The correlation between the physicochemical properties and enzymatic hydrolyzability of hydrothermal pretreated wheat straw: A quantitative analysis. (September 2022)

• Record Type: Journal Article
• Authors: Deng, Zhichao; Xia, Ao; Huang, Yun; Zhu, Xianqing; Zhu, Xun; Liao, Qiang
• Is Part Of: Bioresource technology. Volume 359(2022)
• Publication Date: 2022-09
• Subjects: Lignocellulosic biomass -- Hydrothermal pretreatment -- Enzymatic hydrolysis -- Correlation analysis -- Principal component analysis; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.127475
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: The effect of hydrothermal pretreatments on enzymatic hydrolysis was studied. The highest carbohydrate conversion of pretreated wheat straw was 96.1% Cellulose content shows a markedly positive correlation to carbohydrate conversion. The physicochemical properties were successfully converted into two new variables. The enzymatic hydrolyzability can be well predicted by PC1 and PC2. Abstract: Hydrothermal pretreatment with diluted acid or alkali can disrupt the compact structure of wheat straw at a moderate temperature for efficient enzymatic saccharification. However, the quantitative analysis between the physicochemical properties and enzymatic hydrolyzability of hydrothermal pretreated lignocellulose was rarely investigated, which hindered the development of model-based applications for process design and control. Herein, correlation analysis (CA) and principal component analysis (PCA) were conducted to elucidate the dominant factors affecting the enzymatic hydrolyzability and quantitative relationship between them. CA results suggested the major positive factor affecting carbohydrate conversion was cellulose content (r = 0.86). Through logarithmic processing and linear combination, these intercorrelated factors were successfully converted into two newly uncorrelated variables named the first principal component (PC1) and the second principal component (PC2). The initial hydrolysis rate and carbohydrate conversion can be well predicted by PC1 and PC2 scores through multiple linear regression with a high R-squared (0.91 and 0.80).