Qualitative and quantitative changes of beech wood degraded by
wood-rotting basidiomycetes monitored by Fourier transform
infrared spectroscopic methods and multivariate data analysis
Karin Fackler1,2, Manfred Schwanninger3, Cornelia Gradinger1,2, Barbara Hinterstoisser4 &
Kurt Messner2
1
Competence Center for Wood Composites and Wood Chemistry (Wood K plus), Linz, Austria; 2Institute of Chemical Engineering, Vienna University of
Technology, Getreidemarkt, Vienna, Austria; 3Department of Chemistry, BOKU – University of Natural Resources and Applied Life Sciences Vienna,
Muthgasse, Vienna, Austria; and 4Department of Material Sciences, BOKU – University of Natural Resources and Applied Life Sciences, Vienna, Austria
Correspondence: Karin Fackler,
Competence Center for Wood Composites
and Wood Chemistry, St Peter Strasse 25,
4021 Linz, Austria.
Tel.: 143 1 58801 17241;
fax: 143 1 58801 17299;
e-mail: [email protected]
Received 23 December 2006; revised
21 February 2007; accepted 22 February 2007.
First published online 28 April 2007.
Abstract
Beech wood (Fagus sylvatica L.) veneers were cultivated with white and brown rot
fungi for up to 10 weeks. Fungal wood modification was traced with Fourier transform near infrared (FT-NIR) and Fourier transform mid infrared (FT-MIR)
methods. Partial least square regression (PLSR) models to predict the total lignin
content before and after fungal decay in the range between 17.0% and 26.6% were
developed for FT-MIR transmission spectra as well as for FT-NIR reflectance spectra.
Weight loss of the decayed samples between 0% and 38.2% could be estimated from
the wood surface using individual PLSR models for white rot and brown rot fungi,
and from a model including samples subjected to both degradation types.
DOI:10.1111/j.1574-6968.2007.00712.x
Editor: Nina Gunde-Cimerman
Keywords
brown rot; FT-MIR; FT-NIR; lignin;
weight loss; white rot.
Introduction
The ability to measure brown and white rot decay in wood
and lignocellulosics is important for assessing degradation
of wood in service and for biotechnological applications
using these wood-modifying microorganisms such as biopulping (Akhtar et al., 1997; Aguiar et al., 2006). Infrared
spectroscopy provides representative information about the
composition of entire wood samples and thus serves as a
reliable method to monitor qualitative and quantitative
changes of wood during the cultivation with basidiomycetes.
Studies on fungal wood decay using mid infrared (MIR)
techniques have been carried out by several authors (Körner
et al., 1990; Faix et al., 1991; Ferraz et al., 2000; Pandey &
Pitman, 2003, 2004). Near-infrared (NIR) spectroscopic
techniques have been shown to be rapid tools for the
determination of wood components (Brinkmann et al.,
2002; Gierlinger et al., 2004; Schwanninger et al., 2004a;
Yeh et al., 2004, 2005) and for chemical changes associated
with selective white rot of spruce wood (Schwanninger et al.,
2004a). Kelley et al. (2002) were able to correlate chemical
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c
changes and weight loss of brown-rotted softwood with
differences in NIR spectra.
Multivariate statistical methods are common and often
inevitable when NIR spectra are subjected to quantitative
analysis (Boysworth & Booksh, 2001), but also the interpretation of fourier transform mid infrared (FT-MIR)
spectra may take advantage, when larger spectral regions
instead of single peaks or ratios between isolated peaks are
used (Ferraz et al., 2000).
The aim of this work was to monitor the physicochemical
changes in beech wood subjected to brown rot and white rot
fungal degradation and to evaluate them with fourier transform near infrared (FT-NIR) and FT-MIR spectroscopy
combined with multivariate methods of spectral analysis.
Materials and methods
Fungi
Ceriporiopsis subvermispora CBS 347.63 (selective white
rot), Gloeophyllum trabeum ZIM L017 (brown rot), Poria
FEMS Microbiol Lett 271 (2007) 162–169
163
Basidiomycete decayed beech wood monitored with FT-IR
placenta MAD 698 (brown rot) and Trametes versicolor
(simultaneous white rot) were maintained on malt extract
agar (MEA) slants, and precultivated for 14 days on MEA
plates before use.
Inoculation and incubation of beech veneers
Beech veneers (30 50 1.6 mm, 10–20 replicates, 20.3–
21.2% lignin) were steam sterilized for 15 min and soaked
for a few seconds in 2% (w/v) corn steep liquor (CSL,
Agrana, Austria) containing suspended fungal mycelium
(one MEA plate overgrown by the fungus was mixed in
150 mL 2% (w/v) sterile CSL in a Waring blender for 30 s at
full speed) before they were put in Petri dishes (9 cm
diameter) with 25 mL water agar (1.5%). Beech tooth picks
were used as supports to allow growth of the fungi throughout the entire wood surface. The Petri dishes were sealed
with Parafilms and incubated at 28 1C for 10 days or 3, 5 or
10 weeks. The aim was to obtain samples showing a high
variability of their lignin contents.
Sample preparation for chemical and FT-IR
analyses
After incubation, the fungal mycelia were removed from the
surface of the decayed veneers and the samples were dried
for 1 week at 50 1C. Then, they were milled (Retsch Ultra
Centrifugal Mill ZM 1000, fixed ring sieve, 80 mm holes) and
oven-dried at 50 1C for another week. Finally, the milled
wood samples were extracted according to Schwanninger &
Hinterstoisser (2002) and dried for another week at 50 1C.
Cyclohexane was used instead of benzene (Fengel & Przyklenk, 1983). Control samples (nontreated and steam-sterilized) were dried, milled and extracted in the same way.
Wet-laboratory lignin determination
The determination of the total lignin content (acid-insoluble lignin plus acid-soluble lignin) was carried out from
milled and extracted samples according to Schwanninger &
Hinterstoisser (2002). Each sample was analyzed four times.
Lignin content used in the paper refers to the total lignin
content.
FT-NIR and FT-MIR measurements
FT-NIR reflectance spectra were recorded at ambient temperature using a fibre-probe connected to a Bruker FT-IR
spectrometer (Equinox 55) (Schwanninger et al., 2004a).
Ten to 20 spectra from areas (9 to 10 mm2) of the solid
sample at random positions on the front and backside of the
veneers and four replicate spectra of milled samples and
extracted milled samples were recorded. FT-MIR transmission spectra (two replicates) of the milled samples and of the
milled, extractives free samples (KBr-pellets, 2 mg sample
FEMS Microbiol Lett 271 (2007) 162–169
and 200 mg KBr) were recorded on the same spectrometer
equipped with a DLATGS detector (32 scans per sample,
spectral resolution: 4 cm1). The collected spectra were
rationed against air (Schwanninger et al., 2004a). Furthermore, FT-MIR spectra from G. trabeum degraded wood
surfaces (64 scans per sample, spectral resolution: 4 cm1,
wavenumber range: 4000–850 cm1 using a single reflection
attenuated total reflectance (ATR) device (MIRacleTM, Pike
Technologies, www.piketech.com).
Data processing and analysis
Spectra were processed (smoothed and derived) according
to Savitzky & Golay (1964) by means of a 17-points
smoothing filter and a second-order polynom to obtain first
and second derivatives, respectively, using OPUS software
(version 5.0, www.brukeroptics.de). Partial least squares
regression (PLSR) models of the spectral data were calculated and optimized using the software OPUS QUANT 2. For
that purpose, the average spectra of the replicate spectra
were used. For calibration (cross validation), the infrared
data sets were regressed against the total lignin content and
weight loss, respectively, to find a model with high correlation (i.e. high coefficient of determination R2) and low root
mean square error of cross validation (RMSECV). To
estimate the quality of the prediction models, the number
of samples left out during cross-validation was stepwise
increased from one to five.
Furthermore, band height ratios of nonprocessed
FT-MIR absorption spectra A1505/A1738, A1505/A1375, A1505/
A1158 and A1505/A898 (the subscript indicates the wavenumber per centimeter) were regressed against total lignin
content. The band heights were measured from a baseline
drawn from 1920 to 850 cm1 (Schwanninger et al., 2004a).
Results
Fungal growth on wood, weight loss and lignin
content after fungal decay
Each of the fungal strains was able to colonize and degrade
beech wood. The highest weight loss occurred after 70 days
when T. versicolor degraded beech wood. The brown rot
species G. trabeum and P. placenta, whose preferred natural
substrates are coniferous woods (Goodell, 2003), led to
lower weight losses (Table 1). Their decay pattern was a
pocket kind of brown rot with dark brown degraded
rectangular spots embedded in seemingly nondegraded
wood (see Fig. 4a). This effect is reflected in a higher
variance of the spectral data obtained from brown rot
degraded veneer surfaces (see Fig. 4b). As veneers from
random positions of the stem were chosen, differences of the
initial wood composition are likely. Lignin contents of the
fungally decayed samples ranged between 17.0% and 26.6%.
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164
K. Fackler et al.
Sample
Untreated beech
Sterilized beech
C. subvermispora
C. subvermispora
C. subvermispora
C. subvermispora
C. subvermispora
C. subvermispora
G. trabeum
G. trabeum
G. trabeum
G. trabeum
G. trabeum
G. trabeum
P. placenta
P. placenta
T. versicolor
T. versicolor
T. versicolor
T. versicolor
Incubation
time (days)
Weight loss
(%)
Lignin contentw
(%)
10
21
21z
35
35z
70
10
21
35z
35
70
70
35
70
21
35
35
70
0.0
0.2 (ND)
2.5 (1.1)
3.5 (2.3)
0.2 (0.1)
9.4 (3.4)
3.6 (3.9)
28.4 (5.6)
1.1 (0.5)
2.0 (4.6)
0.0 (4.8)
5.9 (2.9)
28.1 (5.2)
22.6 (4.4)
0.4 (0.3)
11.6 (10.9)
7.4 (2.4)
21.9 (3.6)
14.3 (2.2)
38.2 (7.4)
20.3 (0.2)
20.3 (0.4)
18.7 (0.2)
18.1 (0.6)
21.3 (0.7)
20.4 (0.4)
20.3 (0.1)
17.0 (0.5)
21.2 (0.3)
21.9 (0.7)
21.6 (0.3)
23.0 (0.7)
26.6 (0.5)
25.1 (1.0)
22.3 (0.2)
22.3 (0.5)
21.1 (0.2)
20.0 (0.2)
21.8 (0.6)
23.5 (0.4)
SD in parentheses.
w
Based on extractives free wood.
Poor growth of the fungus.
ND, not determined.
z
NIR spectroscopy -- qualitative statements
Major changes in the band intensities after fungal treatment
were found near 5900 cm1. This spectral region is assigned
to the first overtones of aromatic C–H stretch vibration at
5935 cm1, to C–H stretch vibration of the lignin moieties,
and to the methyl groups of the acetylated hemicellulose
xylan at 5900 and 5865 cm1 (Shenk et al., 2001). To
accentuate the spectral differences, spectra were plotted in
the second derivative mode (Fig. 1): The overlapping lignin
and xylan bands result in a local maximum near 5980 cm1.
Furthermore, changes in the second derivative spectra were
found at the minimum near 5800 cm1, a spectral region
also characteristic for lignin and xylan ( CH2). Treatment
of beech wood with C. subvermispora led to a reduction of
the lignin content, represented by lower lignin signals in the
second derivative of the NIR spectra. Trametes versicolor
showed only small changes in this spectral region although
the weight loss was high during decay (38.2%). Gloeophyllum trabeum and P. placenta showed the expected picture of
brown rot with a relative increase of the lignin content,
indicating the almost exclusive decay of wood polysaccharides. Sterilization of the wood samples had only minor
influence on the spectra: hemicelluloses were slightly degraded or deacetylated indicated by a weaker signal near
5800 cm1 in the NIR second-derivative spectrum (not
shown).
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NIR second derivative
Table 1. Weight loss and lignin content after fungal treatment
5.0e−6
0.0
–5.0e−6
–1.0e−5
–1.5e−5
6200
6100
6000
5900
5800
5700
Wavenumber (cm )
Fig. 1. Second derivative of NIR spectra taken from extracted milled
beech wood after 10 weeks exposure to basidiomycetes: nondegraded
beech: solid line; Ceriporiopsis subvermispora: dotted line; Gloeophyllum
trabeum: dashed line; Trametes versicolor: dash-dotted line.
Estimation of lignin content from FT-NIR spectra
PLSR models using the spectral range between 6080 and
5800 cm1 were found to be optimal for the prediction of
the lignin content (Table 2). The usefulness of several datapreprocessing methods such as vector normalization (VN),
multiplicative scatter correction (MSC), first derivative
(FD), second derivative (SD) and combinations of them
was tested with the best given in Table 2.
The number of PLS factors of the models was one for the
milled samples and three for the veneers. The RMSECVs
(one leave-out sample) ranged between 0.61% for spectra
recorded from extracted milled wood and 0.95% for nonextracted milled wood. Predicted values and their differences to the measured values are given in Table 2. Increasing
the number of leave-out samples from one to five (25% of
the samples) did not lead to big changes of R2 and RMSEs of
the model based on the spectra from extracted milled wood
(R2 = 93%; RMSECV = 0.59%), whereas the PLSR model
based on nonextracted milled wood turned out to be less
stable: increasing the leave-out samples to five (25%) led to
an increase of the validation error to 1.2%. SDs of the
predicted lignin content from the four individual NIR
spectra ranged between 0.1% for milled and extracted
samples and 0.3% for nonextracted samples, indicating that
the main proportion of the prediction error was caused by
the experimental error of the wet-laboratory lignin determination, which had a mean SD of 0.44%. The average SD of
the lignin content predicted from the 10 individual veneer
surface spectra was higher (1.6%) than that of the reference
method. However, the SDs of the lignin content calculated
from the spectra of the individual samples ranged between
only 0.35% for untreated and seemingly undegraded veneers
and 3.46% for veneers degraded for 10 weeks with
P. placenta. Generally, brown rotted veneers had SDs
between 1.99 and 3.46%, reflecting the uneven decay of
their surfaces; SDs of white rotted veneers ranged between
0.43% and 1.43%. The fact that only about 3–6% of
FEMS Microbiol Lett 271 (2007) 162–169
165
Basidiomycete decayed beech wood monitored with FT-IR
Table 2. Spectral and statistic parameters of the PLSR models
Sample form
Component
Number of samples
Number of spectra
Spectral range (cm1)
Veneer surface
Lignin
20
20
6080–5800
Preprocessing of spectra
PLS factors in model
Cross validation
RMSECV (%)
R2 (%)
Veneer surface
Weight loss
20
20
10000–7274
Milled wood
Lignin
20
20
6080–5800
No
3
FD
1
SD
1
0.96
81
5.8
74
0.95
81
Sample-incubation time
Predicted ligninw
Untreated beech
Sterilized beech
C. subvermispora-10 days
C. subvermispora-21 days
C. subvermispora-21 days
C. subvermispora-35 days
C. subvermispora-35 days
C. subvermispora-70 days
G. trabeum-10 days
G. trabeum-21 days
G. trabeum-35 days
G. trabeum-35 days
G. trabeum-70 days
G. trabeum-70 days
P. placenta-35 days
P. placenta-70 days
T. versicolor-21 days
T. versicolor-35 days
T. versicolor-35 days
T. versicolor-70 days
21.2 ( 0.9)
21.1 ( 0.8)
19.4 ( 0.7)
19.7 ( 1.6)
20.3 (1.0)
19.4 (1.0)
19.6 (0.7)
18.7 ( 1.7)
21.3 ( 0.1)
21.1 (0.8)
21.3 (0.3)
22.3 ( 0.7)
27.5 ( 0.9)
24.3 (0.8)
23.8 ( 1.4)
23.1 ( 0.8)
21.3 ( 0.2)
19.3 (0.7)
21.2 (0.6)
22.2 (1.4)
0.3 (0.3)
0.1 (0.1)
4.5 ( 2.0)
3.6 ( 0.1)
1.4 (1.6)
13.7 ( 4.3)
2.7 (0.9)
20.1 (7.3)
1.4 (2.5)
2.8 (4.8)
0.8 (0.8)
13.7 ( 7.8)
29.9 ( 1.8)
13.8 (8.8)
14.1 ( 13.7)
14.5 ( 2.9)
9.5 ( 2.1)
21.7 ( 7.4)
14.4 (7.5)
28.2 (10.1)
Extr. milled wood
Lignin
20
20
6080–5800
21.0 ( 0.7)
20.7 ( 0.4)
19.2 ( 0.5)
18.8 ( 0.7)
19.6 (1.7)
20.2 (0.2)
20.5 (0.2)
19.3 ( 2.3)z
20.9 (0.3)
21.2 (0.7)
20.7 (0.9)
22.6 (0.4)
27.0 ( 0.4)
24.1 (1.0)
22.7 ( 0.4)
23.3 ( 1.0)
20.4 (0.7)
20.9 ( 0.9)
22.4 ( 0.6)
21.9 (1.6)
FD1MSC
1
0.61
96
20.5 ( 0.2)
21.1 ( 0.8)
18.9 ( 0.2)
17.7 ( 0.4)
21.4 ( 0.1)
18.9 (1.5)z
20.7 ( 0.4)
17.6 ( 0.6)
21.4 ( 0.2)
21.6 (0.3)
23.0 ( 1.4)
22.4 (0.6)
26.0 (0.6)
24.2 (0.9)
22.3 (0.0)
22.8 ( 0.5)
21.1 (0.0)
20.0 (0.0)
21.8 (0.0)
23.4 (0.1)
Milled wood
Lignin
20
20
1840–1400
914 856
VN
5
0.73
88
20.9 ( 0.6)
20.9 ( 0.6)
19.6 ( 0.9)
18.2 ( 0.1)
21.0 (0.3)
19.2 (1.2)
20.3 (0.1)
17.8 ( 0.8)
22.0 ( 0.8)
21.3 (0.6)
21.8 ( 0.2)
23.0 (0.0)
26.0 (0.6)
24.4 (0.7)
22.6 ( 0.3)
23.0 ( 0.7)
21.3 ( 0.2)
21.2 ( 1.2)
22.1 ( 0.3)
21.1 (2.4)z
Extr. milled wood
Lignin
20
20
1840–1400
914–856
VN
6
0.71
91
20.0 (0.3)
20.6 ( 0.3)
19.6 ( 0.9)
17.2 (0.9)
21.5 ( 0.2)
19.5 (0.9)
20.2 (0.1)
18.4 ( 1.4)
20.8 (0.4)
20.9 (1.0)
22.1 ( 0.5)
23.0 (0.0)
26.0 (0.6)
25.1 (0.0)
22.3 (0.0)
22.9 ( 0.6)
21.1 (0.0)
20.3 ( 0.3)
21.8 (0.0)
23.1 (0.4)
Extr.; extracted.
Vector normalization was carried out over the modelled spectral range.
w
Difference between the measured and the predicted value in parentheses.
z
Possible outlier.
the veneer surface was subjected to NIR measurements
obviously lead to the higher error of this method.
Estimation of weight loss from FT-NIR spectra
The shape of NIR reflectance spectra is not exclusively
determined by the chemical structure of the sample. Physical
parameters such as particle size, density and consequential
mechanical properties is also reflected by the spectra (Thygesen, 1994; Pasikatan et al., 2001; Tsuchikawa et al., 2005).
During fungal degradation, the density of wood decreases
before the wood cells collapse in late decay stages. The NIR
spectra of the veneer surfaces contain information about the
wood structure. Constant offset elimination of the NIR
spectra revealed the much lower overall reflectance that was
observed in the samples showing high weight losses (Fig.
2a). First-derivative spectra accentuate these differences
FEMS Microbiol Lett 271 (2007) 162–169
(Fig. 2b). This effect can be explained by the loss of IR
radiation in the porous material, which is neither absorbed
nor reflected into the NIR-probe, but scattered. Analogous
differences were not found in the spectra recorded from
milled wood. Correlation between weight loss and NIR
spectra was found in the whole FT-NIR spectrum between
10 000 and 4100 cm1. However the best models were
observed when the range of the second overtone of various
types of C–H bond vibrations and combination bands of
their first overtones between 8700 and 7200 cm1 (Shenk
et al., 2001) representing any organic matter in the wood
samples was included. The intensity of these bands is less
sensitive to qualitative changes of wood chemistry. A
one-factor PLSR model including this particular spectral
region showed a coefficient of determination of 74%
(RMSECV = 5.8%; Table 2). However, some samples – 5
weeks treated with P. placenta and 10 weeks treated with
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166
K. Fackler et al.
0.6
0.4
0.2
0.0
10 000
9000
8000
7000
6000
5000
4000
Wavenumber (cm )
NIR first derivative
1e−4
(b)
0
– 1e−4
– 2e−4
– 3e−4
10 000
9500
9000
8500
8000
Wavenumber (cm )
7500
Fig. 2. (a) NIR reflectance spectra of veneer surfaces (offset corrected);
(b) first derivative of the NIR spectra in the spectral region between 9000
and 7250 cm1. Sterilized beech (solid line); weight loss (WL) = 28.4%
after 10 weeks Ceriporiopsis subvermispora (dotted line); WL = 28.1%
after 10 weeks Gloeophyllum trabeum (short dashes); WL = 11.6% after
10 weeks Poris placenta (dash dotted line), and WL = 38.2% after 10
weeks Trametes versicolor (long dashes).
T. versicolor – are not well described by the model. Kelley
et al. (2002) found a good correlation between weight loss of
spruce wood after brown rot degradation and the NIR
spectra of milled samples. Their findings, however, were
mainly assigned to changes in chemical composition and
consequential colour changes of the brown rotted wood.
Bands that are negatively correlated to the weight loss were
assigned to carbohydrate bands. The number of PLS factors
used in their model calculated from milled samples was 2,
whereas the model from surface spectra described here
is based on only one factor. Kelley et al.’s (2002) findings
for brown rotted softwood could be confirmed for brown
rotted beech wood used in this study. A PLS model
calculated from cellulose-dominated bands in the NIR
region (7235–6106 cm1, 5384–4150 cm1) from the untreated, sterilized and brown rotted veneer surfaces gave an
R2 = 95% and RMSECV of 2.4% and was not applicable for
white rot fungi (R2 = 53%). Just as a model that was optimal
for white rot fungi – selective and simultaneous – with
R2 = 93% (RMSECV = 3.2%) had much lower correlation
with the weight loss of brown rot fungi (R2 = 51%).
FT-MIR spectroscopy -- qualitative statements
The biggest spectral changes after fungal degradation were
found at 1596 cm1 (aromatic skeletal vibration plus C = O
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stretch), 1505 cm1 (aromatic skeletal vibration) and
1462 cm1 (aromatic C–H deformation) all assigned to
lignin (Figs 3a and 4a). Selective white rot led to a decrease
of these bands (Fig. 3a). An increase of the bands at
1738 cm1 (C = O stretch of xylan), 1158 cm1 (C–O–C
asymmetric valence vibration in cellulose and hemicelluloses), the change of the absorption bands assigned to
cellulose at 1375 cm1 (C–H deformation in carbohydrates),
and 898 cm1 (C–H deformation of cellulose) were less
pronounced as would be expected (Fig. 3a and b). Faix
et al. (1991) reported an additional increase at 1646 cm1
(lignin assigned conjugated carbonyl groups) of white rotted
beech, which was also found to a certain extent in the FTMIR spectra presented here, but not as pronounced as in
their study. Unfortunately, they do not provide information
about the chosen baseline and whether the shown spectra
were taken from nonextracted or extracted milled wood
which do not make a comparison possible. However, an
interesting point is that they observed a decrease (14 weeks
treatment) while this study observed an increase (10 weeks
treatment) of the lignin content in the T. versicolor treated
samples. A decrease was observed after 5 weeks treatment in
this study (Table 1). In the 10-weeks-treated samples, the
decrease of the lignin content was also reflected in the double
peak assigned to syringyl ring vibrations and to the C–O
stretch of lignin and xylan near 1244 cm1 and in the shoulder
(a)
Abs.
(a)
1800
1750
1700
1650
1600
1550
1500
1450
1400
Wavenumber (cm )
(b)
Abs.
log [1/R]
0.8
1350
1250
1150
1050
950
850
Wavenumber (cm )
Fig. 3. FT-MIR spectra of extracted milled beech wood and beech wood
degraded for 10 weeks with basidiomycetes between (a) 1800 and
1400 cm1, and (b) 1400 and 850 cm1: extracted beech – black;
Ceriporiopsis subvermispora – red; Gloeophyllum trabeum – blue;
Trametes versicolor – green. Spectra were vector normalized in the
between 1850 and 800 cm1.
FEMS Microbiol Lett 271 (2007) 162–169
167
Basidiomycete decayed beech wood monitored with FT-IR
associated to aromatic skeletal vibration at 1123 cm1 which
both decreased (Fig. 3b). The latter one is pronounced in the
G. trabeum-degraded sample (Fig. 4), whereas the band at
1244 cm1, disappeared and the lignin-derived bands at
1268 and 1220 cm1 appeared (Fig. 4 spectra c–e). Although
the weight loss was high after the degradation with T.
versicolor, owing to the almost simultaneous degradation of
all major wood constituents, only minor changes occurred
in the fingerprint region of the MIR spectra. The increase of
the band at 1738 cm1 is probably caused by oxidative
processes (Goodell, 2003; Messner et al., 2003) rather than
by the relative increase of hemicelluloses. The wet-laboratory analytical results (Table 1) were confirmed by a slight
increase of the lignin band at 1505 cm1 that reflects the 2%
higher lignin content compared with native, untreated
beech, which was expected for brown rot degraded wood
(Fig. 3a). ATR-IR spectra from the surface of a beech wood
veneer unevenly degraded by G. trabeum (Fig. 4) reflect the
differences between seemingly nondegraded (a and b) and
ATR
(a)
a
b
c
d
e
1850
1650
(b)
1450
1250
Wavenumber (cm )
1050
850
a
b
c
dark brown degraded parts (d and e) of the sample, as
mentioned earlier.
Estimation of the lignin content with
transmission FT-MIR
The exclusively lignin-caused band at 1505 cm1 may serve
for the quantification of lignin when it is related to ‘pure’ or
carbohydrate-dominated bands (1738, 1375, 1158,
898 cm1) (Pandey & Pitman, 2004; Schwanninger et al.,
2004a). Other bands in the fingerprint region contain
further information about both the lignin and carbohydrate
content (1462, 1425, or 1244 cm1). PLSR models based on
five PLS factors for nonextracted milled wood and on six
factors for extracted milled wood, respectively, using the
spectral region 1850–1400 cm1 (VN) in combination with
the region of the cellulose band between 914 and 856 cm1
(Table 2) were calculated. Their correlation with the lignin
content was higher than that of the univariate regression
models calculated from various ratios of band heights of the
MIR spectra (Table 3). The best correlation between lignin
content and band height ratios for A‘lignin’/A‘carbohydrate’ was
obtained with the lignin reference peak at 1505 cm1 and the
carbohydrate band at 1375 cm1 (Table 3). Lower coefficients
of determination were observed using the BHRs A1505/A1738
and A1505/A1158. The C = O band at 1738 cm1 is sensitive to
hemicellulose degradation/ deacetylation and oxidative processes that occur during fungal wood decay (Goodell, 2003;
Messner et al., 2003), thus being not a good representative of
the carbohydrate content. The C–O–C band at 1158 cm1 is
known to be sensitive to changes in the crystallinity of the
cellulose, where the band decreases with decreasing crystallinity (Schwanninger et al., 2004b). It is expected that lower
ordered carbohydrate regions including the hemicelluloses
are easier to accessible and therefore preferred by the fungi.
On the other hand, particularly brown rot fungi reduce the
crystallinity of cellulose during their attack (Goodell, 2003).
d
Table 3. Univariate regression models using band height ratios of
original FT-MIR spectra
e
Sample form
5 mm
Fig. 4. (a) ATR-IR spectra from the surface of a sample unevenly
degraded by Gloeophyllum trabeum. Spectra of apparently nondecayed
veneer (a and b), decayed veneer (d and e) and the boundary between
decayed and nondecayed wood (c). (b) Photograph of the same veneer:
ATR-IR-measuring areas (a–e) are indicated as circles.
FEMS Microbiol Lett 271 (2007) 162–169
Component equation
Nonextracted wood meal
Lignin% = 38.0 (BHR) 9.2
A1505/A1738
A1505/A1375
Lignin% = 44.6 (BHR) 9.2
Lignin% = 49.1 (BHR) 3.3
A1505/A1158
A1505/A898
Lignin% = 6.3 (BHR)17.9
Extracted wood meal
Lignin% = 43.1 (BHR) 15.3
A1505/A1738
A1505/A1375
Lignin% = 58.6 (BHR) 17.5
Lignin% = 58.1 (BHR) 6.1
A1505/A1158
A1505/A898
Lignin% = 10.0 (BHR)11.5
R2 (%)
75
85
74
77
48
75
54
76
BHR, band height ratio of lignin and carbohydrate bands.
2007 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
168
Consequently, a lower correlation of this band was found.
Generally, the coefficients of determination were lower than
those reported by Pandey & Pitman (2004) who presented
several univariate models to determine the lignin content of
beech wood decayed by only one fungal species – the brown
rot fungus Coniophora puteana.
Concluding remarks
Using multivariate methods, changes of the lignin content of
beech wood caused by white rot and brown rot fungi can be
estimated with similar accuracy from NIR and MIR spectra.
R2 and RMSECVs depended mainly on the sample preparation: MIR and NIR spectra from extracted milled wood
correlated better with the reference method than those from
nonextracted meals, giving the lowest validation errors. NIR
spectra, however, can be recorded quickly and directly from
the sample surfaces or milled wood, making this method
more convenient for large numbers of samples. It was
possible to predict the lignin content as well as the weight
loss after fungal decay directly from FT-NIR spectra recorded from solid wood. Thus, the latter method has great
potential to serve as a screening method for selectively
degrading fungi of technological interest and for process
control of bio-pulping and related industrial cultivations of
lignocellulosic materials with basidiomycetes.
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