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Multivariate Analysis of 2D-NMR
Spectroscopy -Applications in wood science and metabolomics
Tommy Öman
Kemiska instutionen
Umeå 2013
Copyright © Tommy Öman
ISBN: 978-91-7459-728-8
Picture of Aurora Berealis, taken by Stefan Bergmark Piteå.
Elektronisk version tillgänglig på http://umu.diva-portal.org/
Tryck/Printed by: Print och Media Umeå
Umeå, Sweden 2013
Tillägnad Melker, Sixten, Svante och Mia.
ii
Abstract
Wood is our most important renewable resource. We need better
quality and quantity both according to the wood itself and the
processes that are using wood as a raw material. Hence, the
understanding of the chemical composition of the wood is of high
importance. Improved and new methods for analyzing wood are
important to achieve better knowledge about both refining processes
and raw material. The combination of NMR and multivariate analyses
(MVA) is a powerful method for these analyses but so far it has been
limited mainly to 1D NMR. In this project, we have developed
methods for combining 2D NMR and MVA in both wood analysis and
metabolomics. This combination was used to compare samples from
normal wood and tension wood, and also trees with a down regulation
of a pectin responsible gene. Dissolving pulp was also examined using
the same combination of 2D-NMR and MVA, together with FT-IR
and solid state 13C CP-MASNMR. Here we focused on the difference
between wood type (softwood and hardwood), process type (sulfite
and sulfate) and viscosity. These methods confirmed and added
knowledge about the dissolving pulp. Also reactivity was compared in
relation to morphology of the cellulose and pulp composition. Based
on the method and software used in the wood analysis projects, a new
method called HSQC-STOCSY was developed. This method is
especially suited for assignment of substances in complex mixtures.
Peaks in 2D NMR spectra that correlate between different samples are
plotted in correlation plots resembling regular NMR spectra. These
correlation plots have great potential in identifying individual
components in complex mixtures as shown here in a metabolic data
set. This method could potentially also be used in other areas such as
drug/target analyses, protein dynamics and assignment of wood
spectra.
iii
Sammanfattning på svenska
Skogs- och träindustrin är en av de viktigaste basindustrierna i
Sverige, och speciellt i norra Sverige. Med ökande konkurrens i
världen inom skogs- och träindustrin är det viktigt att Sverige är
ledande i utvecklingen mot effektivare och bättre processer.
Utvecklingen börjar från att framädla/genmodifiera bättre träd med
specifika egenskaper, till att förbättra processer som billigare
framställer gamla och nya produkter baserat på råmaterialet. För att få
en bättre kontroll över vilka egenskaper som är viktiga parametrar i en
process så behöver även analysmetoderna utvecklas. Dessutom
kommer en ökad användning av biprodukter att ställa ännu högre krav
på analysmetoder för att lättare kunna använda rätt råmaterial och
processinställningar för att det ska vara ekonomiskt lönsamt. Även om
tillgängligheten för vissa analysmetoder som vi använt (exempelvis
NMR) idag är begränsad, så finns det stor möjlighet att (inom ett antal
år) antalet spektroskopiska analysmetoder kommer att öka avsevärt
inom skogsindustrin. Sättet att kombinera multivariata metoder med
olika analysmetoder kommer att förenkla tolkningen av komplexa
prov och hitta resultat som annars skulle varit omöjliga att se.
Teknologiskt kommer det att vara svårt att leda utvecklingen. Så
kunskap, erfarenhet och kreativitet är ett måste för att Sveriges
ekonomi i fortsättningen ska vara lika stark.
Här har nya metoder använts för att analysera prov både från
genmodifierade träd och produkter direkt från industrin.
Kombinationen av NMR och multivariata regressionsmetoder har
visat sig vara en robust, lättolkad och spännande metod som hittar
resultat som annars skulle vara svåra eller omöjliga att upptäcka.
För studier av dissolvingmassor fås bra modeller både med råmaterial
och processtyp. Detta kan hjälpa till att både prediktera in okänt
råmaterial och att justera processer för att få bättre slutprodukt.
Dessutom kan kostsamma analysmetoder i framtiden ersättas med
spektroskopiska analyser vilket kan spara både tid och pengar. Det
kan även ses som ett bra komplement som ger större kunskap om
vedproven.
iv
För att identifiera molekyler i komplexa prov som innehåller många
ämnen så har vi utvecklat en ny metod, kallad HSQC-STOCSY.
Denna metod bygger på att signaler från samma molekyl korrelerar
(samvarierar) mellan olika prov och man kan därigenom extrahera ett
spektrum som endast innehåller signaler från en komponent från en
blandning. Detta kan anändas för att tillordna exempelvis spektran
från metaboliska studier eller vedprov. Men här finns även stor
potential i studier av proteiner eller interaktioner mellan proteineroch
ligander..
v
Abbreviations
CP-MAS cross polarization magic angle spinning
DA discriminant analysis
DMSO Dimethyl sulfoxide
FT-IR Fourier transforms infrared spectroscopy
HSQC heteronuclear single quantum coherence spectroscopy
HW hardwood
MVA multivariate data analysis
NMI N-Methyl imidazole
NMR nuclear magnetic resonance spectroscopy
OPLS orthogonal projections to latent structures
OSC Orthogonal Signal Correction
PCA principal component analysis
PLS partial least squares
STOCSY statistical total correlation spectroscopy
SW softwood
vi
List of papers
The work presented in this thesis is based on these papers:
I. Hedenström, M., Wiklund-Lundström, S., Öman, T., Lu, F.,
Gerber, L., Schatz P., Sundberg, B., Ralph, J. Identification
of Lignin and Polysaccharide Modifications in Populus
Wood by Chemometric Analysis of 2D NMR Spectra from
Dissolved Cell Walls. Mol Plant 2, 933-942 (2009).
II. Strunk, P*., Öman, T*., Gorzsas, A., Hedenström, M. &
Eliasson, B. Characterization of dissolving pulp by
multivariate data analysis of FT-IR and NMR spectra.
Nordic Pulp & Paper Research Journal 26, 398-409 (2011).
III. Öman, T*., Strunk, P*.,Eliasson, B., Hedenström, M.
Reactivity of dissolving pulp analyzed with multivariate
data analysis of XRD and NMR data. (Manuscript)
IV. Öman, T., Tessem, M-B, Bertilsson, H., Angelson, A.,
Bathen, T., Antti, H., Hedenström, M., Andreassen, T.
Identification of metabolites from 2D 1H-13C HSQC NMR
using peak correlation plots. (Submitted).
Equal contribution from authors *
Papers not included in this thesis.
Öhman, A., Öman, T., Oliveberg, M. Solution Structures
and backbone dynamics of the ribosomal protein S6 and its
permutant P(54-55). Protein Sci. 19, 183-189. (2010)
Haglund, E., Lind J., Öman, T., Öhman, A., Mäler, L.,
Oliveberg, M. The HD-exchange motions of ribosomal
protein S6 are insensitive to reversal of the protein-folding
pathway. Proc. Nat. Natl. Acad. Sci. USA 106(51), 21619-
21624. (2009).
vii
Table of Contents
Abstract ii
Sammanfattning på svenska iii
Abbreviations v
List of papers vii
1. Nuclear Magnetic Resonance (NMR) 1 1.1. History of NMR 2 1.2. Theory of NMR 2 1.3. Experiments 7
2. Other analytical tools in wood science 11 2.1. Fourier Transform Infrared Spectroscopy (FT-IR) 11 2.2. X-ray diffraction (XRD) 12
3. Multivariate Data Analysis 12 3.1. Principal Component Analysis (PCA) 13 3.2. Partial Least Squares Projections to latent structures (PLS) 14 3.3. Orthogonal Signal Correction (OSC) 15 3.4. Orthogonal projections to latent structures (OPLS) 15 3.4.1. Interpretation of the OPLS models 16 3.5. Pre Treatment of data 17
4. Wood structure 18 4.1. Cellulose 19 4.2. Hemicellulose 20 4.3. Lignin 21
5. NMR in wood analysis 22 5.1. Solid-state CP-MAS NMR 22 5.2. 2D-NMR on wood 23
6. Dissolving pulp 26 6.1. Viscose process 27 6.2. Reactivity 28
7. Metabolomics 29 8. Result and Discussion 31
8.1. MVA analyses with 2D NMR on populous wood. 31 8.2. Investigation of dissolving pulp 34 8.3. Correlation analysis of 2D HSQC spectra (HSQC-STOCSY) 39
9. Future perspective and conclusion 44 10. Acknowledgements 46 11. References 47
viii
1
1. Nuclear Magnetic Resonance (NMR)
The first NMR spectrometer was commercially available more than 50
years ago. Since then, continuous technical development have steadily
increased the usefulness of NMR spectroscopy in many areas of
research.
This thesis focuses on development and application of methods to
combine the NMR method with multivariate data analyses to further
increase the application areas of NMR spectroscopy.
NMR has a wide range of applications, and the development of both
the spectrometer hardware and methods increase the number of
applications even further. Some of the current applications are:
Structure determination of organic molecules and
biomolecules (for example proteins and DNA). An advantage
this method has, in relationship to crystallographic methods, is
the possibility to examine these molecules in solution under
different conditions.
Analysis of complex mixtures of biofluids (with many
different compounds.)
Magic angle spinning (MAS) enables analysis of solid
samples, for example tissues and wood samples.
In medicine, magnetic resonance imaging (MRI) has shown a
large area of applications.
2
1.1. History of NMR
The phenomenon of Nuclear magnetic resonance (NMR) was
discovered in 1945 by two independent groups lead by Ed Purcell and
Felix Bloch and for their work Purcell and Bloch shared the Nobel
Prize in 1952.1,2 In 1951 the chemical shift was discovered by Packard
and Arnold, and they were followed by many new studies which led to
a rapid development in the field of NMR spectroscopy. For example,
the effect of electron spin populations by Overhauser3, the first 13C
spectra by Lauterbur4 and Holm5, the first two-dimensional NMR
spectra by Jeener and Ernst6,7, fourier transform NMR by
Ernst8,9(Nobel Prize in 1991). These new discoveries together with the
fast technical development, has made NMR important in studying
biological molecules structure and for his work within this area
Wüthrich10-12 was awarded the Nobel Prize in chemistry in 2002. In
2003 Mansfield13 and Lauterbur14 received the Nobel prize in
physiology or medicine for their discoveries concerning magnetic
resonance imaging (MRI). More recently, the introduction of
cryogenically cooled probe heads together with ever increasing field
strengths have further increased the sensitivity and resolution of NMR
spectra.
1.2. Theory of NMR
The theory of Nuclear magnetic resonance (NMR) is very complex
and quantum mechanics is needed to describe certain aspects of NMR
but for a basic understanding, the simpler vector model is a good start.
First, let’s discuss the origin of the signal we observe in NMR. As the
name nuclear magnetic resonance implies, we are observing signals
from atomic nuclei. If the sum of number of neutrons and protons is
odd, these nuclei will have magnetic characteristics and can be viewed
(somewhat simplistically) as small magnets with a magnet field
3
pointing in random directions. When we place a sample in a strong
external magnetic field, B0, the magnetic field of the nuclei will align
itself either parallel or antiparallel to B0. The parallel (+1/2 or α) state
have lower energy than the anti-parallel (-1/2 or β) state and will
therefore have a higher population. This creates a net magnetic field,
M0, pointing in the same direction as the external magnetic field. The
alignment of the random magnetic fields with the external field is seen
in Figure 1. This situation is true for spin ½ nuclei (explained below)
but can be more complicated for higher order spins. The most
commonly studied nuclei in NMR are 1H, 13C, 15N, 19F and 31P.
Common for these nuclei is that their spin quantum number, I, is ½.
This means that their magnetic quantum number, m, can adopt two
values m = 1/2 and m = -1/2 which corresponds to the - and -states
described above. All nuclei with I = ½ is referred to as spin half
nuclei.
Figure 1. The random magnetic nucleus aligns in a magnetic field.
The size of M0 is determined by the Boltzmann distribution, see
Equation 1.
Equation 1: N-1/2/N1/2 = e∆E/kT
4
Here N is the population of the higher (-1/2 or β) and lower energy
state (+1/2 or α) of the nuclei, T is the absolute temperature, k is the
Boltzmann constant and E is the energy difference between the two
spin states.
The population difference between the lower and higher energy state
is relatively small and even though a sample contains a gigantic
amount of nuclei, NMR is still considered a relatively insensitive
method. The small population difference is a direct consequence of
the small value of E. This energy difference is dependent on the
strength of the B0-field (figure 2) and this is the reason why we want
to use as strong magnets as possible in NMR spectroscopy but even
with state of the art instruments, the net magnetization from the
sample is very small. The central component of all NMR
spectrometers is a strong magnet that generates the B0 field. These
magnets are superconducting electromagnets cooled with liquid
Helium that can generate magnetic fields up to 25 Tesla (T). In
comparison, the earth’s magnetic field ranges from 25-65 µT
(wikipedia.org).
Figure 2. The Sensitivity of NMR increases with higher magnetic field.
In order to observe any signal from the nuclei, we need to perturb the
net magnetization from the equilibrium position. This is done by
irradiation of the sample with short pulse of frequency in the MHz-
5
range (radiofrequency). This will induce transitions between the α-
and β-states which gives us a means to manipulate the net
magnetization from the sample. In addition to aligning itself to B0, the
magnetic field of the individual nuclei will also precess around the
magnetic field (B0) with a frequency called the Larmor frequency
(Figure 3). This frequency is different for different nuclei, for example
all protons (1H) have a frequency of approximately 600 MHz in a
magnet with a field strength of 14.1 T.
Figure 3. Motion of a nucleus in a magnetic field.
The irradiation frequency is applied perpendicular to B0 and will
create its own magnetic field, called B1. So, in addition to the
precession around B0 the spins will also rotate around B1. This will
result in a rather complex motion of M0 but the key thing is that M0
will be tilted away from the direction of B0, Figure 4. After the pulse,
6
M0 will rotate with its Larmor frequency in the x,y-plane and this
rotating magnetization vector will induce a current in the receiver coil
located next to the sample. This oscillating current, called the Fee
Induction Decay (FID) can then be fourier transformed to create an
NMR spectrum. NMR would not be a very useful analytical tool if we
only observe one signal at the Larmor frequency for a certain nucleus
and this is certainly not the case. The reason for this is that the exact
frequency of a spin depends on its chemical environment within a
molecule leading to small alterations of the Larmor frequency called
the chemical shift. The frequency range that we normally are
interested in is the chemical shift which is in the kHz range instead of
the MHz range of the Larmor frequency. Therefore, the “carrier
frequency” which is the frequency of the RF-pulse is subtracted from
the FID before the fourier transform.
Figure 4. The net magnetization (M0) aligned along the magnetic field (B0). Radio
waves (RF pulse) will move the M0 around the X-axis.
7
1.3. Experiments
In this thesis three different NMR experiments have been used
extensively and will thus be explained in more detail. These
experiments are 1D 1H NMR, 2D 1H-13C Heteronuclear Single
Quantum Coherence (HSQC) NMR and solid-state 13C Cross-
Polarization Magic Angle Spinning (CP-MAS) NMR.
In a regular 1D-1H spectra only protons are observed (figure 5). If
maximum sensitivity and quantification of peaks is the aim of the
study this experiment is preferred. 1D 1H spectra are recorded
relatively fast and are quantitative (if appropriate parameters are
used). 1D-1H spectra are therefore suitable for projects with many
samples like metabolomics. The drawback is the amount of overlap
between peaks and a combination of the 1D-1H spectra with statistics
have often shown to be the best combination to solve this problem.15-
17
Figure 5. A typical 1D-1H NMR spectrum (acetylated cellobiose).
8
The 2D 1H-13C HSQC spectrum shows 1H connected to a 13C. The
extra dimension compared to a 1D spectrum will resolve a lot of the
overlap seen in for example a 1H spectrum of a complex mixture.
Also, the 13C chemical shifts give a lot of useful information for
assignment purposes. HSQC spectra have been used a lot for
characterization of both small organic molecules and proteins. For
mixtures, these spectra (figure 6) can be seen as a detailed chemical
fingerprint of the sample
Figure 6. Typical HSQC spectra showing protons (1H) connected to a carbon (13C).
This spectrum is the metabolites in the synthetic sample from article IV.
The basic principle of the HSQC (and the similar HMQC) experiment
is that the 1J-coupling between 1H and 13C is used to transfer
polarization from 1H to 13C. The 13C magnetization is then rotating in
the x.y-plane during the evolution period. Before acquisition,
polarization is transferred back to 1H. 13C-decoupling is active during
the acquisition time to increase the sensitivity of the experiment and
9
simplify the appearance of the spectrum. Figure 7 shows the pulse
sequence for a typical HSQC experiment using adiabatic 13C pulses.18-
20 Adiabatic pulses are pulses that vary in frequency with time during
the pulse. Their advantage is that they are relatively insensitive to
inhomogeneity in B1 and therefor especially suitable for in vivo
experiments.21
Figure 7. Pulse sequence for a modern HSQC containing adiabatic pulses.
The limitation for HSQC is the high concentration of sample which is
needed since the natural abundance of 13C is only a little more than
1% but the sensitivity of modern NMR spectrometers have enabled
the use of this information-rich experiment also for rather dilute and
complex samples such as biofluids.22,23
Solid-state 13C CP-MAS NMR spectroscopy has been used
extensively24-32 for cellulose and pulp analysis and is particularly
useful for analysis of cellulose crystallinity. NMR spectra from solids
have very broad line widths compared to NMR from a sample in
solution, see Figure 8.
10
Figure 8. Typical 13C CP-MAS spectrum. Sample from dissolving pulp.
The main reason for this is the large chemical shift anisotropy in solid
samples. To avoid this and simulate the isotropic tumbling of a
molecule in a solution, samples in solid-state NMR are subjected to a
mechanical rotation of the sample. Samples are rotated at high speed
(4-20 kHz) at the magic angle 54.47◦ compared to the magnetic field
see Figure 9. However, even at high rotation speeds, the line-widths
are significantly broader than in solution because there are other
factors responsible for broad lines that cannot be fully removed by
spinning.
11
Figure 9. Magic Angle Spinning (MAS). A solid sample rotating in an angle at
54.74◦ against the static magnetic field B0.
2. Other analytical tools in wood science
FT-IR and X-ray diffraction have been used in paper II and paper III,
respectively and although the actual analyses were performed by
collaborators I have included a short description of these methods.
2.1. Fourier Transform Infrared Spectroscopy (FT-
IR)
Even though the penetration depth is limited in FT-IR (ATR) the
method is often used in pulp and paper industry. It is used both in on-
12
line and off-line processes.33 Vibrational spectroscopy has high
sensitivity to steric factors. It has also been shown to have benefits in
robustness which makes FT-IR a good tool for classifying between
different types of discriminants.34 Another benefit for FT-IR is the
relatively low cost. The major drawback of FT-IR is that the obtained
spectral information is not always that easy to interpret. However,
structural composition of cellulose have been examined by Zhbankov
et al.35,36
2.2. X-ray diffraction (XRD)
X-ray diffraction has been used to study the crystalline structure of
cellulose for a long time.37 Compared with solid state 13C CP-MAS
NMR, XRD gives more information about the crystalline regions and
less information about the non-crystalline regions. Even though the
cellulose crystallinity has been studied for a long time, there are
uncertainties about the structures and unit cells of the different
polymorphs of cellulose.
3. Multivariate Data Analysis
In today’s science, it is common to work with data from a large
amount of samples and where many variables describe each sample.
In order to get an overview of such large and often complex data, and
even more so to interpret changes in the data, multivariate statistical
methods are required. Multivariate projection methods have
successfully been used for interpretation and modelling of complex
13
data. Common for these methods are that the data is arranged in a data
matrix, X with N rows (samples) and K columns (variables). For
projection methods to be useful, variables in the X matrix has to
correlate with each other, which is true in almost all cases with a high
K. By finding the underlying structures based on the variation in X,
projection methods models the variation in a few orthogonal latent
variables, which facilitates interpretation. Using NMR as an example,
each row in X is a sample spectrum and each variable refers to an
intensity value along the ppm scale. It is a high probability that peaks
occurring at different ppm values are varying in the same way, since
peaks from the same molecule show the same variation pattern over
all samples. A projection method could thus decrease the amount of
variables to a few latent variables or components focusing on the
variation in the data. By extracting the components describing a large
proportion of the variation in the data, the loss of information should
be small. In the thesis Principal Component Analysis (PCA), Partial
Least Squares Projection to latent structures (PLS) and Orthogonal
Projection to Latent Structure (OPLS) are the multivariate projection
methods used to the acquired NMR data.
3.1. Principal Component Analysis (PCA)
PCA is used for explaining as much as possible of the variation of the
data in as few components (principal components) as possible.38-42
PCA which is unsupervised, is suitable for a first overview of the data:
-finding trends, - outliers, - groups of observations with common
properties. Mathematically, the PCA is an orthogonal transformation
of the data matrix X into linearly uncorrelated variables (principal
components) meaning that the matrix X is divided into two reduced
orthogonal matrixes for scores T and loadings P, see equation 2. E is
here the residual variation in X not explained by the model. The score
values, ti, give the position of the object to the plane while the loading
14
values give the direction of the plane, see Figure 10.
Figure 10. Projection of observation in a three-dimensional space onto a two-
dimensional plane consisting of two principal components, PC1 and PC2.
Equation 2: X = TP’ +E
Each principal component PC, consists of one score vector ti and one
loading vector pi. By plotting the score values for each principal
component, against the score value for another principal component a
score plot is achieved. In this score plot, the pattern between different
observations is studied. With the corresponding loading plot, one
obtain knowledge about which variables that contribute to the pattern
seen in the scores.
3.2. Partial Least Squares Projections to latent
structures (PLS)
PLS is a supervised multivariate regression model used to find the
relationship between the matrix X and the response matrix Y.43,44
15
PLS uses the latent scores of the X matrix in order to describe both the
variation in X (see equation 3) and Y (equation 4). This is in contrast
to PCA which only uses the variation in X. The response matrix Y
could be either a class identity or a regular response value depending
on the problem addressed. The purpose can be both to predict Y from
X and/or to find the variation in X that have influence on the Y.
Equation 3 X = TPT + E
Equation 4 Y = TCT + F
3.3. Orthogonal Signal Correction (OSC)
OSC is a method for removing the systematic variations in X that are
uncorrelated to Y.45 OSC is suitable for removing these systematic
patterns before modelling. The drawback of this method is the
difficulties to validate the models of for example PLS when an OSC
filtering has changed the data and two different methods have to be
validated.
3.4. Orthogonal projections to latent structures
(OPLS)
Orthogonal projections to latent structures (OPLS) is refinement of
PLS by means of an integrated orthogonal signal correction filter of
the regression method Partial Least Squares.43 OPLS divides the
systematic variation of the response matrix (Y), into two blocks: the
predictive (Tp) component which describes the covariance between X
16
and Y and the orthogonal variance (To) which is the systematic
variation not related to the response Y (see Equation 5). 46
Equation 5: X = TPPP’ + ToPo’ + E
The separation between the predictive and orthogonal variation makes
the OPLS easier to interpret compared to the PLS. see Figure 11.
Figure 11. The difference between the PLS and OPLS method.
OPLS is commonly used for discriminant analyses (DA) e.g. to
investigate differences between two classes of samples and where Y
describes the class belonging. .
3.4.1. Interpretation of the OPLS models
Interpretation of the OPLS models is similar to interpretation of PCA
models. An example from 2D-NMR data is given in Figure 12 OPLS-
DA was used to discriminant between softwood (SW) and hardwood
(HW) based on their NMR spectral profiles, see paper II for more
details. The samples are separated into two groups in the OPLS-DA
score plot (Figure 12a) and in the corresponding loading plot (Figure
12b) interpretation of which peaks that contribute to the separation can
be carried out. It was concluded that these peaks stemming from the
molecule Mannose (M) were higher in spectra of softwood samples,
while other peaks where higher in the spectra of hardwood samples.
17
Figure 12. OPLS-DA model of 2D NMR data with type of wood (Hardwood and
Softwood) as discriminant. a) Score plot with SW colored in red and HW colored in
black. b) Corresponding loading plot that separates SW from HW. Positive p-values
colored in red and negative p-values colored in black.
3.5. Pre Treatment of data
Before modeling, some obligatory steps are necessary. These are
alignment of spectra and mean-centering. Mean centering (m.cent),
Equation 4, increases the interpretability of the model. Here xi is the
different variables.
Some optional steps that often are useful are scaling, normalization
and noise reduction. Scaling to unit variance (UV) is the most used
scaling. This gives each variable the same opportunity of influencing
the data. Equation 5 is auto scaling, which is a combination of UV
scaling and mean centering. Thus the UV scaling is done by dividing
the mean centered variables with their standard deviation (σ).
Equation 4. xixicentm .
Equation 5. /)(. xixiUVcentm
18
For 2D-NMR data, the pre-treatment of the data and the
transformation of the two dimensional data to an X-matrix are done
with an in-house script (see chapter 5.2).
4. Wood structure
Wood consists of three main components: Cellulose, Hemicellulose
and Lignin. The chemical composition, see table 1, varies between
softwood (e.g. pine and spruce) and hardwood (e.g. oak and birch)47
and the largest difference is the composition of the lignin where
hardwood lignin consist more of G- and S- with traces of H-unit,
while softwood are composed mostly of the G-unit.48
Table 1. Wood composition in softwood and hardwood.
Softwood (%) Hardwood (%)
Cellulose 40-44 43-47
Hemicellulose 25-29 25-35
Lignin 25-31 16-24
Extractives 1-5 2-7
19
4.1. Cellulose
Cellulose is the main constituent of wood and is a polymer of D-
Glucose units, see Figure 13, connected with (1→4) glycosidic
bonds and exists in several different allomorphs.49 Cellulose is
relatively stable (more crystalline) compared to other carbohydrate
polymers in the wood. Even though cellulose is very abundant and
thoroughly studied, the structures of the different polymorphs are still
under debate. This shows that this is a difficult task that still is an
ongoing project for many researchers.
Figure 13. Picture of the cellulose chain.
Native Cellulose is called cellulose I with the most common structures
cellulose Iα and cellulose Iβ. Wood has a higher amount of cellulose
Iβ compared to algae and bacteria. In the crystal structure of cellulose
I, the cellulose chains are ordered in a parallel fashion.
Cellulose II can be obtained by two different processes: regeneration
and mercerization of the natural cellulose I polymorph. This reaction
is irreversible and cellulose II is thermodynamically more stable than
the cellulose I structure.
In contrast to cellulose I, the chains in regenerated cellulose II are
arranged in antiparallel way consisting of a two-chain unit cell. There
are also indications of more hydrogen bonding between the sheets.
20
4.2. Hemicellulose
Some examples of hemicelluloses are xylan, glucoronoxylan,
arabinoxylan, glucomannan and xyloglucan. Hemicelluloses consist of
shorter chain lengths than cellulose. Other differences are that
hemicelluloses are branched in contrary to cellulose and they are also
amorphous. Hemicelluloses contain many different sugar monomers.
D-xylose and D-mannose are common building units (sugar
monomers). Most hemicelluloses are easily hydrolyzed and can
therefore be removed quite easily in the pulping process. The most
common hemicelluloses are glucomannan (figure 14 A) and xylan
(figure14 B). In softwood, galactoglucomannan is more common than
glucomannan.50
A)
B)
Figure 14. Representation of (A) glucomannan (R=H or CH3CO) and (B) xylan,
two of the most common hemicelluloses.
21
4.3. Lignin
Lignin is the second most common biopolymer in wood. The removal
of lignin is the key factor in the pulp industry. For industry there are
possibilities to affect the efficiency of the pulping process. Lower
amount of lignin or the ratio guaiacyl/syringyl without lowering the
defense or structure of the trees would decrease the cost of producing
the pulp.51 Lignin is a heterogeneous polyaromatic structure built out
of three different monolignols: paracoumaryl alcohol, coniferyl
alcohol and synapyl alcohol. The monolignins build up the lignin to
three different forms of lignin: guaiacyl (G), syringyl (S) and p-
hydroxyphenyl (PB), see Figure 15.48 These units can be linked in
various ways to create a very complex structure. The lignin has a
crucial role for mechanical support, resistance to diseases and water
transport.52 The polymerization buildup of the lignin have been
debated and two different theories exist. The first accepted theory is
that the lignin are built up by a radical coupling.53 The other
challenging theory is that the polymerization is under enzymatic
control.54
Figure 15. Chemical structures of the different structures commonly seen in lignin.
S = Syringyl, G = Guaiacyl and PB = para-hydroxybenzoate
22
5. NMR in wood analysis
5.1. Solid-state CP-MAS NMR
Solid-state CP-MAS 13C NMR have for a long time been used to
study the structure and characteristics of wood and especially
cellulose.1,25,26,31,55-59 The most common parameter studied is the
crystallinity index (CI) which describes the relative amounts of
crystalline and amorphous cellulose. In addition to 13C CP-MAS
NMR, also XRD, FT-IR and Raman spectroscopy can be used to
study cellulose crystallinity. Within each of these analytical
techniques there exist a couple of variations and the absolute numbers
for the determined CI can sometimes vary not only by the analytical
technique used but also as a result on exactly how the data has been
analyzed.60,61 Here a brief introduction to how the CI can be calculated
(or at least estimated) using 13C CP-MAS NMR is presented. The
method is based on the fact that the peak from position 4 (C4) in
cellulose appear as 2 well resolved peaks in a CP-MAS spectrum, one
for crystalline cellulose located at 89 ppm and one for amorphous
cellulose located at 84 ppm.24
The easiest approach is to integrate the crystalline and amorphous
peaks and calculate CI by dividing the crystalline peak area with the
sum of the area of the amorphous and crystalline peaks according to
equation 6. This is a simple approach but also very sensitive to
varying amounts of hemicellulose because peaks from hemicellulose
are overlapping with the C4 peak from cellulose, especially the
amorphous peak.
Equation 6: CI=C4cr / (C4cr + C4am)
Here, C4cr is the peak area of the crystalline peak and C4am the peak
area of the amorphous peak.
Another approach is to subtract a spectrum of amorphous cellulose
amorphous sample from the sample of interest. The amorphous
spectrum are subtracted after a scale factor leading to a residual
spectra which don’t have any negative parts.61 This method is also
23
relatively easy and quick to use. The peak deconvolution method is
another option based on spectral fitting method where line shape and
peak intensities are fitted to the experimental spectrum.to get more
accurate values of C4cr and C4am The peak deconvolution method can
be done with two peaks, the amorphous and crystalline peak at C4 but
in reality these two peaks consist of several components from different
cellulose polymorphs and hemicellulose. These components can also
be included in the spectral fitting. To get good reliable results from the
peak deconvolution method, good resolution of the spectra is
essential.
A clever approach to get rid of unwanted signals to be able to measure
CI is the linear combination method, by Newman and Hemmingson
that takes advantage of the difference in T2-relaxation rates between
the different components in wood.62 Two sets of spectra are recorded
for each sample, one normal CP-MAS spectrum and one spectrum
with a short spin-lock before acquisition. In the second spectrum,
signals from lignin and hemicellulose will be attenuated because of
their faster T2-relaxation. By a linear combination of the two spectra, a
sub spectrum containing only the cellulose peaks can be extracted.
Multivariate analysis can also be used on CP-MAS spectra to measure
crystallinity or at least to see if there is a variation of cellulose
crystallinity between samples.32,63,64
5.2. 2D-NMR on wood
In order to get high-resolution 2D NMR spectra of wood, it will have
to be dissolved in some way. In 2003 Ralph et al published a method
for dissolving wood samples.65 The purpose is to dissolve the cell wall
material with as little modification in structure of the different
polymers as possible and without doing any isolation/separation. This
method includes ball-milling, swelling in DMSO and N-methyl
imidazole (NMI), acetylation and precipitation into water. After that it
24
is possible to get fully dissolved (although quite viscous) NMR
samples with CDCl3 as solvent. Spectra with high resolution are
achieved with the drawback that the natural degree of acetylation is
impossible to examine.66 Since then a number of studies of wood
samples with 2D-HSQC experiments have been published.58,67-71
There are also other possibilities with direct dissolution of the wood
sample in DMSO-d6 directly after the ball-milling step.72,73 This
method increases the speed of sample preparation, with the possibility
to examine the natural acetylation. However the gel like samples gives
spectra with slightly broader lines compared to the acetylated samples.
In order to perform multivariate analysis of 2D NMR spectra some
special preparation of the data has to be performed. One approach is to
integrate all peaks of interest and create a peak list that can be used for
multivariate analysis.74 You could also work directly with the time-
domain data before fourier transform.75 Peak integration will
inevitably lead to some loss of information in crowded regions of the
spectra and we therefore opted for a different approach where the full-
resolution fourier transformed 2D spectrum is used without the need
of integrating peaks. The challenge was then to transform a dataset
containing a number of 2D spectra (a three-dimensional matrix) to a
two-dimensional matrix suitable for MVA. For this purpose we
developed a Matlab script with that can import processed spectra
processed in Topspin 3 (Bruker Biospin, Germany), unfold each
spectrum into a row vector and then place each spectrum as a row in
the final data matrix (see Figure 16 for an overview).76 The data
matrix can then be opened in another software for the MVA and we
have used SIMCA-P+ 12 (Umetrics, Sweden) for this purpose.
Loadings from the multivariate models can then be refolded in the
Matlab script to create a 2D loading plot with the same dimensions as
the original 2D spectra and even exported in Bruker format. This
allows us to take advantage of all features in Topspin to interpret the
loadings. Different scaling can of course be used in multivariate
models and this needs to be taken into consideration when the
loadings are visualized. For example, we often use UV-scaled data to
give all peaks equal weight in the model regardless of their intensities
in the original spectra. This is usually a good thing but will make the
interpretation harder because a UV-scaled spectrum looks very
25
different from a normal spectrum. We therefore added a feature called
back-scaling feature to the script. This procedure is basically the
reverse of the UV-scaling and the loadings from a model with UV-
scaled data are multiplied with the standard deviation of each variable.
This will lead to a 2D loading plot that looks like a normal spectrum
and we can also use this feature to choose cut-off values to visualize
only the most important variables in the model. When UV-scaled data
is used in a model, the loadings will have values between 0 and 1 (or -
1) depending on how important they are for the model. If a cut-off
value of for example 0.9 is used, only the most important variables
will be shown in the loading plot. This approach has previously been
used for 1D NMR spectra.15 This script has been used in papers I-III
and a modified version in paper IV. With this software it is also
possible to include regions of interest and to exclude unwanted
regions such as solvent peaks
26
Figure 16. Overview of the in-house script for multivariate analyses of 2D-NMR
data. 1) Pre-treatment steps such as alignment, normalization, scaling and unfolding.
2) Noise exclusion and exporting files to external software for MVA. 3) Loadings
from the models are back transformed to spectrum which can be visualized as a 2D
spectrum for interpretation.
6. Dissolving pulp
Dissolving pulp has a low amount of hemicelluloses and lignin
compared to other pulps. It is used in the production of regenerated
cellulose. The dissolving pulp is used to make many cellulose
derivatives, to mention a few: textiles, cellophanes, filter paper and
fillers.
There are two main process types used: The sulfite process and the
sulfate process (or Kraft process), see Figure 17.
The sulfite process produces a pulp with a high purity. The cations can
vary between calcium, magnesium, sodium and ammonium. To
further remove the amount of hemicellulose and lignin, additional
cooking steps is used. These additional cooking steps are necessary
for some wood types, for example pine where cooking steps with
different pH are used. The acidification of the pulp causes
depolymerization of the cellulose and hemicellulose chains.
In the sulfate process (or Kraft process), NaOH and Na2S are added to
remove the lignin. The sulfate process can process all wood types
which is an advantage compared to the sulfite process.
27
The dissolving pulp is suitable for manufacturing of viscose and
cellulose derivatives (for example the EHEC process) because of the
high purity of the cellulose.
Figure 17. Overview of the different processes for dissolving pulp, Sulfite and Kraft
(or Sulfate).
6.1. Viscose process
The viscose process is the most common method for preparation of
rayon. Cellulose is treated with NaOH and CS2. The result is called
cellulose xanthate. If dissolved in NaOH a yellow solution called
viscose is formed. This solution is forced through a spinneret and
allowed to cool in an acid solution coagulating to fine strands.
28
6.2. Reactivity
The reactivity of dissolving pulp can be seen as the accessibility of the
cellulose to different chemicals. Crystallinity, fiber length, amount of
hemicelluloses and viscosity are some of the factors that are supposed
to be important for the reactivity. There are some industrially used
methods to describe the reactivity. The most used method used to
measure the reactivity of dissolving pulp is the Fock method.77,78 In
short, the Fock method is the viscose process in lab-scale. A NaOH
(typically 9%) solution with excess CS2 is added to a known amount
of dry pulp to achieve a viscose-like solution. Cellulose is regenerated
by acidification. The cellulose is then quantified with oxidation and
titration. A high Fock value is high pulp reactivity. Another way of
examine the reactivity or quality of the viscose is filter value
determination. The viscose filter clogging value Kw was introduced
by Treiber.79,80 It is a quality and reactivity parameter where the
viscose is pressed through a specific filter under pressure. The Kw
value is related to the difference in volume of liquid that have passed
through the filter at two different times, and to the difference between
the two times. Instead of the Kw value a correction according to the
viscosity can be made. This is the viscosity corrected clogging value
Kr81, see equation 7. A low Kr indicates the quality of the filterability
of the viscose dope is increased.
Equation 7. Kr = Kw/0.4
Gel concentration is a photo analysis to determine the amount of
undissolved material (gel). Gel particles in viscose are generally
thought to consist of undissolved cellulose from the dissolving pulp.
Those can cause severe problems by blocking spinneret holes and
reduce the fiber quality by defects.
Cloud temperature (or flocculation temperature) is the temperature
when a clear solution of a cellulose derivative, typically a cellulose
29
ether, in water becomes cloudy when warmed82. Clarity (or visibility)
measures the colorization of undissolved particles. Clarity is measured
as the transmittance at 550 nm.
7. Metabolomics
Metabolomics aims to study and quantify cellular metabolites in
biological systems and is evolving. The hypothesis is that the
metabolites in question should describe the physical state of an
organism. The two most commonly used analytical techniques for
metabolite quantification are NMR and mass spectrometry coupled
with a separation technique such as gas or liquid chromatography
(GC-MS or LC-MS).
NMR is suitable for analyzing the complex mixtures we have in
metabolic studies. The samples in these studies are often different
biofluids such as urine, plasma, cerebrospinal fluid and saliva.
Compared to competing/complementary techniques, such as GC-MS
and LC-MS, NMR is insensitive. However, the techniqual advances
and experimental development of NMR, together with its benefits in
being none destructive, robust and relatively easy to quantify, imply
that NMR will be an even more important method in the future. In
addition to the biofluids there is also a possibility to analyze tissue
with magic angle spinning (MAS) experiments. The development of
NMR concerning both technique and new experiments will probably
increase the usefulness of NMR in metabolic studies and two
dimensional NMR is an area that probably will be more utilized for
metabolomics studies in the future. Even though the new SOFAST83-86
experiments are more or less only suitable for 2D 1H-15N HSQC, in
the future, there may exist similar experiments which are useful for
2D 1H-13C HSQC as well. This would have a large importance for the
usefulness of 2D-NMR (1H-13C) experiments because of the reduced
acquisition times. In many metabolomics studies with 1H NMR there
30
is severe overlap between peaks which makes it more difficult to
properly assign and quantify the data. In 2D-NMR experiments the
problem with overlap can be significantly reduced. In many metabolic
studies, there is easy to obtain a large number of samples. The
complexity of the spectra and large amount of data has divided the
field into two parts: Chemometric approach and quantified
metabolomics (also called magnetic resonance diagnostics or targeted
profiling)87. The biggest difference between these methods is that the
chemometric approach finds pattern with peaks that are important and
tries to identify, while the quantified metabolomics identifies peaks
and quantify them before any statistical method is applied. In the
chemometric approach there exist many methods to analyze the NMR
data. Studies have for example been done with: PCA, PLS, OPLS and
STOCSY.15,17,88-93
31
8. Result and Discussion
8.1. MVA analyses with 2D NMR on populous wood.
In paper I, we demonstrate how a combination of 2D 13C-1H HSQC
NMR spectroscopy and multivariate data analyses can be used to
visualize the difference between samples. In this case the purpose is to
study the difference between tension wood (TW) and normal wood
(NW) for Populus. This study was the first where 1H-13C HSQC data
from dissolved wood was evaluated with MVA and thus served as a
proof of concept of this approach. Sample preparation and data
analysis was performed as described in chapter 5.2. In short, these
were the steps involved.
1. Pre-treatment of data: Alignment, normalization and noise
exclusion.
2. Unfolding of 2D NMR data so that each spectrum is
transformed to one row in a data matrix.
3. Multivariate data analyses. Either PCA or OPLS-DA to
investigate variations between wood samples.
4. Refolding of loadings to 2D loading spectra analysed in
TOPSPIN.
The first analysis was the comparison of tension wood and normal
wood. Tension wood contains a cellulose-rich layer called the G-layer
and in total, tension wood therefore contains more cellulose than
normal wood. Since tension wood is so well studied, we already had
good knowledge about the differences in composition that we should
observe with more cellulose in tensions wood and relative higher
amounts of lignin and hemicellulose in normal wood. This
32
comparison was therefore a good model data set to show the power of
our method.
For the tension/normal wood problem the multivariate analysis was
done with a PCA on mean centred data and a good separation in the
score plot between the tension wood and normal wood samples was
obtained (Figure 18a). Red peaks in Figure 18b are positive and are
thus elevated in tension wood compared to normal wood and these
peaks corresponds, as expected, to cellulose. Blue and green peaks are
negative and correspond to lignin and hemicellulose.
Figure 18. PCA of HSQC spectra from tension wood and normal wood. A) Score
plot showing tension wood in red and normal wood in green. B) 2D loading plot
constructed from the loading vector along the first principal component (t1).
We were also interested in studying the lignin composition in more
detail. From the PCA in figure 18, no information about this could be
extracted because the difference in cellulose content is dominating the
model. A second PCA was therefore done where only the region of
the spectra where the aromatic peaks from lignin were included, figure
19.
33
Figure 19. PCA model on the aromatic region which separates the tension wood
samples (TW) against the normal wood (NW). A) Score plot with samples from TW
positive in t1 and NW negative. B) Corresponding loading plot where red peaks are
positive and black peaks negative.
In this model we could also see a separation between tension wood
and normal wood samples and the loading plot shows that we have
higher amounts of syringyl (S) lignin in tension wood. This result was
also expected based on previous knowledge about tension wood.94,95
An even clearer difference would probably be seen with an OPLS-DA
since the variation not correlated with the response would have been
removed.
The second part of this study was the investigation of wood from
poplar with a down-regulation of the enzyme pectin methyl esterase
(PME). The most interesting result from an OPLS-DA model
comparing the transgenic trees with wild-type was that we could see a
difference in S/G ratio of lignin. Samples from the transgenic trees
had a relatively higher amount of the syringyl compared to the
guaiacyl. These peaks are also highly important for the separation
between transgenic and normal tree, since they are visual even with a
cut off (0.5). The S/G ration is important for the paper industry since
the lower cost of remove the syringyl lignin. This result was not
expected but was confirmed by pyrolysis-MS analysis. The conclusion
from this study is that this approach of using MVA on 2D NMR data
from dissolved wood samples is a useful method.
34
8.2. Investigation of dissolving pulp
In paper II and III, dissolving pulps from different manufacturers
have been investigated. Paper II is focussed on the study of how
dissolving pulp samples with different characteristics such as
viscosity, raw material used in the production (hardwood or softwood)
and process type used (sulfite and sulfate) differ in their chemical
composition. 2D 1H-13C HSQC NMR, 13C CP-MAS NMR and FT-IR
was used in this study. In paper III we focussed on correlating
different reactivity parameters of these dissolving pulps to 2D HSQC
spectra and data from XRD.
The aim in paper II was to study a broad range of dissolving pulps
with different characteristics. This will give more information about
how the choice of raw material and process type will influence the
final product. The samples were analysed with FT-IR, solid state
NMR and 2D1H-13C HSQC NMR. These analytical methods were all
combined with a multivariate method, either PCA or OPLS. The
viscosity, raw material and process type were used as a response in
these models.
Unfortunately, no reliable models could be calculated for viscosity.
For raw material, models were achieved with data from both FT-IR
and 2D 1H-13C HSQC NMR
In the OPLS model on HSQC data with softwood (SW) and hardwood
(HW) as Y-variables, a decent model was achieved (Q2 = 0.4), see
Figure 20. Softwood samples colored in red are positive in tp1 and
have more of the positive peaks (red) in the corresponding loading
plot (Figure 20b). It is clear that Mannose (M) is highly important for
the model that describes the difference the softwood from hardwood
samples. Therefore, at least for these samples, dissolving pulp made
from softwood have relatively higher amount of mannose (from
glucomannan) than hardwood pulp The model of the FT-IR data was
harder to interpret but could nevertheless be used to classify
dissolving pulps made from softwood or hardwood.
35
Figure 20. OPLS-DA with softwood (SW) and hardwood (HW) as discriminant. a)
Score plot showing softwood pulps in red and hardwood pulps in black. b) 2D
loading plot where positive peaks are shown in red and negative peaks in black.
Also for process type (sulfite and sulfate), a good OPLS-DA based on
HSQC data was achieved with a Q2=0.64.
In the score plot (Figure 21a), samples from the sulfate process are
positive and therefore contains relatively more of positive (red) peaks
in the corresponding loading plot (Figure 21b). The sulfate process
have less of the negative peaks, so less of the reducing end of
cellulose and the sulfite process have the opposite – more of the
reducing end of cellulose (Cred α and Cred β). This loading plot is with
a cut-off at 0.6 showing only the peaks that have the highest impact on
the model. This implies that dissolving pulps from the sulfite process
have more of shorter cellulose chains.
36
Figure 21. OPLS-DA with process type as discriminant. a) Sulfate samples are
positive in the score plot and sulfite negative. b) Corresponding loading plot to t1. A
cut off at 0.6 used to plot only the peaks that are most important for the model.
The OPLS discussed above was made to look at specific parameters of
the dissolving pulps. An unsupervised PCA of both 2D HSQC and 13C
CP-MAS data was also performed and these models show that what
mostly differs between the samples that were analyzed, are which
producers they derive from. This is no surprise because there are many
steps in a process which are similar if derived from the same
producers. The solid-state 13C NMR combined with PCA is quite
interesting because difference in cellulose crystallinity could be
detected, Figure 22. This is something that cannot be detected by
NMR in solution.
The C4 and C6 peaks from cellulose are split up into two set of peaks,
crystalline (cr) and amorphous (am) part. Observations (observation 1
for example) in the bottom in Figure 22a are negative in t2 in the score
plot. That indicates that it has more of the negative peaks in the
corresponding loading plot p2 plotted in Figure 22b. From the loading
plot, it is clear that sample 1 (and to a lesser degree sample 3) has
more amorphous cellulose than the other samples.
37
Figure 22. PCA of 13C CP-MAS NMR data. a) Score plot. b) Loading plot showing
p2. CII is most likely a peak from the cellulose II polymorph and cr and am
corresponds to crystalline and amorphous cellulose, respectively.
In paper III the purpose is to correlate 2D 1H-13C HSQC data and
XRD data from dissolving pulp with different reactivity parameters.
Samples from different producers with different reactivity parameters
have been studied. As discussed in chapter 5.2, reactivity is very
important in order to further refine dissolving pulp into other products.
Multivariate models against the different reactivity parameters both
with 1H-13C HSQC NMR data and XRD data showed some interesting
result.
Perhaps the most interesting result is how well the composition of the
hemicellulose in dissolving pulp correlates to reactivity. 1H-13C HSQC
NMR data is very suitable to study difference in the amount of
hemicellulose. Figure 23a is the score plot with gel as response
(Q2=0.55). Positive samples in the score plot have more of positive
peaks in the corresponding loading plot. Samples to the right in the
score plot have higher gel values and these samples have more of
xylose (xylan).The score plot with Vis as the response is shown in
figure 23c). In the score plot, samples with lower Vis value are located
to the left. They have less of positive peaks in the corresponding
loading plot (figure 23d). So samples with low Vis value have low
mannose (glucomannan) content. It is no surprise that the amount of
hemicellulose affect reactivity of dissolving pulps but it is interesting
38
to note that this result implies that the amount of xylan and
glucomannan have different influence on these two reactivity
parameters.
Figure 23. OPLS with 1H-13C HSQC. a) Score plot of Gel as response. b)
Corresponding loading plot to gel. c) Score plot of Vis as response. d)
Corresponding loading plot to Vis.
The reactivity could also be correlated to crystallinity with XRD data
when the raw data was modeled against Fock and Vis (paper III). A
low visibility had higher crystallinity. For Fock values high reactivity
correlated to a low crystallinity.
We also attempted to calculate crystallinity of those pulp samples with 13C CP-MAS NMR using several different approaches (described in
chapter 5.1). All crystallinity values from both 13C CP-MAS NMR
and XRD are summarized in Table 2.
39
Table 2. Crystallinity index measured with different techniques comparing different
methods such as linear combination (lin comb), and deconvolution (dec) and peak
height (ph).
Sample NMR
Lin.comb.
NMR(dec)
Lin. comb
NMR(dec) XRD
(ph)
XRD
(dec)
Sample
1 67,1 65,3
62,9 64,6 52,3
Sample
2 68,0 63,4 62,2 56,7 49
Sample
3 65,5 63,3 59,5 59,1 49,7
Sample
4 67,7 66,4 68,9 67,2 54
Sample
5 66,8 65,1 61,7 60,9 51.4
Sample
6 67,3 65,2 64 68,6 55.5
Sample
7 65,1 63,5 61,4 70,5 56.8
Sample
8 67,1 64,4 71,1 59,7 56.6
Sample
9 67,1 65,2 68,3 58,2 51.1
When comparing the CI values calculated with XRD and NMR there
are no typical pattern. This is probably due to the low resolution of the
13C CP-MAS spectra (typical spectra seen in figure 8). Therefor we
chose to rely our crystallinity measurements on the XRD data.
8.3. Correlation analysis of 2D HSQC spectra (HSQC-
STOCSY)
40
The method used in article IV is an extension of the Statistical Total
Correlation Spectroscopy (STOCSY) method used in metabolomics
studies with 1D 1H NMR.15,17,90,96-100 The purpose of the STOCSY
method is to identify metabolites of interest by using the high
correlation for peaks that originate from the same compound. We have
implemented this approach with 2D NMR data and in addition to
metabolite identification; this method has a number of possible
applications. For example protein dynamics studies, identification of
individual components in mixtures and drug/target studies. The
reason why we wanted to develop the correlation analysis used in
STOCSY to 2D HSQC NMR data is to be able to take advantage of
the reduced overlap between peaks in this type of experiment.
Statistical Total Correlation Spectroscopy performed on 1H spectral
data is based on calculating a full correlation matrix, C, for a set of 1H
NMR spectra (equation 8).15,90,101 The correlation plot can then be
plotted as a 2D plot with “cross-peaks” between peaks that are
correlating to each other. The superficial resemblance between such a
plot and a 2D Total Correlation Spectroscopy (TOCSY) spectrum is
the origin of the name STOCSY.
Equation 8 C = XtX
In equation 8, C is the correlation matrix, X is the data matrix and n is
number of spectra.
Instead of calculating the full correlation matrix, an alternative
approach can been used where only one selected peak is correlated to
the other peaks, equation 9.
Equation 9 cpeak = vpeakt X
In equation 9, cpeak is the correlation vector, vpeak is the vector of the
chosen peak, n is number of spectra and X is the matrix with spectra.
Equation 9 has been used in Diffusion Ordered Projection
Spectroscopy (DOPY). 17
When it comes to 2D NMR data, we chose equation 8 because the full
correlation matrix C will be gigantic considering the very large
41
number of data points in 2D spectra. In this study, we have shown the
advantage of using this method for identification of individual
components in two sets of complex mixtures. One data set was a
synthetic metabolomics data set and the other a real metabolomics
data set from human biological fluid. The correlation analysis was
implemented in the Matlab script described in chapter 5.2, see Figure
24 and 25. In short, after the unfolding of the 2D spectra, one peak of
interest is chosen and cpeak is calculated. This vector is then refolded to
a 2D correlation plot with the same dimension as the original spectra.
This plot will show all the peaks that are correlated to the chosen
peak. The values of cpeak will vary between 0 (no correlation) and 1
(perfect correlation) and an appropriate cut-of value can be chosen to
only show the peaks originating from the same molecule. Figure 25
shows the actual graphical interface of the Matlab script and some of
the available features.
.
Figure 24. Description of the script for assignment of metabolic samples. Steps 1
and 2 are the same as described previously in paper I. 3) One peak of interest is
42
chosen from the raw spectra. This peak is correlated to the other peaks using
equation 8. 4) Refolding of correlated peaks higher than a manually chosen cut off
and plotting all of these peaks as an NMR spectrum.
Figure 25. Picture of the Graphical user interface for the HSQC-STOCSY method.
In the first part of the study, 28 common metabolites were mixed and
out of these 7 (2 in the same way) were varied according to a
fractional factorial design (26-2). This resulted in a total of 19 samples.
One peak from each varied metabolite was chosen in the correlation
analysis which resulted in six correlation plots. From these, all of the
varied metabolites could be assigned and the assignments were
confirmed by comparison with data from HMDB.ca. The correlation
plots were achieved with a correlation cut off above 0.8. The cut off
can easily be varied which also is needed for some metabolites
especially in a crowded area of the spectrum. Two examples of
correlation plots are shown in Figure 26.
43
Figure 26. Example of HSQC-STOCSY correlation plots. a) Assigned
Proline/Fructose spectra. B) Assigned α-D-Maltose spectra.
For the next part, real samples from a metabolic study were used to
study the assignments of these spectra. Plotting of peaks with high
correlations could be achieved and even though some of the molecules
didn’t exist in the HMDB.ca database the correlation plots are very
suitable for assignment of metabolic experiments. All of the
metabolites (both synthetic and real samples) were successfully
assigned using the HMDB.ca database.87,102-104
44
9. Future perspective and conclusion
With the new method to dissolve wood samples to achieve well
resolved 2D 1H-13C HSQC NMR published by the group of John
Ralph in 2003,65 a new and important area of scientific studies
opened. This method (used in paper I-III), combined with multivariate
statistics has proven to be a good method for analysing wood samples
and products. In paper I, the difference between tension wood and
normal wood was studied and this study showed that this approach is
very useful to investigate differences both in the carbohydrate and
lignin composition between different types of wood samples, for
example between different transgenic trees. We showed that
differences in the S/G ratio of lignin in a transgenic line of poplar
could be detected. The S/G-ratio is important because it has an effect
on how efficiently lignin can be removed from wood in the pulping
process.
Dissolving pulps from different producers have been examined in
Paper II-III. We showed with different spectroscopic techniques
(primarily 2D 1H-13C HSQC NMR) that there are differences in our
samples both according to raw material used in the production and
type of process (sulfite or sulfate). There are differences in
hemicellulose composition between softwood and hardwood pulp.
The other difference is that dissolving pulp from the sulfate process
have higher amount of shorter cellulose chains. These findings are not
revolutionary in any way in themselves but the fact that all of this
information can be found by a single NMR experiment on each
dissolving pulp sample is very promising.
Reactivity parameters for the dissolving pulp samples were also
correlated to 2D HSQC data and XRD data using MVA. XRD data
showed that crystallinity of cellulose has an effect on reactivity and
with 2D NMR data we could show that the hemicellulose content and
composition is important for reactivity.
45
Many of the analytical methods used today for characterization of
wood and pulp are very time consuming and expensive. The
spectroscopic methods used in these thesis will probably not replace
those (at least not in the pulp industry) but we have shown that NMR,
and specifically 2D HSQC NMR is a powerful tool in wood analysis.
More knowledge about cellulose and other wood components is still
needed and research in this field therefore continues to be important.
Comparing different spectroscopic methods according the cellulose
morphology is one subject that should be further examined. Inspired
by the multivariate approach it would, for example, be interesting to
examine and comparing the methods with O2PLS.
In paper IV we developed a new approach for identification of
individual components from 2D 1H-13C HSQC spectra on complex
mixtures. This is based on the STOCSY approach that has been used
on 1D NMR data but we have extended it to be used for 2D-NMR.
With this method we were able to extract sub spectra (or correlation
plots) of several different metabolites from a metabolomics data set.
These spectra could be unambiguously assigned
Such correlation plots could also be important in other areas such as
protein dynamics (and assignment) and drug/target analyses.
In HSQC spectra of wood, there are still peaks that are unassigned and
the use of 2D correlation plots could assist the assignment of more
peaks in these spectra.
46
10. Acknowledgements
Tack till alla som samarbetat och gjort det här möjligt!
Extra tack till..
Mattias, min handledare under alla år. Tack för chansen och vilken
fantastisk handledare du är! Jag har verkligen inte nånting att klaga på
under alla dessa år!
Henrik, min biträdande handledare. Tack för all hjälp, alla ideer och
roliga diskussioner. Man kan ju tro att jag följer efter dig men jag
hann i alla fall först tillbaka till Piteå.
Bertil och Peter för erat samarbete med dissolvingmassorna.
Nils, Andrãs och Tryggve och alla andra medförfattare till artiklarna.
Gruppen! Pär ,Anna, Elinarna, Lina.
Familjen Morssy, som lät mig bo hur mycket som helst!
Lättström, så många trevliga tillfällen...när flyttar ni till Piteå?
Moens, som delat skjutsning till alla träningar.
Pelle, Maria J, Barbro och Carina för all praktisk hjälp.
Beachgubbarna Micke, Isak, Tom och Rami som förgyllt
fredagslunchen med roliga matcher...
Ett extra stort tack till familjen! Mia, Melker, Sixten
och Svante...ni är ju bara bäst!
Resterande ur familjen.. Mamma, pappa, Anders med familj... och
givetvis Marianne, Rickard, Lasse och Monica. För all hjälp med
allting!.
47
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