activity recovery when pectinase load and agitation rate are fixed at 50
U/ml and 213 rpm, respectively; (c) Effect of genipin concentration and agitation
rate on activity recovery when pectinase load and coupling time are fixed at 50
U/ml and 120 min, respectively; (d) Effect of pectinase load and coupling time on
activity recovery when genipin concentration and agitation rate are fixed at 12 %
(w/v) and 213 rpm, respectively; (e) Effect of pectinase load and agitation rate on
activity recovery when genipin concentration and coupling time are fixed at 12
% (w/v) and 120 min, respectively; and (f) Effect of coupling time and agitation
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rate on activity recovery when pectinase load and genipin concentration are fixed
at 50 U/ml and 12 % (w/v) , respectively.
Figure 8.6. Grid color charts representing the effect of independent variables on
xylanase activity recovery (%): (a) Effect of genipin concentration and xylanase
load on activity recovery when coupling time and agitation rate are fixed at 120
min and 250 rpm, respectively; (b) Effect of genipin concentration and coupling
time on activity recovery when xylanase load and agitation rate are fixed at 50
U/ml and 250 rpm, respectively; (c) Effect of genipin concentration and agitation
rate on activity recovery when xylanase load and coupling time are fixed at 50
U/ml and 120 min, respectively; (d) Effect of xylanase load and coupling time on
activity recovery when genipin concentration and agitation rate are fixed at 12 %
(w/v) and 250 rpm, respectively; (e) Effect of xylanase load and agitation rate
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activity recovery when genipin concentration and coupling time are fixed at 12
% (w/v) and 120 min, respectively; and (f) Effect of coupling time and agitation
rate on activity recovery when xylanase load and genipin concentration are fixed
at 50 U/ml and 12 % (w/v) , respectively.
From Fig. 8.5a and Fig. 8.6a, it is observed that with increase in genipin concentration,
immobilization efficiency will also theoretically increase, as more attachment points
become available for enzyme immobilisation on the alginate beads. However,
increasing the enzyme load (pectinase or xylanase) will not always lead to an increase
in immobilisation efficiency, presumably due to insufficient attachment points of
genipin for enzyme. Similar results were reported by Sukri and Munaim (2017) where
the highest xylanase activity recovery was achieved when the alginate beads were
activated by a higher concentration of glutaraldehyde and a lower enzyme loading. As
one might expect, a longer coupling time and higher genipin concentration resulted in
higher immobilization efficiency at constant enzyme loading, as shown in Fig. 8.5b
and Fig. 8.6b. On the other hand, longer coupling time and higher enzyme loading did
not result in higher immobilization efficiency at constant genipin concentration, as
shown in Fig. 8.5d and Fig. 8.6d. The effect of agitation rate on the immobilization
efficiency (Fig. 8.5c and Fig. 8.6c) had a smaller effect compared to the effect of other
variables, most probably as mixing serves the single purpose of generating a
homogeneous suspension of bead- and enzyme solution. Such a homogeneous
suspension improves contact of the free enzyme with the beads, which results in higher
immobilization efficiency. Hence, as the agitation rate increases above the optimum
rate, immobilization efficiency shows no significant improvement. Sukri et al., (2020)
reported an equivalent result, reporting that an agitation rate of 200 rpm resulted in an
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optimum activity recovery (83.93%) of xylanase (200 U) on alginate beads activated
by glutaraldehyde (12%, w/w), but no improvements could be achieved in using a
higher rate.
The model validation was carried out by running at the predicted conditions (Table
8.5). As a result of three successive runs, only slight variation (1.5-1.6%) in the value
of enzyme immobilization was observed, suggesting that the optimal conditions for
immobilization of both enzymes generated by the ANN algorithms were reliable and
valid.
Table 8.5. Experimental validation of the optimization values predicted
by ANN for enzyme (pectinase or xylanase) activity recovery (%).
Replicates Pectinase Xylanase
Predicted Observed Predicted Observed
1
82.56
80.57
83.89
82.56
2 81.96 81.92
3 81.45 83.14
Mean 81.33 Mean 82.54
Error (%) 1.52 Error (%) 1.64
8.3.1.4 Evaluation of the immobilized enzymes
Immobilized enzymes were evaluated by studying their key required operational
parameters (temperature, pH, storage and recycle stability) in comparison with free
forms (Figure 8.7).
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Figure 8.7. Panels (A–C) shows the effect of temperature (A), pH (B), storage
time (C), and recycle count (D) on relative activity of immobilized pectinase and
xylanase in comparison with free enzyme.
As shown in Figure 8.7a, the immobilization did not change the optimal temperature
of xylanase (60°C) at pH 5. However, the optimum temperatures of the free and
immobilized pectinase were 50°C and 60°C, respectively. Such a forward shift in the
temperature optimum of immobilized pectinase by 10°C could be the result of
improved enzyme rigidity after covalent binding on alginate beads (Ortega et al.,
2009). To study the pH-dependent activities of the free and immobilized enzymes, the
temperature of assay mixtures was maintained at 60°C while pH values were varied
from 2.0 to 11.0 (Figure 8.7b). Although the immobilized enzymes retained the
optimal pH (5.0) of their free pectinase and xylanase counterparts, the pH scope of the
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immobilized pectinase was expanded, and it retained more than 80% activity over a
wider pH range of 4.0–8.0 than that of the free form (pH range of 5.0-7.0). Similarly,
the immobilized xylanase exhibited improved pH stability, and retained greater than
80% activity over a wider pH range of 3.0–7.0 than that of the free form (pH range of
4.0-6.0). These results may be attributed to the free protein undesired aggregation that
is prevented by the covalent bonding of the enzyme onto alginate beads during the
immobilization process (Mostafa et al., 2019).
The data in Figure 8.7c show the storage stability of enzymes immobilized onto
alginate beads over 30 days at 4°C. Immobilized pectinase and xylanase retained 60%
and 51%, respectively, of their initial activity after 30 days of storage. In contrast, the
free pectinase and xylanase lost more than 46% and 51%, respectively, of their initial
activity after 20 days of storage. The multiple re-use capability of immobilized
enzymes can be achieved by recovering the beads from the reaction mixture, thereby
reducing costs. As shown in Figure 8.7d, the residual activity of the immobilized
enzymes was 55% (pectinase) and 51% (xylanase), after five consecutive cycles. The
activity loss of the immobilized enzyme may be due to a combination of inactivation
and enzyme leakage from the support (Mohamad et al., 2015).
8.3.2 Enzymatic treatment of apple juice
8.3.2.1 The ANN model training
The larger subset (n=25) comprising more than 80% of the available experimental data
was used for the ANN training and model building. To determine the optimal topology
for the networks, the number of neurons in the hidden layer was varied from 1 to 7. A
decision on the optimum topology was subsequently based on the minimum RMSE
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(and the closer R2 to 1) of the training set values. Figure 8.8a illustrates the
performance of the network for testing data versus the number of neurons in the hidden
layer using different learning algorithms. According to the RMSE, the network with 3
hidden neurons produced the optimum performance when any of the four algorithms
(IBP, BBP, QP and LM) was employed. Therefore, the optimum topology of the
networks (Figure 8.8b) was 4-3-1 (four neurons in the input layer, three neurons in the
hidden layer and one neuron in the output layer).
Figure 8.8. The left panel (a) shows the performance of different learning
algorithms (Incremental backpropagation algorithm, IBP; Batch
backpropagation algorithm, BBP; Quick propagation algorithm, QP; and
Levenberg-Marquardt algorithm, LM) for training data versus of the number of
neurons in the hidden layer for predicting the apple juice clarification (%) using
pectinase and xylanase immobilized onto alginate beads. The right panel (b)
shows schematic diagram of the optimal multi-layer, normal feed-forward neural
network architecture for apple juice clarification.
8.3.2.2 Selection neural network model
The model architecture of 4-3-1 was selected as the best topology for the four learning
algorithms. Figure 8.9 presents the predictions using different learning algorithms with
138
optimum architecture (4-3-1) versus the observed values of the juice clarification (%)
which were obtained in the laboratory.
Figure 8.9. The scatter plots of the predicted juice clarification versus the
observed juice clarification for training dataset that shows the performed R2 and
RMSE of different learning algorithms at optimal neural network architecture
(4-3-1).
The comparison of the RMSE proved that the BB with 4 nodes in input, 3 nodes in
hidden, and 1 node in the output layer (BB-4-3-1) presented the minimum RMSE,
while its R2 was at the maximum value. As illustrated, the RMSE was 1.21, and the R2
was 0.998 which indicated the great predictive accuracy of the model. Therefore, BB-
4-3-1 was selected as the final optimum provisional model of the juice clarification for
an evaluation test. Table 8.6 displays the ANN predicted values for the testing datasets
using BB-4-3-1 for juice clarification (%). It is worth noting that the batch algorithms
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are effective in training small datasets with small network topologies (Plagianakos et
al., 2001).
Table 8.6. Experimental design showing the observed and predicted values
of juice clarification (%) as output for training dataset.
Run
Independent Variables Response
A B C D Juice clarification (%)
Observed Predicted
Training Data
1 40 20 60 55 50.46 50.32
2 30 30 80 50 33.79 32.84
3 20 40 100 55 29.96 30.21
4 50 30 80 50 44.79 44.68
5 20 20 60 55 30.19 29.86
6 20 40 60 55 20.27 17.34
7 40 40 60 45 36.48 35.51
8 20 20 100 45 47.46 47.20
9 30 50 80 50 15.14 18.24
10 20 20 100 55 67.49 67.73
11 40 20 60 45 50.84 49.79
12 20 40 100 45 25.75 23.80
13 30 30 80 50 33.79 34.36
14 40 40 100 55 29.69 28.82
15 30 30 80 50 33.79 34.63
16 40 20 100 55 85.80 84.82
17 30 30 40 50 28.97 28.34
18 30 30 80 50 33.79 34.63
19 40 20 100 45 79.37 79.99
20 20 20 60 45 16.96 17.73
21 30 30 80 50 33.79 34.17
22 40 40 60 55 18.59 20.19
23 10 30 80 50 11.19 10.84
24 30 30 80 40 42.48 44.02
25 30 30 80 60 46.31 46.64
R2 0.99
RMSE 1.22
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A subset (n=5) comprising just above 15% of the available experimental data was used
for ANN testing to predict the unseen data (data not used for ANN training). Hence,
the trained ANNs was tested against the corresponding testing datasets to assess the
predictive power of the developed ANN models (Table 8.7). The R2 value was 0.99,
where RMSE value was 1.84 for the trained ANN. Such an R2 value indicates that the
regression predictions perfectly fit the data. In addition, the obtained RMSE values
showed a small difference between training and testing datasets (0.62), indicating good
generalization capability and accuracy of the trained ANN model (BB-4-3-1).
Table 8.7. Experimental design showing the observed and predicted values
of juice clarification (%) as output for testing dataset.
Run
Independent Variables Response
A B C D Juice clarification (%)
Observed Predicted
Testing Data
1 30 10 80 50 67.02 64.36
2 40 40 100 45 39.09 39.88
3 20 40 60 45 21.18 22.09
4 30 30 120 50 68.87 71.61
5 30 30 80 50 33.79 32.90
R2 0.99
RMSE 1.84
8.3.2.3 Optimization of juice clarification process using trained
ANNs
The optimum points for juice clarification were determined by comparing three
different algorithms, namely rotation inherit optimization (RIO), particle swarm
optimization (PSO), and genetic algorithm (GA). However, there was no significant
difference in values of enzyme immobilization (%) predicted by the algorithms. The
141
predicted conditions for juice clarification (85.62 %) were 50 U of pectinase/ml of
juice and 20 U of xylanase/ml of juice for 100 min at 57 °C. Thus, our findings are in
line with previous literature (Rai et al., 2004; Sin et al., 2006) confirming that
enzymatic treatment for juice clarification is greatly influenced by enzyme loading,
holding time and temperature. After the experimental validation of the model using
the optimization conditions, it was found that the observed value (84.33± 0.32%) was
close to that predicted (85.62%), suggesting the appropriateness of the developed
ANN model.
Similarly, Singh and Gupta (2004) achieved apple juice clarification of 85% (%T650)
using polygalacturonase from Aspergillus niger (15 IU/ml) in the presence of 0.01%
gelatin at 45 °C and with a 6 h holding time. The most effective clarification (%T660,
97%) was reported by Dey and Banerjee (2014) with 1% polygalacturonase from
Aspergillus awamori Nakazawa (9.87 U/mL) and 0.4% α-amylase from A. oryzae (899
U/mL), in the presence of 10 mM CaCl2 at 50 °C and with a 2 h holding time. Also,
Dey et al. (2014) reported the maximum transmittance of 93% (%T660) in clarified
apple juice upon enzymatic treatment using polygalacturonase from Aspergillus
awamori Nakazawa (9.87 U/ml) at 50 °C and a 2 h holding time. Gaomei et al. (2011),
reported in their patent a novel technology for clarifying apple juice. In the latter
invention, the maximum clarity of apple juice (%T660, 93%) was achieved when cider
boiled for 5-10 min and then treated with pectinase (60 U/gram cider, dissolved in
buffer solution of pH=3.5) for 2h at 50°C.
Additionally, researchers have previously reported that the treatment with xylanase
positively contributes to the clarity of apple juice. For instance, the treatment of juice
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with xylanase (15 IU/g of apple pulp) from Bacillus pumilus SV-85S lead to a clarity
in terms of % transmittance of approximately 42 (%T660) at 40 °C, with a 30 min
holding time (Nagar et al., 2012). Adigüzel and Tunçer (2016) reported the maximum
transmittance of about 18% (%T660) in clarified apple juice upon enzymatic treatment
using xylanase from Streptomyces sp. AOA40 (12.5 U/ml of apple juice) at 60 °C and
a 90 min holding time. Also, Phadke and Momin (2015) reported a maximum
transmittance of 20% (%T650) in clarified juice upon enzymatic treatment using
xylanase from Bacillus megaterium (20 U/g of apple pulp) at 37 °C and a 4 h holding
time.
Madhu et al. (2015) achieved apple juice clarification of approximately 42%, and 49%
(%T650) using enzyme cocktails (cellulase, pectinase and xylanase) from P. exigua and
A. niger, respectively, at 60°C and a 50h holding time. The study of Pal and Khanum
(2011b) explored a synergistic effect of xylanase, pectinase and cellulase to improve
clarity of pineapple juice, achieving about 81% (%T650) clarity.
Figure 8.10 demonstrates the importance of effective parameters on apple juice
clarification as an output of the model. The importance values of the parameters were
pectinase loading > xylanase loading > temperature > holding time in the selected
range of the variables. Thus, the effects of the two most important parameters
(pectinase and xylanase loadings) on apple juice clarification are presented in Figure
8.11, where temperature and time were kept constant at the optimal values (100 min,
and 57 °C, respectively). As shown in Figure 11, apple juice clarity increased with an
increase in pectinase rather than xylanase loading at optimum reaction conditions (100
143
min, and 57 °C). This can be attributed to the fact that apples are particularly rich
sources of pectin rather than xylan.
Figure 8.10. Importance of effective parameters on apple juice clarification.
Figure 8.11. Three-dimensional surface plot of pectinase load and xylanase load
effects on apple juice clarification. The other variables (temperature and time)
were kept constant at the optimal values.
144
Finally, table 8.8 summarizes application data sheets of different commercial enzyme
preparations available in the market for apple juice clarification and suggests a role for
the use of immobilized enzyme beads developed in our study.
Table 8.8. Application of different commercial enzyme preparations in apple
juice clarification
Company Product Description Dosage
Application
Ref. pH Temp. (°C)
Time (min)
DSM RAPIDASE Pectinases from A. niger and A. aculeatus
20-40 ml/ 1,000 L of Juice
3.5-5.5 45-55 90-120 (DSM, 2015)
Biovet Pectinase
Pectinesterase, polygalacturonase, pectinlyase from A. niger
2-20 g/ton
3.5-6.0 50 - 55 30-60 (Biovet)
Eaton Panzym Pro Clear
Polygalacturonase from A. niger and A. aculeatus
20-50 ml/ 1,000L of Juice
- 50-55 60-120 (Eaton, 2015)
8.4 Conclusion
The artificial neural network (ANN) modelling was adopted to simulate and predict
the activity recovery of enzymes (pectinase and xylanase) when immobilized onto
genipin-activated alginate beads, as well as their application in apple juice
clarification. The predicted conditions for optimum pectinase activity recovery (~83
%) were 50 U/ml pectinase with 12% of genipin crosslinker and a coupling time of
120 min (agitation rate of 213 rpm). On the other hand, the predicted conditions for
optimum xylanase activity recovery (~84 %) were also 50 U/ml xylanase with 12% of
genipin crosslinker and coupling time of 120 min, but at an agitation rate of 250 rpm.
A maximum activity recovery was observed to be about 81-83% for both enzymes.
Moreover, the predicted conditions for juice clarification (85.62 %) were 50 U/ml of
pectinase, 20 U/ml of xylanase for 100 min at 57 °C. It was found also that the observed
value (84.33± 0.32%) was close to that predicted (85.62%). The developed model
indicated pectinase loading as the most important factor, having a dramatic influence
145
on apple juice clarification. Enzyme beads prepared at optimum immobilization
conditions were suitable for up to 5 repeated uses, losing only ~45% (pectinase) and
~49% (xylanase) of their initial activity.
146
General Conclusions
Future recommendations
This chapter includes conclusions of present work and discusses the area of future
recommendations.
147
9.1 General conclusions
The overall aim of the project was to utilise brewer’s spent grain (BSG) for production
of industrially important enzymes, which are in turn expected to contribute to BSG
waste recycling and reduce the cost of enzymes. BSG generated from beer-brewing
has been estimated at 3.4 million tonnes annually in the EU alone, and over 4.5 million
tons in USA as the largest craft beer producer. Novel enzymes identified in this work
were immobilised to enable enzyme re-use, extend storage time, and also potentially
achieve higher stability under varying temperature and pH. Finally, the immobilised
enzyme preparations were tested for proof-of-concept in apple juice clarification
process.
In line with the aim of developing a pre-treatment strategy that is devoid of any
chemicals, a novel extraction treatment for BSG was identified and optimised based
on the energy-efficient technologies of microwave heating and ultrasound irradiation.
For pretreatment of brewer's spent grain, BSG was subjected to microwave and
ultrasound pretreatment in separate one-factor-at-a- time (OFAT) experiments to study
the effect of different variables on fermentable sugar yields from BSG. Microwave
pretreatment of BSG at the optimum condition (600 w for 90 s) achieved a higher
fermentable sugar yield (64.4±7 mg / 1 g of BSG) as compared to ultrasound
pretreatment of BSG (39.9±6.1 mg / 1 g of BSG) at the optimum condition (frequency
of 25 kHz, power of 550 W, and time of 60 min.). The reducing sugar yield for native
BSG used in this study before pretreatment was 24.5± 0.3 mg/g of biomass.
However, the derived ultrasound pretreatment time (1 hour) during the OFAT
experiments was relatively long. Such a long duration may adversely affect the uptake
of such technology by industry. Thus, response surface methodology (RSM) was used
148
for optimization of the multiple variables of ultrasound pretreatment of BSG and to
investigate the possibility for reducing the pretreatment time. Additionally, chemical
composition, morphological structure and thermal behavior of BSG before and after
pretreatment were studied and compared.
The optimal conditions for ultrasound-assisted pretreatment of BSG were found to be
20% US power of 550 W, 60 min, 26.3°C, and 17.3% w/v of biomass in water. BSG
pretreatment under the optimal ultrasonication conditions resulted in a 2.1-fold higher
reducing sugar yield, relative to native BSG. These results suggest that performing a
design of experiments (DOE) to optimise ultrasound pretreatment of BSG achieved a
higher fermentable sugar yield (2.1-fold, relative to native BSG) as compared to the
one-factor-at-a-time (OFAT) approach used in previous experiments for optimisation
of ultrasound pretreatment (that achieved 1.6-fold increase in fermentable sugar yield,
relative to native BSG).
However, the same long pretreatment time (60 min.) was required for optimisation of
ultrasound pretreatment which can lead to adverse effects (due to collision and
aggregation between the particles) and increased energy consumption during the
process. Therefore, microwave pretreatment was selected as an energy efficient and
effective pretreatment step for BSG for all subsequent fermentation studies.
In order to discover novel enzyme activities, twenty-nine fungal isolates were
obtained from spoiled-fruit samples of 19 different varieties. The isolates were
distributed across three genera, namely: Mucor spp. (19 isolates), Penicillium spp. (6
isolates), and Aspergillus spp. (4 isolates). All isolates were selected for subsequent
pectinase and xylanase production screening. Out of the isolates recovered, Mucor
149
hiemalis isolated from Bramley apple (Malus domestica) was found to produce
xylano-pectinolytic enzymes with higher specific activity.
The highest enzyme activity (137 U/g, and 67 U/g BSG, for pectinase and xylanase,
respectively) was achieved in a medium that contained 15 g of BSG/250ml conical
flask, at pH 6.0, temperature of 30°C, and supplemented with 1.0 % xylan or pectin
for inducing the production of xylanase or pectinase, respectively. Thus, BSG
represent an attractive feedstock for microbial fermentation–based biomanufacturing.
Following removal of biomass by centrifugation, pectinase and xylanase produced by
Mucor hiemalis were purified by ammonium sulphate precipitation, resulting in a
95% and 76% yield for pectinase and xylanase, respectively. The ammonium sulfate-
enriched fraction of pectinases and xylanases were further concentrated to 14-fold and
12-fold respectively by ultrafiltration. The partially purified and concentrated
pectinase and xylanase were optimally active at 60°C and pH 5.0 (1602 U/ml of
pectinase, and 839 U/ml of xylanase). Thus, the present study suggests that BSG is an
effective substrate for production of microbial enzymes such as pectinase and
xylanase.
The enzymes obtained after fermentation of BSG were immobilised on novel magnetic
nanoparticles and a natural hydrogel. Superparamagnetic magnetite nanoparticles
(MNPs) were biosynthesized using Aspergillus flavus isolated from spoiled strawberry
(Fragaria ananassa Duchesne, Variety: Rociera). The morphology of MNPs was
found to be flake-like, as confirmed by Field Emission Scanning Electron Microscopy
(FESEM), while the average crystallite size was ~16 nm, as determined by X-ray
diffraction (XRD). Energy dispersive X-ray (EDX) analysis was performed to confirm
the presence of elemental Fe in the sample. Pectinase and xylanase were covalently
150
immobilized on MNPs with efficiencies of ~84% and 77%, respectively. Compared to
the free enzymes, the immobilized enzymes were found to exhibit enhanced tolerance
to variation of pH and temperature and demonstrated improved storage stability.
Furthermore, the residual activity of the immobilized enzymes was about 56% for
pectinase and 52% for xylanase, after four and three consecutive use cycles,
respectively. However, rapid and efficient separation of magnetic nanoparticles from
the reaction medium to prevent unintended leaching of potentially toxic nanoparticles
into consumer products remains a challenge.
As an alternative food-friendly approach, this project also investigated sodium alginate
from the brown algae laminaria hyperborea as a carrier and genipin from gardenia
fruit Gardenia jasminoides as a cross linking agent for immobilization of pectinase
and xylanase from Mucor hiemalis. The artificial neural network (ANN) modelling
was adopted to simulate and predict the efficiency of enzyme immobilization onto
genipin-activated alginate beads, as well as their application in apple juice
clarification. The predicted conditions for optimum pectinase activity recovery (~83
%) were 50 U/ml pectinase with 12% of genipin crosslinker and a coupling time of
120 min (agitation rate of 213 rpm). A maximum activity recovery was observed to be
about 81-83% for both enzymes. On the other hand, the predicted conditions for
optimum xylanase activity recovery (~84 %) were also 50 U/ml xylanase with 12% of
genipin crosslinker and coupling time of 120 min, but at an agitation rate of 250 rpm.
The following table provides a comparison between MNP and alginate beads as
carriers for pectinase and xylanase:
151
Table 9.1. Comparison between MNP and alginate beads as carriers for pectinase and
xylanase
Carriers
Features
Magnetic nanoparticles Alginate beads
Pectinase Xylanase Pectinase Xylanase
Fractional enzyme activity (%) 74% 68% 81% 83%%
Optimal temperature (20 - 80°C) 50°C 60°C 50/60°C 60°C
Optimal pH (2.0 to 11.0) 5.0 5.0 5.0 5.0
Half-life of the enzyme at 4-6 °C 30 days 25 days 20 days 20 days
Reusability 4 cycles 3 cycles 5 cycles 5 cycles
Moreover, the predicted conditions for juice clarification (85.62 %) were 50 U/ml of
pectinase, 20 U/ml of xylanase for 100 min at 57 °C. It was found also that the observed
value (84.33± 0.32%) was close to that predicted (85.62%). The developed model
indicated pectinase loading as the most important factor, having a dramatic influence
on apple juice clarification. Enzyme beads prepared at optimum immobilization
conditions were suitable for up to 5 repeated uses, losing only ~45% (pectinase) and
~49% (xylanase) of their initial activity. The immobilized enzymes are of industrial
relevance in terms of biocompatibility, recoverability and operational-storage stability.
From the above mentioned results, one can conclude that our in-house pectinase and
xylanase recyclable preparations can improve juice appearance through a cost-
effective enzymatic clarification process at bench-scale. The obtained experimental
proof of concept should encourage investment into pilot-scale demonstration in a move
towards full commercial scale. Moreover, the use of alginate beads as enzyme carriers
represents a feasible option for juice producers, as such an approach will require only
installing a stainless steel perforated mesh basket or a retaining stainless-steel filter at
the outlet of the enzyme treatment tank for bead retention.
152
Although the aims of this project were successfully achieved, more studies can be
conducted in this area to further the possibilities of this work. A few of the
recommendations have been mentioned below.
9.2 Future recommendations
9.2.1 Other potential lignocelluosic wastes
This work investigated the application of selected emerging technologies such as
ultrasound and microwave as promising technologies in the pretreatment of BSG prior
to microbial fermentation. Similar to the work undertaken in this thesis, an interesting
continuation of this work would be investigating ultrasound and microwave as
potential technologies for sustainable pretreatment of other lignocellulosic wastes and
subsequent microbial fermentation. Other potential lignocellulosic wastes from
various industries includes sugarcane bagasse (from sugar milling), pomace (pressing
of tomato), apple and grapes (juice), spent coffee grounds (coffee preparation), as well
as citrus and potato peels. This calls for the need for investigating those lignocellulosic
feedstock options for fermentation processes.
9.2.2 Isolation of pectin and xylan degrading bacteria
Out of twenty-nine (29) isolates recovered in this study, Mucor hiemalis isolated from
Bramley apple (Malus domestica) produced acidic pectinolytic and xylanolytic
enzymes with higher specific activity. Enzymes produced were successfully used after
immobilisation for clarification of apple juice that is acidic in nature due to presence
of acids, mainly malic acid. The objectives of this project can be extended to include
the production of other hydrolytic enzymes, such as cellulase, amylase, and ligninase.
153
Furthermore, pectinase and xylanase have also potential applications in alkaline
medium such as biobleaching of paper pulp, and the bioscouring of cotton fabrics.
Production of alkaline enzymes require isolation of pectin and xylan degrading
bacteria which in itself comprise as a topic of extensive research.
Moreover, the advent and proliferation of recombinant DNA technology has enabled
scientists to clone and mass produce enzymes of any origin in microbes to fit the
demand of various industries. Strain improvement via mutation is another technology
widely employed in industry to increase the yield of enzyme production. Such
advanced approach has proven to be important to industry and represent possible
direction for future research.
9.2.3 The application of magnetic nanoparticles in food industry
This project provided a proof of concept study that iron oxide nanoparticles from
Aspergillus flavus (isolated from Strawberry) have potential as enzyme carrier.
However, this study has not investigated the safety of magnetic nanoparticles and their
use in food industry applications. Therefore, cellular uptake, trafficking and potential
toxicity of ingested MNPs in the human gastrointestinal route would warrant further
in vitro-in vivo investigations prior to application of MNPs in food industry.
In fact, there is a lack of adequate standard methods for nanoparticles testing under
reproducible and realistic conditions to predict the fate and toxicity of MNPs in the
human gastrointestinal tract and the external environment. Thus, only after going
through safety trials of MNPs, potential applications of MNPs in food applications is
recommended.
154
Back to the big picture, it is worth noting that successful commercialisation of
lignocellulose-based biorefining requires policy support, optimisation of feedstock
supply chain, and assessing the economic feasibility in a large-scale production
scenario, which itself comprises further directions for future research. Although
politics and economics were not included in the project scope, below are some
important considerations to support the biorefining nascent industry in the EU.
9.2.4 Polices
European Commission initiatives, such as Projects-for-Policy (P4P), aims to use
results from research and innovation projects to shape policy making. In this context,
P4P (2018) published reports have recommended policy measures to unlock the
unexploited potential of industrial waste streams, and to enhance circular utilisation of
resources. Moreover, independent alliances, such as the European Bioeconomy
Alliance, have requested revision of the bioeconomy strategy to ensure that
biorefineries and related technologies become an integral part of EU level policies.
Recently, the commission expert group on bio-based products in the EU reported that
progress in the development of a renewables-based economy is at risk of being slower
than the rest of the world in achieving the targeted shift to a renewables-based
economy. As a result, the expert group also recommended the revision of the EU
bioeconomic strategy.
9.2.5 Sustainable feedstock logistics
The results suggest that brewing waste (BSG) can be utilised for production of
industrially important enzymes such as pectinase and xylanase. However, from a
commercial point of view, the logistical challenge surrounding the feedstock supply
recommend several future research directions. Areas for future research should include
155
the development of integrated logistical models to remove supply chain bottlenecks,
the availability of machinery which is capable of handling large amounts of feedstock,
the mapping of biomass inventories, and the establishment of a centralized regional
hub for biomass collection and storage.
156
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157
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List of Publications
This thesis is in the format of peer-reviewed publications published by the author in
international recognised journals.
193
Peer Review Publications
1. Hassan, S. S., Williams, G. A. & Jaiswal, A. K.(2020). Computational
modelling approach for the optimization of apple juice clarification using
immobilized pectinase and xylanase enzymes. Current Research in Food
Science, In Press, https://doi.org/10.1016/j.crfs.2020.09.003.
2. Hassan, S. S., Tiwari, B., Williams, G. A. & Jaiswal, A. K.(2020).
Bioprocessing of brewers’ spent grain for production of Xylanopectinolytic
enzymes by Mucor sp . Bioresource Technology Reports, 9, 100371.
https://doi.org/10.1016/j.biteb.2019.100371
3. Hassan, S. S., Ravindran, R., Jaiswal, S., Tiwari, B.,Williams, G. A. & Jaiswal,
A. K. (2020). An evaluation of sonication pretreatment for enhancing
saccharification of brewers' spent grain. Waste Management, 105, 240-247.
https://doi.org/10.1016/j.wasman.2020.02.012
4. Hassan, S. S., Williams, G. A. & Jaiswal, A. K. (2019). Moving towards the
second generation of lignocellulosic biorefineries in the EU: drivers,
challenges, and opportunities. Renewable and Sustainable Energy Reviews,
101, 590-599. https://doi.org/10.1016/j.rser.2018.11.041
5. Hassan, S. S., Williams, G. A. & Jaiswal, A. K. (2019). Lignocellulose
Biorefining in Europe: Current State and Prospects. Trends in Biotechnology,
37 (3), 231-234. https://doi.org/10.1016/j.tibtech.2018.07.002
6. Hassan, S. S., Williams, G. A. & Jaiswal, A. K. (2018). Emerging technologies
for the pretreatment of lignocellulosic biomass. Bioresource Technology, 262,
310-318. https://doi.org/10.1016/j.biortech.2018.04.099
7. Ravindran, R., Hassan, S. S., Williams, G. A. & Jaiswal, A. K. (2018). A
Review on Bioconversion of Agro-Industrial Wastes to Industrially Important
Enzymes. Bioengineering, 5 (4), 93.
https://doi.org/10.3390/bioengineering5040093
194
Poster / Oral Presentations
1. Hassan, S. S., Tiwari, B., Williams, G. A. & Jaiswal, A. K.(2021).
Extracellular production of polysaccharide degrading-enzymes by Mucor sp.
through fermentation of brewers' spent grain. The 2nd International Conference
on Microbial Food and Feed Ingredients (MiFFI). 15–17 April 2020
(Postpended to 16-18 November 2021 due to COVID-19), Copenhagen,
Denmark.
2. Hassan, S. S., Williams, G. A. & Jaiswal, A. K.(2020). Green synthesis of
magnetic nanoparticles using Aspergillus flavus as novel support for recycling
pectolytic and xylanolytic enzymes. 20th IEEE International Conference on
Nanotechnology. 19-21 July 2020, Montreal, Canada, Virtual conference.
3. Hassan, S. S., Williams, G. A. & Jaiswal, A. K.(2020). Fungus-Mediated
Synthesis of magnetic nanoparticles for immobilisation of Pectolytic and
xylanolytic enzymes. 11th International Conference on Nanotechnology
Fundamentals and Applications (ICNFA’20). 19-21 August 2020, Prague,
Czech Republic, Virtual conference.
4. Hassan, S. S., Ravindran, R., Jaiswal, S., Tiwari, B., Williams, G. A. &
Jaiswal, A. K. (2020). Optimization of Process Conditions Using Response
Surface Methodology for fermentable sugars release from ultrasound
pretreated brewers' spent grain. 28th European Biomass Conference &
Exhibition (EUBCE), Transition to a Bioeconomy. 6 – 9 July 2020, Marseille,
France Virtual conference.
5. Hassan, S. S., Tiwari, B., Williams, G. A. & Jaiswal, A. K. (2020). Production
and purification of Pectinase and Xylanase from fermentation of brewers' spent
grain by Mucor sp. 28th European Biomass Conference & Exhibition
(EUBCE), Transition to a Bioeconomy. 6 – 9 July 2020, Marseille, France
Virtual conference.
6. Hassan, S. S., Williams, G. A. & Jaiswal, A. K.(2019). Utilisation of agri-food
waste streams for production of novel lignocellulose-degrading enzymes. DEL
Research Colloquium. 22nd February 2019, Greenway Hub, Grangegorman,
Dublin, Ireland.
195
List of Employability Skills and
Discipline Specific Skills Training
196
Structured PhD Modules
Technological University Dublin (TU Dublin; formerly DIT)
2018 GRSO1001 Research Methods
2018 GRSO1005 Introduction to statistics
2019 GRSO1012 Research Integrity
The Agri-Food Graduate Development Programme (AFGDP)
2018 FDSC40670 PhD and beyond Masterclass
2018 FDSC40250 Hot topics in the agri-food sector
2018 FDSC40160 Innovation and Creativity for the Agri-Food Researcher
2018 Career Development and Management the Agri Food Sector
University of Limerick (UL)
2018 PH6022 Writing for Science and Engineering
