FEMS Microbiology Letters 209 (2002) 107^111
www.fems-microbiology.org
Use of NIR and pyrolysis-MBMS coupled with multivariate analysis
for detecting the chemical changes associated with brown-rot
biodegradation of spruce wood
Stephen S. Kelley
a
a;
, Jody Jellison b , Barry Goodell
c
National Bioenergy Center, National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, USA
b
Biological Sciences Department, 160 Hitchner Hall, University of Maine, Orono, ME 04469, USA
c
Wood Science and Technology, 5755 Nutting Hall, University of Maine, Orono, ME 04469-5755, USA
Received 26 October 2001; received in revised form 21 January 2002; accepted 25 January 2002
First published online 1 March 2002
Abstract
Near infrared (NIR) spectroscopy and pyrolysis-molecular beam mass spectrometry (py-MBMS) analysis can be used in conjunction
with multivariate regression and principal components analysis to differentiate brown-rot-degraded wood from non-degraded spruce and
to follow the temporal changes in wood undergoing brown-rot degradation. Regression of NIR test results vs. percent weight loss for
Postia placenta- and Gloeophyllum trabeum-infected spruce wood blocks yielded a correlation coefficient of 0.96. Regression of MBMS
test results for the same samples yielded a correlation coefficient of 0.96. Principle components analysis was used to differentiate noninfected wood and P. placenta- and G. trabeum-infected wood. These techniques may be used to detect different types of biodegradation
and to develop a better understanding of the chemical changes that the wood undergoes when it is subjected to brown-rot
biodegradation. ß 2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.
Keywords : Brown rot; Wood decay; Near infrared; Pyrolysis-molecular beam mass spectrometry ; Postia placenta; Gloeophyllum trabeum
1. Introduction
Decay and discoloration caused by fungi are major
sources of loss in value for both timber production and
wood in service, causing 15^25% loss in the value of standing timber and 10^15% loss in the value of wood products
during storage and utilization. A better understanding of
the chemical parameters involved in brown-rot decay is a
necessary prerequisite for developing methods of protecting wood from brown-rot degradation in a targeted and
environmentally friendly manner. This work seeks to characterize chemical changes associated with the brown-rot
biodegradation of wood.
Unlike the white-rot fungi, which attack all wood constituents often leaving a white, lignin-depleted residue, the
brown-rot fungi attack primarily the cellulose and hemi-
* Corresponding author. Tel. : +1 (303) 384 6123;
Fax : +1 (303) 384 6363.
E-mail address : [email protected] (S.S. Kelley).
cellulose of the wood leaving an amorphous, brown, crumbly residue of modi¢ed lignin. This modi¢ed lignin is distinguished by reduced methoxyl content and increased solubility. The lignin residues remaining after white-rot
degradation are frequently characterized as having undergone extensive side chain oxidation and aromatic ring
cleavage [1,2], whereas brown-rot residues are enriched
in hydroxylated phenyl (catechol derivatives) substituents
as a result of methoxyl demethylation or demethoxylation,
with only a small degree of side chain oxidation [3]. It has
been established that non-enzymatic oxidative degradation
processes are involved in the brown-rot attack of lignocellulose [4^6]. The overall result of brown-rot attack is
wood, which is virtually devoid of hemicelluloses ; characterized by depolymerization and loss of the cellulose and
by residual lignin with decreased methoxyl content, increased aromatic hydroxyls, and an increase in the content
of oxygen due to the formation of conjugated carbonyl
and carboxyl groups [7]. Speci¢c observations on spruce
wood show a two-fold increase in lignin carboxyl and keto
groups and a decrease in biphenyls and diaryl ethers [8].
There are a variety of sensitive spectroscopy methods
0378-1097 / 02 / $22.00 ß 2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 1 0 9 7 ( 0 2 ) 0 0 4 9 4 - 9
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S.S. Kelley et al. / FEMS Microbiology Letters 209 (2002) 107^111
that have been used to characterize the chemical composition of biomass, wood, and wood components [9^12].
Relative to wet chemical methods, spectroscopic methods
are much faster, and potentially give a more complete
picture of all the components in the wood structure
[9,13]. The utility of near infrared (NIR) spectrometry
for rapid measurement of chemical and physical properties
of wood has been demonstrated and developed for a wide
variety of di¡erent biomass sources [9^12,14,15]. Several
of these researchers have applied NIR to the characterization of decayed foliage [14,15]. It has been suggested
that NIR could be used to measure the strength of dried
samples taken from utility poles exposed to soft rot decay
[16]. Chemical changes in cellulose induced by exposure to
temperatures between 120 and 160‡C have been monitored
with NIR [11].
Pyrolysis mass spectroscopic (pyMS) methods for studying wood have also been extensively developed [17^19].
These methods can be used to quantify the chemical composition of wood as well as study subtle di¡erences in the
chemical structure of woody biomass as it undergoes
brown-rot biodegradation. Several recent reports have
demonstrated the use of pyMS for studying soil organic
matter [20,21].
The goal of this work was to evaluate the utility of using
these two spectroscopic techniques to study wood subjected to brown-rot decay fungi.
2. Materials and methods
2.1. Wood-block preparation
Wood blocks were prepared following a modi¢ed
ASTM procedure [22]. The feeder strips in each jar were
inoculated with either Postia placenta (Mad-698-R) or
Gloeophyllum trabeum (Mad-617-R). In non-inoculated
jars, wood blocks were incubated with cubes of sterile
malt agar on the feeder strips. The wood used was red
spruce sapwood (26U26U13 mm) cut from the same
tree. Blocks were allowed to incubate from 1 week to
8 months. Wood blocks were harvested aseptically, mycelia were removed from the surface and the change in
weight determined on the basis of dry weight. The wood
was ground in a Wiley mill (40 mesh screen) before NIR
or pyrolysis-molecular beam mass spectrometry (pyMBMS) analysis.
2.2. NIR analysis
The visible/NIR spectra (350^2500 nm) were acquired
with a FieldSpec FR Spectrometer made by Analytical
Spectral Devices. The instrument’s ¢ber optic probe gathers re£ected radiation from samples illuminated by a DC
light source. Thirty individual scans were averaged for
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each re£ectance spectrum (0.1 s/spectrum) and two spectra
were taken from each sample. The re£ectance spectra were
converted to absorption spectra and used directly for
quantitative analysis.
2.3. Py-MBMS analysis
The py-MBMS analyses were conducted using a pyrolysis furnace coupled to a free-jet molecular beam mass
spectrometer. Ground samples (20^30 mg) were pyrolyzed
in a furnace that was preheated to 550‡C. The molecular
fragments were swept out of the furnace into the MBMS
with an argon gas stream. The gas stream was expanded in
a series of three vacuum chambers to quench most intermolecular collisions. A low-energy electron beam (23 eV)
in the triple quadruple mass spectrometer produced a positive ion mass spectrum. The analysis was conducted with
fragments between 50 and 325 amu, with 1 amu resolution, providing 275 data points for the quantitative analysis. The py-MBMS protocol is described in detail elsewhere [17].
2.4. Multivariate analysis
While a complete description of multivariate analysis
can be found elsewhere [23,24] the following summary
describes the steps used to construct projection to latent
structures (PLS) (also known as partial least squares)
models in this work. Multivariate analysis was performed
using The Unscrambler0 (CAMO, Corvallis, OR, USA).
For PLS analysis, the NIR spectra or py-MBMS spectra
were combined into two di¡erent data X-matrices while
information on the exposure times were used as the
Y-matrix. The software was used to systematically extract
(decompose) variation in the data X-matrix while principal
component regression was used to regress each response
variable onto the decomposed spectra, and make a PLS.
This process allowed for the simultaneous and independent decomposition of both the X- and Y-matrices and
then performed the regression of the Y-matrix onto the
X-matrix, e.g., weight loss onto spectral features. The
weight-loss data were randomly assigned to one of two
sets. The calibration set (CALB) contained 23 samples
while the test set (TEST) contained 11 samples. The
CALB set was used to construct a PLS model. The
TEST set, whose samples were not included in the
CALB model, was then used to test the PLS model. The
CALB models were constructed using a full cross-validation approach. The PLS models and predictions from
these models were all based on two latent variables. The
quality of the models was evaluated by comparing the
correlation coe⁄cient (r) and root mean square error of
calibration (RMSEC) and root mean square error of prediction (RMSEP) [23]. All of the PLS models reported
here are based on only two principal components.
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S.S. Kelley et al. / FEMS Microbiology Letters 209 (2002) 107^111
Fig. 1. Plot of NIR spectra of spruce samples exposed to decay fungus
G. trabeum for varying lengths of time.
3. Results
Weight losses over the time period of the test ranged
from 0.0 to 0.4% for the non-inoculated controls and from
0.3 to 70.7% for G. trabeum-infected wood and from 0.2 to
54.6% for P. placenta-infected wood. As would be expected, weight loss increased over time of incubation.
Fig. 1 shows changes in the NIR spectra as a function
of incubation time which was highly correlated to wood
weight loss. Curves are given for 0, 2, 4, 8 and 16 weeks
of incubation for characteristic brown-rot infection by
G. trabeum. Similar results (data not shown) were seen
for wood degraded by P. placenta. There were several
changes in the NIR spectra of wood as the decay time
increased. These changes included an increase in brown
color (600 nm) and a decrease in hydroxyl vibrations associated with carbohydrates (1490 and 2100 nm) [11,25].
There were also changes observed in the lignin peak associated with the wood hydroxyls and hydrogen bonded
water (1920 nm) [25].
The results of both the CALB and TEST models for
measured weight loss vs. predicted weight loss are shown
in Fig. 2A. The quality of the models as measured by their
r values, the RMSEC and the RMSEP are shown in Table
1. For this analysis the G. trabeum and P. placenta samples
were considered together, although a slightly better correlation could be achieved if the two fungal species were
treated separately (not shown). These results show a very
strong correlation between the measured and predicted
weight loss. The RMSEP shows that NIR can be used
109
to predict weight loss of an unknown sample with an accuracy of þ 6 weight percent. The accuracy of the prediction would improve with a larger TEST set.
The chemical features (regression coe⁄cients) that drive
the correlations in Fig. 2A are shown in Fig. 2B. These are
the NIR spectral vibrations that are strongly correlated
with weight loss in decayed wood. NIR is particularly
sensitive to subtle di¡erences associated with hydrogen
bonding in wood, and di¡erences between carbohydrate
and lignin hydroxyls, all which might be impacted by
the decay process. Three major peaks showed a negative
correlation with weight, e.g., they decreased in intensity as
weight loss increased. All three peaks, 1480, 1920 and 2090
nm, were assigned to carbohydrate vibrations [11,25]. A
fourth major peak in the regression coe⁄cients showed an
increase in intensity with weight loss. This peak was associated with the color increase characteristic of these decay
fungi. The same carbohydrate vibrations dominated the
regression coe⁄cients even when only the NIR region of
the spectra (1000^2500 nm) was used for the PLS analysis
(not shown).
The strong correlation coe⁄cient between predicted and
observed wavelength indicates that the NIR spectra are
very sensitive to changes in the wood structure that are
associated with degradation, as measured by weight loss,
in brown-rotted wood samples. The PLS regression coef-
Table 1
Parameters of the PLS models from NIR and py-MBMS data
CALB
NIR
py-MBMS
TEST
r
RMSEC
r
RMSEP
0.98
0.98
5.1
2.3
0.96
0.96
5.9
5.1
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Fig. 2. A: Results of the PLS model for measured weight loss and the
weight loss predicted with NIR. The ¢lled symbols indicate the samples
used in the CALB set, the open symbols indicate the samples used for
the TEST set. B: Regression coe⁄cients for the PLS model showing a
correlation between NIR spectra and weight loss of decayed wood.
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110
S.S. Kelley et al. / FEMS Microbiology Letters 209 (2002) 107^111
in mass 168. Both of these changes are consistent with
oxidation of the basic lignin structure [17].
4. Discussion
Fig. 3. A: Results of the PLS model for measured weight loss and the
weight loss predicted with py-MBMS. The ¢lled symbols indicate the
samples used in the CALB set, the open symbols indicate the samples
used for the TEST set. B: Regression coe⁄cients for the PLS model
showing a correlation between py-MBMS spectra and weight loss of decayed wood.
¢cients suggest that this correlation is driven by the loss of
carbohydrates and the relative proportional increases in
lignin.
G. trabeum and P. placenta samples were also examined
by py-MBMS. The py-MBMS spectral data can also be
treated with PLS techniques and used to predict the
weight loss of G. trabeum and P. placenta samples. Regression of py-MBMS spectral data against the actual weight
loss for both the CALB set and TEST set are shown in
Fig. 3A. Again, samples exposed to both types of decay
fungi were used for one regression model, although principal component analysis (not shown) suggested that there
were subtle chemical di¡erences between wood samples
exposed to the di¡erent fungi. The quality of the models
as measured by the r values, the RMSEC and the RMSEP
are shown in Table 1. These results show a very strong
correlation between the measured and predicted weight
loss. The RMSEP shows that py-MBMS can be used to
predict weight loss of an unknown sample with an accuracy of þ 5 weight percent.
The regression coe⁄cients for this PLS model (Fig. 3B)
show that changes in the py-MBMS spectra with increasing decay time are driven by the loss of carbohydrate
(masses 85, 114, 126) and a relative increase in the amount
of monomethoxylated lignin fragments (masses 123, 138,
and 151). There was also a loss of mass 180 and increase
FEMSLE 10382 1-5-02
Brown-rot fungi are characterized by an ability to colonize wood and to utilize wood constituents for their
growth and metabolism. These organisms play a signi¢cant role in the forest ecosystem and can also be a major
factor in the biodegradation of wood in service, limiting
both the application and durability of speci¢c wood products. The brown-rot fungi are able to cause rapid strength
losses in wood early in the degradation process. These
strength losses are associated with the rapid loss of cellulose DP and are postulated to be the result of non-enzymatic processes involving iron, hydrogen peroxide, and
biochelators [4,26]. Initial stages of brown-rotted wood
may show little visible signs but may nonetheless display
extensive chemical modi¢cation. Spectroscopic techniques
including NIR and py-MBMS have been used previously
to monitor the chemical properties of wood and biomass
[10,11,13,17,19].
The results of this study indicate that NIR and MBMS
analyses, in conjunction with multivariate statistical analyses, are useful in di¡erentiating and characterizing wood
subjected to biodegradative fungi. These rapid screening
tools reduce the time for chemical analysis of whole wood
from days to minutes, and can provide both qualitative
and quantitative measures of the chemical di¡erences that
occur during biodegradation of wood. Both techniques
were sensitive to changes in the chemical structure. NIR
shows changes in the extent and type of hydrogen bonds
between the wood components while py-MBMS is particularly sensitive to subtle changes in the structure of lignin.
Information obtained using these techniques can be used
for both decay detection and mechanistic studies of wood
decay. Both techniques are useful for measuring the weight
loss for unknown samples.
Acknowledgements
The authors wish to thank the National Renewable Energy Laboratory Director’s Discretionary Research and
Development for partial support for this work. This
work was supported in part by USDA Grant 97-341585023, USDA Grant 2000-02346, and the Maine Agricultural and Forestry Experimental Station. This is publication 2531 of the Maine Agricultural and Forestry Experimental Station.
References
[1] Srebotnik, E., Jensen, K.A., Kawai, S. and Hammel, K. (1997) Evi-
Cyaan Magenta Geel Zwart
S.S. Kelley et al. / FEMS Microbiology Letters 209 (2002) 107^111
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
dence that Ceriporiopsis subvermispora degrades nonphenolic lignin
structures by a one-electron-oxidation mechanism. Appl. Environ.
Microbiol. 63, 4435^4440.
Umezawa, T. and Higuchi, T. (1987) Mechanism of aromatic ring
cleavage of L-O-4 lignin substructure models by lignin peroxidase.
FEBS Lett. 218, 255^260.
Ander, P., Stoytschev, I. and Eriksson, K.-E. (1988) Cleavage and
metabolism of methoxyl groups from vanillic and ferulic acids by
brown rot and soft-rot fungi. Cell. Chem. Tech. 22, 255^266.
Goodell, B., Jellison, J., Liu, J., Daniel, G., Paszcynski, A., Fekete,
F., Krtishnamurthy, S., Jun, L. and Xu, G. (1997) Low molecular
weight chelators isolated from wood decay fungi and their role in the
fungal degradation of wood. J. Biotech. 53, 133^162.
Paszczynski, A., Crawford, R., Funk, D. and Goodell, B. (1999) De
novo synthesis of 4,5-dimethoxycatechol and 2,5-dimethoxyhydroquinone by the brown rot fungus Gloeophyllum trabeum. Appl. Environ.
Microbiol. 65, 674^679.
Kerem, Z., Jensen, K.A. and Hammel, K.E. (1999) Biodegradative
mechanism of the brown-rot basidiomycete Gloeophyllum trabeum:
evidence for an extracellular hydroquinone-driven fenton reaction.
FEBS Lett. 446, 49^54.
Eriksson, K.-E., Blanchette, R.A. and Ander, P. (1990) Microbial
and Enzymatic Degradation of Wood and Wood Components,
Springer-Verlag Series in Wood Science. Springer-Verlag, Berlin.
Kirk, T.K. (1975) E¡ects of a brown-rot fungus, Lenzites trabea, on
lignin in spruce wood. Holzforschung 29, 99^107.
Marten, G., Shenk, J.S., Barton III, F.E. (1985) Near Infrared Re£ectance Spectroscopy (NIRS) : Analysis of Forage Quality. USDA
Agriculture Research Service, Handbook No. 643.
Schimleck, L.R., Wright, P.J., Mitchell, A.J. and Wallis, A.F.A.
(1997) Near infrared spectra and chemical compositions of E. globules and E. nitens plantation woods. Appita J. 50, 40^46.
Ali, M., Emsley, A.M., Herman, H. and Heywood, R.J. (2001) Spectroscopic studies of the ageing of cellulose paper. Polymer 42, 2893^
2900.
Hiukka, R. (1998) A multivariate approach to the analysis of pine
needle samples using NIR. Chronometrics and Intelligent Laboratory
Systems 44, 395^401.
Schimleck, L.R., Mitchell, A.J., Raymond, C.A. and Muneri, A.
(1998) Assessment of the pulpwood quality of standing trees using
near infrared spectroscopy. Journal of Near Infrared Spectroscopy 6,
A117^A123.
Couteaux, M.M., McTinernan, K.B., Berg, B., Szuberla, D., Dar-
FEMSLE 10382 1-5-02
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
111
denne, P. and Bottner, P. (1998) Chemical composition and carbon
mineralization potential of Scots pine needles at di¡erent stages of
decomposition. Soil Biol. Biochem. 30, 583^595.
McLellan, T.M., Aber, J.D., Martin, M.E., Melillo, J.M. and Nadelho¡er, K.J. (1991) Determination of nitrogen, lignin, and cellulose
content of decomposing leaf material by near infrared re£ectance
spectroscopy. Can. J. For. Res. 21, 1684^1688.
Ho¡meyer, P. and Pedersen, J.G. (1995) Evaluation of density and
strength of Norway spruce wood by near infrared re£ectance spectroscopy. Holz Roh Werkst. 53, 165^170.
Evans, R.J. and Milne, T.A. (1987) Molecular characterization of the
pyrolysis of biomass, 1. Fundamentals. Energy Fuels 1, 123^137.
Agblevor, F.A., Evans, R.J. and Johnson, D.K. (1994) Molecularbeam mass-spectrometric analysis of lignocellulosic materials. I. Herbaceous biomass.. J. Anal. Appl. Pyrolysis 30, 125^144.
Rodrigues, J., Meier, D., Faix, O. and Pereira, H. (1999) Determination of tree to tree variation in syringyl/guaiacyl ratio of Eucalyptus
globulus wood lignin by analytical pyrolysis. J. Anal. Appl. Pyrolysis
48, 121^128.
Schulten, H.R. (1996) Direct pyrolysis-mass spectrometry of soils: A
novel tool in agriculture, ecology forestry and soil science, In: Mass
Spectrometry of Soils (Boutton, T.W. and Yamasaki, S.-H., Eds.),
pp. 373^436. Marcel Dekker Inc. New York.
Magrini, K.A., Evans, R.J., Hoover, C.M., Elam, C.C. and Davis,
M.F. (2001) Use of pyrolysis molecular beam mass spectometry (pyMBMS) to characterize forest soil carbon: method and preliminary
results. Environ. Pollut., in press.
American Society for Testing and Materials (1994) Standard method
of accelerated laboratory test of natural decay resistance of woods.
(D 1413-76). In: 1994 Annual Book of ASTM Standards, Sect. 4,
Vol. 04.10., pp. 218^224. American Society for Testing and Materials,
Philadelphia, PA.
Martens, H. and Naes, T. (1991) Multivariate Calibration. John Wiley and Sons, Chichester.
Wold, S., Ebensen, K. and Geladi, P. (1987) Principal component
analysis. Chemometr. Intelligent Lab. Syst. 2, 37^52.
Fourty, T., Baret, F., Jacquemoud, S., Schmuck, G. and Verdebout,
J. (1996) Leaf optical properties with explicit description of its biochemical composition : direct and inverse problems. Remote Sensing
Environ. 56, 104^117.
Jellison, J. (2001) Brown-rot fungi. In: The Encyclopedia of Plant
Pathology (Maloy, O.C. and Murray, T.D., Eds.), pp. 159^160. John
Wiley and Sons, NY.
Cyaan Magenta Geel Zwart