Variation in the chemical composition of wheat straw: the role of tissue ratio and composition
Biotechnology for Biofuels
Variation in the chemical composition of wheat straw: the role of tissue ratio and composition
Samuel RA Collins 0
Nikolaus Wellner 0
Isabel Martinez Bordonado 0
Andrea L Harper 1 2
Charlotte N Miller 2
Ian Bancroft 1 2
Keith W Waldron 0
0 Institute of Food Research , Norwich Research Park, Colney, Norwich NR4 7UA , UK
1 Present address: Department of Biology, University of York , Wentworth Way, Heslington, York YO10 5DD , UK
2 John Innes Centre , Norwich Research Park, Colney, Norwich NR4 7UH , UK
Background: Wheat straw is an attractive substrate for second generation ethanol production because it will complement and augment wheat production rather than competing with food production. However, like other sources of lignocellulosic biomass, even from a single species, it is heterogeneous in nature due to the different tissues and cell types, and this has implications for saccharification efficiency. The aim of this study has been to use Fourier transform infrared (FTIR) spectroscopy and Partial least squares (PLS) modelling to rapidly screen wheat cultivars for the levels of component tissues, the carbohydrate composition and lignin content, and the levels of simple cross-linking phenolics such as ferulic and diferulic acids. Results: FTIR spectroscopy and PLS modelling was used to analyze the tissue and chemical composition of wheat straw biomass. Predictive models were developed to evaluate the variability in the concentrations of the cell wall sugars, cell wall phenolics and acid-insoluble lignin. Models for the main sugars, phenolics and lignin were validated and then used to evaluate the variation in total biomass composition across 90 cultivars of wheat grown over two seasons. Conclusions: Whilst carbohydrate and lignin components varied across the varieties, this mainly reflected differences in the ratios of the component tissues rather than differences in the composition of those tissues. Further analysis indicated that on a mol% basis, relative levels of sugars within the tissues varied to only a small degree. There were no clear associations between simple phenolics and tissues. The results provide a basis for improving biomass quality for biofuels production through selection of cultivars with appropriate tissue ratios.
Wheat straw; Lignocellulose; Composition; Biomass; Ethanol yields
Lignocellulosic biomass is recognized as an important
resource for the production of renewable energy, biofuels
and biochemicals [
]. Lignocellulosic biomass may be
obtained from many sources, from waste streams in
forestry and agriculture through to energy crops grown
for the purpose. However, there is concern that cultivation
of the latter may result in competition with food
]. Wheat straw is produced globally in large
]. It is an attractive substrate for second
generation ethanol production because it will complement
and augment wheat production rather than competing
with food production. As a result there has been much
research to develop biorefining technologies to pretreat,
enzymatically saccharify and ferment the constituent
sugars of wheat straw to produce ethanol and other
products. Lignocellulosic biomass, even from a single
species such as wheat, is heterogeneous in nature [
The chemical compositions may vary according to
constituent agronomic conditions, location and local climate
, in addition to heritable variation. This will have an
impact on the saccharification potential for production
of ethanol [
]. Assessing the chemical composition of
lignocellulosic biomass is therefore necessary for the
optimization of biorefining approaches [
analysis is also needed to provide a basis for future
breeding improvements not only for biofuel production, but
also for other potentially renewable products that can be
produced from straw components. These include fibres
], functional hemicelluloses [
] and phenolics such as
ferulic acid [
]. Whilst many cultivars of wheat have been
developed in order to optimize grain quality and yield
for human and animal consumption, there has been
little emphasis on developing the non-food components
for biorefining purposes. Unfortunately, wet chemical
analysis of large numbers of different samples is expensive
and time consuming. Hence there have been several
studies to evaluate the potential utilization of
spectroscopy in measuring (rapidly) the composition of feedstock.
For example Liu et al. [
] investigated the use of Fourier
transform near infrared spectroscopy (FT-NIR) techniques
to evaluate variability in biomass chemical composition
in corn stover and switch grass. Lindedam et al. [
demonstrated that FT-NIR spectra could be used to screen
sugar release and chemical composition in 20 cultivars
of wheat straw and further demonstrated considerable
varietal differences in sugar yield [
]. Lomborg et al.
] used 44 samples of wheat straw to demonstrate the
use of near infrared spectroscopy in quantifying key
carbohydrate components and lignin. Tamaki and Mazza
] demonstrated the potential to use Fourier
transform (mid) infrared (FTIR) spectroscopy to develop partial
least squares (PLS) models for predicting carbohydrates,
ash and extractives in two cultivars of wheat and triticale,
and used a similar technique to measure lignin in wheat
straw. In spite of these models, only one  has been
used to actually screen a range of wheat cultivars, and in
that case a very large degree of variation was found in
the results which related to digestible sugars rather
than original composition.
The aim of this study has been to use FTIR and PLS
modelling to develop a rapid method of evaluating the
levels of component tissues, the carbohydrate composition
and lignin content, and the levels of simple cross-linking
phenolics such as ferulic and diferulic acids. This approach
has been used to screen biomass samples from 90 cultivars
of wheat grown at several locations over two seasons, and
assess the variation within the lignocellulose, as well as the
correlations between components measured.
Results and discussion
Composition of wheat plant tissues
Wheat straw biomass consists mainly of lignocellulosic
materials, but the different parts of the plant have quite
distinct variations in their compositions [
]. In this
study, six selected cultivars, Cadenza (CAD), Paragon
(PAR), Savanah (SAV), Robigus (ROB), Charger (CHA) and
Avalon (AVA) were evaluated for their tissue yields and
compositions. The accessions used for model development
were selected from available seed stocks according to their
morphology and growth habit. They represent both winter
and spring wheat types, and their morphology includes
solid and hollow straw types and a range of plant heights.
It was anticipated that these accessions would capture
diversity for both tissue composition and cell wall
chemistry traits to facilitate the development of FTIR models
for screening these parameters. The proportions of the
air-dried component tissues are shown in Figure 1a.
The quantity of node tissue was small and showed little
variation between the plants, whilst the internode and
leaf tissues comprised the bulk of the biomass and varied
considerably. In ROB, the leaf tissue comprised about 50%
of the plant materials and was twice that of the internode
tissue at 25%. In contrast, in PAR and CAD the levels of
internode and leaf tissues were similar at about 40%. The
ear tissue was also significant at between 20 and 28% (%
mass fraction), but showed no trend in relation to other
tissues. For comparison, Jacobs et al.  in evaluating
the mass balance of tissues in winter wheat (Madsen
cultivar) found the ratios of internode:node:leaf to be
53.2:9.1:37.3% respectively, and Pearce et al. [
50:8:42% respectively (on an unnamed cultivar).
The dried tissues were milled (<250 μm); moisture
contents were measured and found to be constant at
7-8% (w/w). The milled materials were chemically
analyzed in this state without any treatments to remove
any extractable substances (and were thus representative
of raw whole material). The amounts of rhamnose, fucose,
arabinose, xylose, mannose, galactose, glucose, uronic acid
(as anhydro sugar equivalents) and a range of functionally
important cell wall phenolics including ferulic acid, a
range of diferulic acids, coumaric acid and lignin are
presented in Additional files 1 and 2: Tables S1 and S2.
The analyses showed the expected distinct differences
in tissue composition. Total carbohydrate levels were
highest in ear tissues at 62% w/w (67% w/w dry matter
(DM)) in SAV and lowest in leaf tissues at 46% w/w
(49% DM) in CHA. The main sugars were glucose and
xylose. Glucose was the dominant cell wall sugar and its
content was highest in the internode, ranging from 29%
w/w (31% w/w DM) in AVA to 44 w/w (48% w/w DM)
in CAD, and lowest in leaf tissue, consistent with earlier
] but demonstrating significant variation between
plants. The xylose content of internode, leaf and node
tissues was generally half the level of glucose, but was
significantly (approximately 30%) higher in the ear than
in the other tissues. Arabinose at between 1 and 3% DM
was lowest in the internode (where the xylans are poorly
branched) and about double that level in all other tissues,
reflecting the presence of highly substituted arabinoxylans.
Lignin (corrected for ash) content was highest in the
internode, and lowest in the node, probably reflecting the
requirement for the node to undergo controlled extension
to address lodging disturbances.
Uronic acid was present in all tissues, and will have
been derived predominantly from glucuronic acid found
in glucuronoarabinoxylans [
]. However some will have
originated from galacturonic acid in the small quantities
of pectic polysaccharides found particularly in the leaf
tissues. This was clearly indicated by the small but
measurable levels of rhamnose, which was highest in
leaf tissues and lowest in internode and ear tissues. The
internode-derived uronic acid was generally between 4
and 5% dry mass fraction. However, the leaf uronic acid
component varied considerably in the leaves and nodes
of the modelled tissues, ranging between 6 and 10%.
Mannose was present at its highest levels in the node
and internode compared with leaf tissue, and lower still
in ear tissue. These values reflect the predominance of
lignocellulose or hemicellulose in the stem. It is possible
that some mannose may have been derived from
hydrolysis and reduction of any residual sucrose present
in the tissues.
Lignin was measured gravimetrically and corrected
for ash in all tissues (Additional files 1 and 3: Table S1
and S3). There was considerable variation in content,
which related to both tissue type and cultivar. In nearly all
cultivars lignin was at its highest level in the internode
tissue, but varied from over 20% w/w in ROB down to
under 14% w/w in AVA. The level of lignin in the other
tissues was generally within 20% of that of the internode
value, but their relative levels also varied between cultivars.
For comparison, Additional file 3: Table S3 shows
published values for cell wall sugars and lignin from whole
wheat straw and constituent tissues from a number of
studies over the last 25 years. Notwithstanding minor
variations in the methodologies, it is important to note
that on a dry matter basis, the level of glucose in whole
straw ranges from under 30% w/w DM [
] to over 40%
w/w DM [
], and is reported as high as 44.8% w/w DM
in internode tissues [
], although that calculation was
gravimetric and by difference. Xylose and lignin values
vary considerably also (18 to 24% and 14 to 25% w/w
DM respectively). Such variation is consistent with that
found in the compositions of component tissues of the
six cvs reported in this study.
Phenolic esters were analyzed across the four tissues
in the six plant varieties for modelling. The distributions
are shown in Additional file 2: Table S2. The main phenolic
ester was p-coumaric acid (pCA) which, in all the cultivars,
was highest in the node tissues, ranging from 0.5% w/w in
PAR up to 1.8% w/w in ROB. In most cultivars, the leaf
tissue exhibited the lowest level of pCA, ranging from
0.25% w/w to 0.5% w/w in ROB, CHA and AVA. The
distribution of pCA in the internode and ear tissues varied
widely. The next most prominent phenolic ester was
trans-ferulic acid (FA). The levels differed considerably
between the cultivars, but were distributed in a similar
manner between the tissues. FA ranged from under
0.3% w/w in PAR to up to 0.6% in CHA and AVA. The
other main phenolic moieties comprised diferulic acid
species of which the 8-0-4’DiFA was generally highest
in leaf and ear tissues at about 0.1% (w/w) and lowest
in internode tissues. Small but significant levels of other
phenolics were identified, including vanillic acid and
vanillin. For each cultivar, the standard errors for the
phenolics data were quite noticeable (Additional file 2:
Table S2). However, this was not due to experimental
error, but due to strong variability between different
replicate plants used in building the model. For individual
plants, the errors were small (in the region of 2 to 4% of
the means). This contrasts with the sugars data which
gave low variation between plants of a specific cultivar
(Additional file 1: Table S1).
Development of partial least squares tissue models from
Fourier transform infrared spectra
Figure 2 shows representative Fourier transform infrared
attenuated total reflectance (FTIR-ATR) spectra of the
separated tissue types from wheat straw internode and
node, leaf, and ear spikelets. The varying chemical
composition of the tissues was reflected in distinct variations
between their FTIR spectra. The spectra of nodes and
internodes showed more prominent bands at 1590 and
1510 cm−1 than those of the leaf and ear spikelets. These
bands are generally attributed to lignin-like moieties,
although this did not directly reflect differences in Klason
ash-corrected lignin. In contrast, broad absorption bands
at 1630 and 1550 cm−1 indicated a higher amount of
protein in the latter two tissues, and the carbohydrate bands
at 1020 and 990 cm−1 were relatively smaller. The spectra
of these two tissues also lacked two smaller bands at 860
and 820 cm−1 in the anomeric region of carbohydrates.
These spectral differences were consistent across lines,
although band intensities varied between different samples.
Since whole wheat straw biomass is a mixture of these
tissues, it would be reasonable to assume that the spectrum
of the whole wheat is a linear combination of the
component spectra (the models were derived from tissues
dissected from whole plants which had not been subjected
to harvesting-related losses of loose and friable parts like
leaves). Hence a PLS model was created to quantify the
relative amounts of these tissues in wheat straw biomass.
The assumption was then justified by confirming that the
spectrum of a measured mixture of the four tissue powders
was equivalent to one obtained by digitally adding the four
%-weighted spectra of the individual components (results
not shown). Models made from the raw spectra performed
reasonably well. However some of the hemicellulose sugars,
notably xylose, exhibited a constant underestimation bias in
test set predictions. This was successfully addressed by
using first derivative spectra to eliminate nonlinear baseline
effects. In contrast, a fourth order polynomial spline
baseline correction did not improve the predictions.
Examples of correlations between measured and
predicted values for tissue proportions are shown in
Figure 3a-d. Satisfactory predictions with relative errors
between 6 and 8% could be made for the relative amounts
of internode, ear and leaf tissue. The prediction error for
the amount of node was adequate but greater (12%),
firstly, because this was by far the smallest constituent
and secondly, because the similarity of its chemical
composition with the internode is likely to have caused
some material to be misallocated.
Development of partial least squares chemical models from Fourier transform infrared spectra
A total of 28 chemical constituents of the wheat straw
were modelled using the calibration sample set listed in
relatively similar and could also be obscured by the much
bigger lignin bands in the region of 1400 to 1640 cm−1.
Therefore it was quite surprising that PLS models could
be made to work for a large number of these (Table 2),
albeit with relatively high prediction errors in the order
of 20 to 30%. Such an error is not surprising in view of
the level of variability between individual plants discussed
The quality of modelling can be compared with that of
other recent, relevant studies. Lomborg et al. [
] used a
wide range of approximately 100 whole straw samples
(down-sampled, milled to 1 mm) from a variety of sources
and different seasons to explore the use of FT-NIR
spectroscopy in determining chemical composition. They
reported %RMSEP (root mean square of prediction) values
of 11% for glucan and xylan, 13% for arabinan and 12%
for lignin, using 5, 5, 4 and 7 PLS factors respectively.
This relied on heavy use of outlier rejection (as much as
18% for lignin). A subsequent FT-NIR rapid analysis study
by Liu et al. [
] on corn and switchgrass (not wheat
straw) gave lower relative errors of 1.99, 2.3, 10.96,
7.53, 6.65, 3.62 and 13.95% for glucan, xylan, galactan,
arabinan, mannan, lignin and ash. FTIR has an
advantage over FT-NIR in that much more chemical
information is shown by the fundamental vibrations.
Using FTIR spectroscopy, Tamaki and Mazza [
relative prediction errors of 1.11% and 1.35% for total
glycans and glycan, 1.8% for xylan, 9.15% for galactan,
6.95% for arabinan and 23.8% for mannan. Tamaki and
Total Sugars (µg/mg)
10 20 30
Total PhOHs (µg/mg)
50 100 150 200
Lignin (corrected) ( µg/mg)
] also reported relative prediction errors of 2%
for lignin (10 to 11 PLS factors for glucan and xylan, 6
to 7 for arabinan and galactan, and 9 to 12 for lignin).
However, although their study involved whole straw
samples for triticale and wheat collected over two seasons
at different locations in Canada, they used only two to
three cultivars each, demonstrating inherently much less
variation. They explained the lower accuracy for smaller
components by the low concentrations and relatively
greater errors in their chemical analysis.
In the present study our results have shown similar
trends, with the minor components like mannose giving
worse predictions than the predominant glucose and
lignin. The prediction errors in our models are similar
to those of Liu et al. [
] and Lomborg et al. [
], and a
little higher than in the Tamaki studies [
However, compared to Tamaki and Mazza [
], this study
used fewer PLS factors. Increasing the number of the
PLS factors would have improved the RMSEC values
obtained by internal cross-validation. However, overfitting
the calibration set would have made the prediction errors
for independent samples worse. Averaging the input
spectra did not increase the prediction accuracy because
the replicate infrared spectra were already closely grouped,
and very few spectra could be considered as outliers. In
addition, we observed a marked increase in accuracy when
we reduced the number of different wheat lines in the
calibration set, and individual lines tended to model
extremely accurately. Nevertheless, the aim of this study
was to evaluate variation across a wide range of samples.
Hence the models were developed with six wheat lines
that had been preselected for high phenotypic variability
in order to maximize the potential for downstream
evaluation of field-grown cultivars (below).
Assessment of variation in 90 varieties of field-grown wheat
A set of 90 field-grown wheat cultivars with a spread
of genetic variation was grown over two seasons (see
Materials and Methods). The whole plants were
carefully harvested (to avoid loss of friable tissues such as
dry crumbly leaves) and milled to less than 250 μm
particle size. Moisture content was between 7 and 8%
(w/w). The milled samples were analyzed by FTIR and
the spectra were fed into the PLS models. The results
were then used to assess variation of chemical parameters
across the cultivars, and correlations between chemical
and physical parameters. In parallel, an additional set of
plants (five replicates) were assessed for key physical
parameters (dimensions and mechanical properties).
Correlations between different chemical moieties in the wheat lines
A correlation table was created to assess the
interrelationships between chemical data calculated from the models,
and selected physical data assessed post-harvest (Additional
file 4: Table S4). The correlation table highlighted areas of
positive (green) and negative (red) correlation. In this paper,
the data discussed are those of 2011. However, the trends
described were also shown in the 2010 results. Height and
stem length were, unsurprisingly, closely correlated (0.993).
Both of these characteristics were positively correlated with
the quantities of internode tissues (0.753 and 0.754) but
negatively with the proportion of leaf tissues (−0.630
and −0.629 respectively). There was no significant
association with the quantity of ear and node tissue. Figure 1b
shows the percentage tissue weights of the 270 replicate
samples from 2011 which have been ordered in increasing
weight of internode tissue. The level of internode tissue
ranges from under 30% air-dry weight to nearly 60% and
the proportion of leaf is inversely related. Node and ear
tissues show no obvious trends relative to the other tissues.
Additional file 4: Table S4 also includes correlations
between these physical parameters and the chemical
compositional data derived from the chemical model. Of
particular note were the relationships between the tissue
types (which had been independently derived from the
tissue model) and several key chemical components. The
most prominent of these are presented as correlation plots
in Figure 4. The levels of total sugars, glucose and xylose,
and to a lesser extent lignin, are positively correlated with
the proportions of stem tissue, and negatively correlated
with the proportions of leaf tissue. There is little correlation
with ear or node tissues. In contrast, the levels of galactose
and rhamnose are negatively correlated with stem tissue
and positively correlated with the proportion of leaf tissue,
reflecting the more pectin-rich cell wall chemistry. Again,
there is little correlation with node or ear. These results are
consistent with the observed correlation between glucose,
total sugars and xylose with stem height. They also strongly
indicate that much of the variation in carbohydrate and
lignin chemistry across the different cultivars is dependent
on the proportions of the component tissues, particularly
the ratios of internodes and leaves.
The interpretation of phenolic data was less clear, partly
because of the higher levels of error in the model as
discussed above. However, the correlation table suggested
a positive correlation between 8-0-4’DiFA and leaf, but
negative with internode tissue. These data are supported
by the chemical analysis of the six cultivars used for
developing the PLS models; the results (Additional file 2:
Table S2) show that 8-0-4’DiFA is often highest in the leaf
tissue. The conclusion is strengthened further by the
observation that of all the phenolics, the 8-0-4’DiFA
gave the lowest RMSEC as a percentage of the average at
16%. The interpretation is consistent with the observation
that the 8-0-4’DiFA is inversely correlated with lignin,
which is present in low levels in the leaf tissue but high in
the internode where the 8-0-4’DiFA is low. Interestingly,
in spite of the relatively high RMSEC values, many of the
diferulic acids showed good correlations with each other,
reflecting the commonality of synthesis during
peroxidative cross-linking within the plant cell walls.
Several additional positive and negative correlations
between the chemical components could also be detected.
Lignin (corrected or not) was negatively correlated with
nearly all of the diferulates (Additional file 4: Table
S4, −0.3 to −0.77), presumably reflecting the lack of a
phenolic cross-linking requirement in lignified stem
tissues and the reduced levels of lignin in
phenoliccross-linked leaf tissues. However, lignin was highly
correlated with xylose (0.73) but not arabinose, reflecting
the higher degree of lignification in xylan-rich cell walls.
Vanillin was highly correlated with ferulic acid, probably
reflecting the flux through phenolic synthesis pathways
common to both moieties [
Whilst the results highlighted the important role of the
tissue ratios in determining the overall straw chemical
composition, the modelled data could not provide any
information on variation within the tissues across the
cultivars. This is because the PLS models, whilst enabling
the levels of tissues and chemical components to be
assessed in whole plant material, could not provide any
indication of the chemical compositions of the individual
tissues. However, such variation could be evaluated from
the chemical analyses of the individual tissues from six
lines used in developing the models. Figure 5a shows
the mean values for carbohydrate compositions in the
different tissues from the modelling lines. The error
bars show significant variation, particularly for glucose,
reflecting a spread in the composition (as indicated also
in Table 2). However, presentation of the sugars data as
mol% (Figure 5b) shows very little variation. Hence,
although the overall levels of individual sugars in any one
tissue vary between cultivars on a total weight basis,
the ratios between the component sugars are almost
unchanged. This suggests that the cell wall carbohydrate
chemistry within wheat organs is highly conserved. The
variation in the overall composition is thus attributable
to changes in the relative levels of non-carbohydrate
components such as lignin, ash, and extractives (not
assessed) on an individual tissue basis, strongly
modulated by the relative ratios of the tissues themselves. In
addition, since significant quantities of leaves are often
lost during harvest due to conversion to dust, it is likely
that further variability will result. Variation in tissue
and chemical compositions is likely to have a significant
impact on the way in which the straw is best exploited,
whether it be for bioethanol production or for the
extraction of other components, such as hemicelluloses and
phenolics. Zhang et al. [
] have demonstrated that pure
leaf fractions of wheat straw were much less recalcitrant
compared to pure stem, and were easily digested by
commercial cellulase after moderate hydrothermal
pretreatment. Artificially-constituted mixtures of leaf and
stem tissues were found to require differing levels of
enzymes. The authors concluded that the leaf:stem ratio
is important when optimizing conversion processes and
additionally in feedstock breeding. Our present study
highlights the different ratios of leaf and stem within a
wide range of wheat cultivars, thus indicating that there
is significant potential for breeding wheat with varying
Using PLS models to rapidly quantify tissues and
chemistry of straw from 90 cultivars has demonstrated a wide
variation in chemistry which is strongly influenced by
relative levels of tissues, particularly stem and leaf. Glucose,
xylose and lignin positively correlate with stem proportion
and height, but negatively correlate with leaf tissue. Pectins
and diferulates positively correlate with leaf tissue but
negatively correlate with stem and height. Total polysaccharide
is also affected by the relative levels of non-carbohydrate
components. Polysaccharides within tissues are highly
conserved. The variation is likely to have significant
impact on the potential to convert the biomass into
biofuels or chemicals.
Materials and Methods
Wheat straw samples
Development of Fourier transform infrared models
Plants from six lines (cv Avalon, Cadenza, Charger,
Paragon, Robigus and Savannah; four plants per line) were
grown to maturity under greenhouse conditions at the John
Innes Centre, Norwich. After harvest, the plants’ physical
dimensions were measured and the grain and any ‘grain
husks’ removed from the ear using a scalpel, leaving the
spikelets. The remaining material was divided into four
fractions: internode, node (including the true node and leaf
base), leaf and ear. The leaves were connected at the node
and wrapped tightly around the stem, often passing the
next node along and completely enveloping it. Care had to
be taken to remove all the external leaf that was wrapped
around the stem. The plants were stored for two to three
months in an ambient room temperature atmosphere to
ensure air-dryness. Stems were cut at the ‘taper point’ above
and below the nodes, to leave separate nodes and
internodes. The share of these fractions in the total dry mass of
the plant was determined gravimetrically.
90 cultivars of wheat (listed in Table 3) were grown in the
UK at KWS UK, Rothamsted Research and 17
Velcourtmanaged farms, and harvested in the summer of 2010. All
lines were re-sown at KWS to enable a second year of
phenotyping, and material was harvested in the summer
of 2011. The field-grown wheat plants were cut at the
roots and dried in air at ambient conditions. The grain
was separated from the ‘waste stream’ tissues and the
whole straw used for analysis. For each cultivar, three
plants were harvested. These were left to dry for two to
three months to ensure air dryness before samples were
milled to less than 250 μm prior to analysis.
Sample homogenisation by milling
Wheat straw is a heterogeneous and highly structured
material. Because the applied analysis methods use only
small amounts of material, the straw was homogenized
in order to enable representative sampling. The
equilibrated air-dry (between 7 and 8% moisture) wheat tissue
fractions or whole plants were milled with a J&K MF10
analytical sieve mill (IKA®-Werke GmbH & Co. KG; Janke
& Kunkel-Str. 10; Staufen, Germany) to less than 250 μm.
Any remaining material greater than 250 μm was
remilled for 7 minutes with a J&K A10 grinder with a water
cooling jacket (IKA®-Werke GmbH & Co. KG; Janke &
Kunkel-Str. 10; Staufen, Germany) to less than 250 μm.
The milled powder was mixed thoroughly before being
measured by Fourier transform infrared (FTIR)
spectroscopy and analyzed for chemical composition.
Sugars were released from the fractions by hydrolysis with
H2SO4 (72% w/w) for 3 hours at room temperature,
followed by dilution to 1 mol L−1, and hydrolysis at 100°C
for 2.5 hours (Saemen method of hydrolysis [
Hydrolyzed monosaccharides were analyzed as their alditol
acetates by gas chromatography (GC) on a Perkin-Elmer
(Waltham, Massachusetts, USA) Autosystem XL (GC1),
Column: Restek Rtx-225, 30 m, 0.32 mm internal diameter
(ID), 0.25 μm column film thickness (df ), with flame
ionization detection (FID) [
] using 2-deoxyglucose
(200 μL, 1 mg mL−1) as an internal standard. Total uronic
acid content was determined colorimetrically by the
method of Blumenkrantz and Asboe-Hansen [
glucuronic acid as a standard. Each determination was
carried out in triplicate.
Phenolic acids were extracted from the samples with
progressively higher concentrations of alkali and quantified
using HPLC with a Perkin-Elmer series 200 LC pump,
Perkin-Elmer advanced LC Processor ISS200, Phenomenex
Column Luna 5 μ C18(2), 250 × 4.0 mm with pre-column,
and Perkin Elmer (Waltham, Massachusetts, USA) Diode
Array Detector (UV) [
]. Analytical grade reagents and
HPLC grade solvents were used.
Lignin was determined as 'Klason lignin' using the method
described by Wood et al. [
] with the addition of sample
stirring during the initial treatment with 72% sulfuric acid.
Subsequently the sulphuric acid was diluted to 1 M and
the polysaccharides were heated at 100°C to complete
hydrolysis, leaving as a residue Klason lignin (a mixture
of lignin, residual protein and ash).
Fourier transform infrared attenuated total reflection (FTIR-ATR) spectroscopy
All FTIR was carried out using ATR sampling.
FTIRATR spectra were measured with a BioRad FTS175
Fourier transform infrared spectrometer equipped with a
MCT detector and a GoldenGate (Specac; Orpington,
Kent, UK) single reflection diamond ATR accessory
(BioRad, Cambridge, MA, USA). Five replicates from
each milled sample powder were individually loaded on
the ATR crystal and pressed down with the clamp. For
each replicate, 64 spectra at 4 cm−1 resolution in the region
of 4000 to 800 cm−1 were averaged and referenced against
a spectrum of the empty crystal.
Partial least squares models
The spectra were analyzed with MATLAB V7.14
(MathWorks Inc., Natick, Massachusetts, USA). The spectral
range was truncated to 1800 to 800 cm−1 and any linear
offset was removed by zeroing the absorption at 1800
cm−1. Additional baseline correction was performed with
a fourth order polynomial anchored at the spectra
minima. All spectra were area-normalized after baseline
correction. First derivatives of the spectra were calculated
with a three point moving window. PLS models for each
variable were generated with the ‘plsregress’ function in
the MATLAB statistics toolbox V8.0 (MathWorks Inc.,
Natick, Massachusetts, USA). Internal 'leave one out'
cross-validation was used, both for individual samples
and blocks of samples from whole wheat lines. The
optimal numbers of PLS factors for the individual models
were determined from the percentage of explained
variation and residual errors.
Partial least squares model of chemical composition
PLS models were generated for total sugar content, and
separately for the contents of glucose, rhamnose, fucose,
arabinose, xylose, mannose and galactose, as well as uronic acid.
PLS models were made for contents of lignin, acid
insoluble ash, and acid insoluble ash corrected lignin.
PLS models were made for the contents of
protocatechuic acid, protocatechuic aldehyde, chlorogenic acid,
p-OH-benzoic acid, p-OH-phenyl acetic acid, vanillic acid,
caffeic acid, p-OH-benzaldehyde, truxillic acid (coumaric
acid), truxillic acid (ferulic acid), vanillin,
trans-p-coumaric acid 8,8'-DiFA (aryl tetralin), sinapic acid, FA,
cis-p-coumaric acid, 8,8'-DiFA, 8,5'-DiFA, cis-ferulic acid,
5,5'-DiFA, 8-O-4'-DiFA, 8,5'-DiFA (benzofuran), and total
phenolics (including unknown peaks).
The calibration set was chosen to deal with the wide
variety of sample parameters (Table 1). The set contained
whole-plant samples from eight field-grown wheat lines
(one plant each of cv Consort, Deben, Etoile-de-Choisy,
Extrem, Hustler, Orlando, Sperber and Steadfast) as well
as samples from six greenhouse-grown wheat lines (four
plants each of cvs Avalon, Cadenza, Charger, Paragon,
Robigus and Savannah) which had been dissected into
internode, node, leaf and ear. For these six lines, the
chemical compositions and spectra of the whole-plant
samples were calculated from the measurements of the
individual tissues and their percentage dry weight. By
using separated tissues, a wider range of concentrations
was available for calibrating the PLS models, as was
suggested in earlier studies [
]. In tissues, the total sugars
content ranged from 425 to 629 mg/g, compared with 463
to 574 in whole plants.
In order to account for ATR sampling variations, all five
replicate spectra from each reference sample were fitted
individually against the reference values. The replicate
field line sample spectra were subjected individually to the
PLS analysis, and their PLS prediction results averaged at
PLS model of tissue composition
In order to estimate the distribution of tissues within
biomass samples from field samples, a PLS model was
derived from the FTIR spectra and air-dried biomass
weights of the four tissues (node, internode, leaf and ear)
from each of four replicate plants from the six
greenhousegrown lines (cv Avalon, Cadenza, Charger, Paragon, Robigus
and Savannah). The tissue spectra from individual plants
were convoluted and fitted to the measured amounts (in
percentage weight) of the four tissues in the whole plants.
Spectra from the individual tissues were modelled as 100%
of the respective tissue type.
Additional file 1: Table S1. Carbohydrate and lignin composition of
component tissues of wheat straw cultivars.
Additional file 2: Table S2. Phenolic compositions of the component
tissues from six wheat cultivars.
Additional file 3: Table S3. Published data on composition of whole
wheat straw and component tissues.
Additional file 4: Table S4. Correlation table for 2011.
AVA: Avalon; CAD: Cadenza; CHA: Charger; DM: Dry matter; FA: Trans-Ferulic
Acid; FTIR: Fourier transform infra red; FT-NIR: Fourier transform near infrared;
GC: Gas chromatography; PAR: Paragon; pCA: Para-Coumaric Acid; PLS: Partial
Least Squares; RMSEC: Root mean square error of calibration; ROB: Robigus;
The authors declare that they have no competing interests.
KW, IB, SC, NW and AH designed the experiments and supervised the project.
SC, NW, CM and IM carried out the sample acquisition, preparation and
chemical analysis. NW and SC carried out the modelling. KW, NW and SC
drafted the manuscript. SC, NW and AH analyzed the data and critically revised
the manuscript. All of the authors read and approved the manuscript.
IFR and JIC receive strategic funding from the Biotechnology and Biological
Sciences Research Council of the UK. This work was supported by Integrated
Biorefining Research and Technology (IBTI, grant numbers BB/H00436X/1 and
BB/H004351/1) and the Institute Strategic Programme ‘Food and Health’
(grant number BB/J004545/1).
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