Results for “suberin” — Page 2

DOE OSTI2023

Benzene polycarboxylic acids, lignin and hydrolysable lipids data from the whole-soil warming experiment at Blodgett Forest, CA, 2018, from: "Rapid loss of complex polymers and pyrogenic carbon in subsoils under whole soil warming"

This data package contains benzene polycarboxylic acids, lignin and hydrolysable lipids data used for the paper: “Rapid loss of subsoil plant-derived polymers and pyrogenic carbon with whole-soil warming”.On April 2018 (after 4.5 years of warming), we collected soil cores down to 90 cm depth from the whole soil profile warming experiment at the University of California Blodgett Forest Research Station, in the Sierra Nevada foothills near Georgetown, CA at 1370 m above sea level. Cores were collected from 6 experimental plots (three replicated blocks); samples were collected in 10 cm increments from 0 to 90 cm depth. This data package includes a file in .csv format and a file in .xlsx format, which can be accessed and processed using MS Excel or R. Benzene polycarboxylic acid, lignin and hydrolysable lipid concentrations are provided as processed data files.The aim of the measurements was to assess how +4°C of whole-soil warming for 4.5 years will affect structurally complex polymeric compounds at different soil depths. The research was supported by Swiss National Science Foundation (SNF) project 172744 (DEEP C) and U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract number DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

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.

59 BASIC BIOLOGICAL SCIENCES↗