Literature DB >> 30825829

Modeling growth and fermentation inhibition during bioethanol production using component profiles obtained by performing comprehensive targeted and non-targeted analyses.

Kazuki Watanabe1, Seiga Tachibana2, Masaaki Konishi3.   

Abstract

Corn cob and corn stover hydrolysates are forms of lignocellulosic biomass that can be used in second generation bioethanol production and biorefinery processes. Growth and fermentation inhibitors generated during physicochemical and enzymatic hydrolysis decrease ethanol and biomaterial production during the subsequent biological processes. Here, estimates of growth and fermentation inhibition during bioethanol fermentation were made using component profiles of corn cobs and corn stover at different degrees of hydrolysis. The component profiles were acquired by non-targeted gas chromatography mass spectrometry and targeted high-performance liquid chromatography. Correlations between the comprehensive analysis results and yeast growth and ethanol production were modeled very accurately by partial-least-squares regression analysis. Acetate, apocynin, butyrovanillone, furfural, furyl hydroxymethyl ketone, m-methoxyacetophenone, palmitic acid, syringaldehyde, and xylose, were compounds with very variable importance in projection values and had negative correlation coefficients in the model. In fact, methoxyacetophenone, apocynin, and syringaldehyde inhibited fermentation more than furfural in equivalent concentration.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Corn stover; Corncob; Ethanol; Lignocellulose; Partial least square regression (PLS-R)

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Year:  2019        PMID: 30825829     DOI: 10.1016/j.biortech.2019.02.081

Source DB:  PubMed          Journal:  Bioresour Technol        ISSN: 0960-8524            Impact factor:   9.642


  2 in total

1.  Metabolic and Evolutionary Engineering of Diploid Yeast for the Production of First- and Second-Generation Ethanol.

Authors:  Yang Sun; Meilin Kong; Xiaowei Li; Qi Li; Qian Xue; Junyan Hou; Zefang Jia; Zhipeng Lei; Wei Xiao; Shuobo Shi; Limin Cao
Journal:  Front Bioeng Biotechnol       Date:  2022-01-28

2.  Machine learning modeling of the effects of media formulated with various yeast extracts on heterologous protein production in Escherichia coli.

Authors:  Seiga Tachibana; Tai-Ying Chiou; Masaaki Konishi
Journal:  Microbiologyopen       Date:  2021-06       Impact factor: 3.139

  2 in total

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