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Data set of green extraction of valuable chemicals from lignocellulosic biomass using microwave method.

ABSTRACT: Lignocellulosic biomass is a promising alternative for the replacement of limited fossil resources to produce various chemical compounds, such as 5-hydroxymethylfurfural, furfural, vanillin, vanillic acid, ferulic acid, syringaldehyde, and 4-aminobenzoic acid. However, the complex biomass structure is a limitation to making effective use of this naturally found feedstock. This research presents a data set of different compounds obtained directly from forest residues, with special emphasis on achieving effective utilization of the biomass. The extraction method and the catalyst are considered as the two main factors in this valorization process.

SUBMITTER: Osorio-Gonzalez CS

PROVIDER: S-EPMC6731349 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Data set of green extraction of valuable chemicals from lignocellulosic biomass using microwave method.

Osorio-González Carlos S CS Hegde Krishnamoorthy K Brar Satinder K SK Kermanshahipour Azadeh A Avalos-Ramírez Antonio A

Data in brief 20190822

Lignocellulosic biomass is a promising alternative for the replacement of limited fossil resources to produce various chemical compounds, such as 5-hydroxymethylfurfural, furfural, vanillin, vanillic acid, ferulic acid, syringaldehyde, and 4-aminobenzoic acid. However, the complex biomass structure is a limitation to making effective use of this naturally found feedstock. This research presents a data set of different compounds obtained directly from forest residues, with special emphasis on ach ...[more]

PMID: 31516937

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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

Data set of green extraction of valuable chemicals from lignocellulosic biomass using microwave method.

Publications

Data set of green extraction of valuable chemicals from lignocellulosic biomass using microwave method.

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