Dataset Information

High-throughput microanalysis of large lignocellulosic sample sets by pyrolysis-gas chromatography/mass spectrometry.

ABSTRACT: High-throughput analytical techniques to assess the chemistry of lignocellulosic plant material are crucial to plant cell-wall research. We have established an analytical platform for this purpose and demonstrated its usefulness with two applications. The system is based on analytical pyrolysis, coupled to gas chromatography/mass spectrometry - a technique particularly suited for analysis of lignocellulose. Automated multivariate-based data-processing methods are used to obtain results within a few hours after analysis, with an experimental batch of 500 analyzed samples. The usefulness of multivariate sample discrimination methods and hierarchical clustering of samples is demonstrated. We have analyzed an Arabidopsis mutant collection consisting of 300 samples representing 31 genotypes. The mutant collection is presented through cluster analysis, based on chemotypic difference, with respect to wild type. Further, we have analyzed 500 thin sections from five biological replicate trees to create a spatial highly resolved profile of the proportions of syringyl-, guaiacyl- and p-hydroxyphenyl lignin across phloem, developing and mature wood in aspen. The combination of biologically easy to interpret information, the low demand of sample amount and the flexibility in sample types amenable to analysis makes this technique a valuable extension to the range of established high-throughput biomaterial analytical platforms.

SUBMITTER: Gerber L

PROVIDER: S-EPMC4738464 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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High-throughput microanalysis of large lignocellulosic sample sets by pyrolysis-gas chromatography/mass spectrometry.

Gerber Lorenz L Öhman David D Kumar Manoj M Ranocha Philippe P Goffner Deborah D Sundberg Björn B

Physiologia plantarum 20151126 2

High-throughput analytical techniques to assess the chemistry of lignocellulosic plant material are crucial to plant cell-wall research. We have established an analytical platform for this purpose and demonstrated its usefulness with two applications. The system is based on analytical pyrolysis, coupled to gas chromatography/mass spectrometry - a technique particularly suited for analysis of lignocellulose. Automated multivariate-based data-processing methods are used to obtain results within a ...[more]

PMID: 26477543

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Project description:High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

Dataset Information

High-throughput microanalysis of large lignocellulosic sample sets by pyrolysis-gas chromatography/mass spectrometry.

Publications

High-throughput microanalysis of large lignocellulosic sample sets by pyrolysis-gas chromatography/mass spectrometry.

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