Use of NIR and pyrolysis-MBMS coupled with multivariate analysis for detecting the chemical changes associated with brown-rot biodegradation of spruce wood

Chemical Characterization and Visualization of Progressive Brown Rot Decay of Wood by Near Infrared Imaging and Multivariate Analysis

Brown rot fungi cause a type of wood decay characterized by carbohydrate degradation and lignin modification. The chemical and physical changes caused by brown rot are usually studied using bulk analytical methods, but these methods fail to consider local variations within the wood material. In this study we applied hyperspectral near infrared imaging to Scots pine sapwood samples exposed to the brown rot fungi Coniophora puteana and Rhodonia placenta to obtain position-resolved chemical information on the fungal degradative process. A stacked-sample decay test was used to create a succession of decay stages within the samples. The results showed that the key chemical changes associated with decay were the degradation of amorphous and crystalline carbohydrates and an increase in aromatic and carbonyl functionality in lignin. The position-resolved spectral data revealed that the fungi initiated degradation in earlywood, and that earlywood remained more extensively degraded than latewood even in advanced decay stages. Apart from differences in mass losses, the two fungi produced similar spectral changes in a similar spatial pattern. The results show that near infrared imaging is a useful tool for analyzing brown rot decayed wood and may be used to advance our understanding of fungal degradative processes.

Identification and control of wood-deteriorating fungi

ABSTRACT: Wood is rich in organic compounds; thus, it is susceptible to attacks by several deteriorating agents because they acknowledge such attacks as a necessary energy supply. Fungi stand out among these agents since they can attack the wood in trees (before felling) and in several post-felling stages. Fungi are biological agents that use organic compounds as food sources, and that is the reason why they degrade most chemical components found in wood. Wood attacked by fungi suffers significant value loss, since these degrading agents affect their mechanical and aesthetic properties. Fungi — which are classified as staining, molding and rotting — are one of the main responsible for the biggest losses in the timber industry. Wood can be virtually used in a whole range of environments, a fact that makes it susceptible to attacks by different fungal species, since the ideal conditions for fungal development change from species to species. In other words, the fact that one or more fungal species are capable of deteriorating wood is directly related to conditions wood is subjected to. Thus, it is essential knowing how to evaluate fungal attacks, symptoms to be taken into consideration at the time to identify the type of attacking organism and the attack stage, as well as the control and prevention measures to be applied to these organisms.

NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review

Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review aims to serve as a guide for choosing the most effective data analysis methods for NIR and Py-mbms characterization of biomass.

Using a grass substrate to compare decay among two clades of brown rot fungi

Interest in the mechanisms of wood-degrading fungi has grown in tandem with lignocellulose bioconversion efforts, yet many potential biomass feedstocks are non-woody. Using corn stover (Zea mays) as a substrate, we tracked degradative capacities among brown rot fungi from the Antrodia clade, including Postia placenta, the first brown rot fungus to have its genome sequenced. Decay dynamics were compared against Gloeophyllum trabeum from the Gloeophyllum clade. Weight loss induced by P. placenta (6.2 %) and five other Antrodia clade isolates (average 7.4 %) on corn stalk after 12 weeks demonstrated inefficiency among these fungi, relative to decay induced by G. trabeum (44.4 %). Using aspen (Populus sp.) as a woody substrate resulted in, on average, a fourfold increase in weight loss induced by Antrodia clade fungi, while G. trabeum results matched those on stover. The sequence and trajectories of chemical constituent losses differed as a function of substrate but not fungal clade. Instead, chemical data suggest that characters unique to stover limit decay by the Antrodia clade, rather than disparities in growth rate or extractives toxicity. High p-coumaryl lignin content, lacking the methoxy groups characteristically cleaved during brown rot, is among potential rate-distinguishing characters in grasses. This ineptitude among Antrodia clade fungi on grasses was supported by meta-analysis of other unrelated studies using grass substrates. Concerning application, results expose a problem if adopting the strategy of the model decay fungus P. placenta to treat corn stover, a widely available plant feedstock. Overall, the results insinuate phylogenetically distinct modes of brown rot and demonstrate the benefit of using non-woody substrates to probe wood degradation mechanisms.

How spectroscopy and microspectroscopy of degraded wood contribute to understand fungal wood decay

Nuclear magnetic resonance, mid and near infrared, and ultra violet (UV) spectra of wood contain information on its chemistry and composition. When solid wood samples are analysed, information on the molecular structure of the lignocellulose complex of wood e.g. crystallinity of polysaccharides and the orientation of the polymers in wood cell walls can also be gained. UV and infrared spectroscopy allow also for spatially resolved spectroscopy, and state-of-the-art mapping and imaging systems have been able to provide local information on wood chemistry and structure at the level of wood cells (with IR) or cell wall layers (with UV). During the last decades, these methods have also proven useful to follow alterations of the composition, chemistry and physics of the substrate wood after fungi had grown on it as well as changes of the interactions between the wood polymers within the lignocellulose complex caused by decay fungi. This review provides an overview on how molecular spectroscopic methods could contribute to understand these degradation processes and were able to characterise and localise fungal wood decay in its various stages starting from the incipient and early ones even if the major share of research focussed on advanced decay. Practical issues such as requirements in terms of sample preparation and sample form and present examples of optimised data analysis will also be addressed to be able to detect and characterise the generally highly variable microbial degradation processes within their highly variable substrate wood.

High-throughput screening of plant cell-wall composition using pyrolysis molecular beam mass spectroscopy

We describe a high-throughput method for estimating cell-wall chemistry traits using analytical pyrolysis. The instrument used to perform the high-throughput cell-wall chemistry analysis consists of a commercially available pyrolysis unit and autosampler coupled to a custom-built molecular beam mass spectrometer. The system is capable of analyzing approximately 42 biomass samples per hour. Lignin content and syringyl to guaiacol (S/G) ratios can be estimated directly from the spectra and differences in cell wall chemistry in large groups of samples can easily be identified using multivariate statistical data analysis methods. The utility of the system is demonstrated on a set of 800 greenhouse-grown poplar trees grown under two contrasting nitrogen treatments. High-throughput analytical pyrolysis was able to determine that the lignin content varied between 13 and 28% and the S/G ratio ranged from 0.5 to 1.5. There was more cell-wall chemistry variation in the plants grown under high nitrogen conditions than trees grown under nitrogen-deficiency conditions. Analytical pyrolysis allows the user to rapidly screen large numbers of samples at low cost, using very little sample material while producing reliable and reproducible results.

Enhanced discrimination and calibration of biomass NIR spectral data using non-linear kernel methods

Rapid methods for the characterization of biomass for energy purpose utilization are fundamental. In this work, near infrared spectroscopy is used to measure ash and char content of various types of biomass. Very strong models were developed, independently of the type of biomass, to predict ash and char content by near infrared spectroscopy and multivariate analysis. Several statistical approaches such as principal component analysis (PCA), orthogonal signal correction (OSC) treated PCA and partial least squares (PLS), Kernel PCA and PLS were tested in order to find the best method to deal with near infrared data to classify and predict these biomass characteristics. The model with the highest coefficient of correlation and the lowest RMSEP was obtained with OSC-treated Kernel PLS method.

Degradation of eucalypt waste components by Lentinula edodes strains detected by chemical and near-infrared spectroscopy methods

There are many changes, both qualitative and quantitative, in eucalypt waste during growth and fructification of Lentinula edodes. Wet chemical analysis and near-infrared (NIR) spectroscopy were used in conjunction with multivariate regression and principal components analysis to monitor biodegradation of eucalyptus waste during growth of several L. edodes strains. Weight and component losses of eucalypt residue after biodegradation by L. edodes strains were compared for periods of 1 to 5 mo. Decrease in cellulose, hemicellulose, and lignin contents occurred, however it was not concomitant. Measurement of lignin degradation by NIR and wet chemical analysis indicated its attack in the early stages of biodegradation. Selective lignin degradation by L. edodes was observed up to 2 mo of biodegradation for strains DEBIQ and FEB-14. One group of degraded substrate was identified based on the principal component analysis (PCA) of the data on their biodegradation time. Samples treated for 5 months by L. edodes strains (DEBIQ, UFV or FEB-14) differed from other, but no discrimination was observed among them. By the end of 5 mo, NIR analyses showed decrease of about 18-47% cellulose, 35-47% polyose and 39-60% lignin. These data were used for comparison with those obtained by wet chemical method for the degradation of the substrate by other five L. edodes strains cultivated at the same conditions. NIR calibration developed in this study was proven to be perfectly suitable as an analytical method to predict the changes in lignocellulose composition during biodegradation.

Poplar genome sequence: functional genomics in an ecologically dominant plant species

In addition to their value for wood products, members of the genus Populus (poplars) provide a range of ecological services, including carbon sequestration, bioremediation, nutrient cycling, biofiltration and diverse habitats. They are also widely used model organisms for tree molecular biology and biotechnology. The sequencing of the poplar genome to an approximately 6x depth adds to a long list of important attributes for research. These include facile transformation, vegetative propagation, rapid growth, modest genome size and extensive expressed sequence tags. Here, we discuss how the genome sequence and transformability of poplar, together with its high levels of genetic and ecological diversity, are enabling new insights into the genetic programs controlling ontogeny, ecological adaptation and environmental physiology of trees.