Dataset Information

Exploration of the Interrelationship within Biomass Pyrolysis Liquid Composition Based on Multivariate Analysis.

ABSTRACT: The diverse utilization of pyrolysis liquid is closely related to its chemical compositions. Several factors affect PA compositions during the preparation. In this study, multivariate statistical analysis was conducted to assess PA compositions data obtained from published paper and experimental data. Results showed the chemical constituents were not significantly different in different feedstock materials. Acids and phenolics contents were 31.96% (CI: 25.30-38.62) and 26.50% (CI: 21.43-31.57), respectively, accounting for 58.46% (CI: 46.72-70.19) of the total relative contents. When pyrolysis temperatures range increased to above 350 °C, acids and ketones contents decreased by more than 5.2-fold and 1.53-fold, respectively, whereas phenolics content increased by more than 2.1-fold, and acetic acid content was the highest, reaching 34.16% (CI: 25.55-42.78). Correlation analysis demonstrated a significantly negative correlation between acids and phenolics (r² = -0.43, p < 0.001) and significantly positive correlation between ketones and alcohols (r² = 0.26, p < 0.05). The pyrolysis temperatures had a negative linear relationship with acids (slope = -0.07, r² = 0.16, p < 0.001) and aldehydes (slope = -0.02, r² = 0.09, p < 0.05) and positive linear relationship with phenolics (slope = 0.04, r² = 0.07, p < 0.05). This study provides a theoretical reference of PA application.

SUBMITTER: Guo G

PROVIDER: S-EPMC9457913 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploration of the Interrelationship within Biomass Pyrolysis Liquid Composition Based on Multivariate Analysis.

Guo Genmao G Huang Qing Q Jin Fangming F Lin Linyi L Wang Qingqing Q Fu Qionglin Q Liu Yin Y Sajjad Muhammad M Wang Junfeng J Liao Zhenni Z Cai Miao M

Molecules (Basel, Switzerland) 20220902 17

The diverse utilization of pyrolysis liquid is closely related to its chemical compositions. Several factors affect PA compositions during the preparation. In this study, multivariate statistical analysis was conducted to assess PA compositions data obtained from published paper and experimental data. Results showed the chemical constituents were not significantly different in different feedstock materials. Acids and phenolics contents were 31.96% (CI: 25.30−38.62) and 26.50% (CI: 21.43−31.57), ...[more]

PMID: 36080423

Similar Datasets

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

Exploration of the Interrelationship within Biomass Pyrolysis Liquid Composition Based on Multivariate Analysis.

Publications

Exploration of the Interrelationship within Biomass Pyrolysis Liquid Composition Based on Multivariate Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets