College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China; CAS Key Laboratory of Bio-based Materials, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, PR China; PCC & Laboratory of Wood and Paper Chemistry, Ǻbo Akademi University, Turku FI-20500, Finland; state Key Laboratory Base for Eco-Chemical Engineering in College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China.
College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China; CAS Key Laboratory of Bio-based Materials, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, PR China; state Key Laboratory Base for Eco-Chemical Engineering in College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China.
Bioresour Technol. 2019 May;279:271-280. doi: 10.1016/j.biortech.2018.12.096. Epub 2019 Jan 9.
In this work, multivariate data analysis was employed to correlate variables of pretreatment process of lignocellulosic biomass. Principal component analysis and partial least square methods were performed to get the inner-relationship and data interpretation between the crystallinity and other parameters of mechanical refining-assisted sodium hydroxide pretreatment followed by enzymatic saccharification of corn stover. The PCA and PLS models showed that Sodium hydroxide dosage, mechanical refining treatment, lignin removal rate and crystallinity had close inner-related relationship with the efficiency of pretreatment and enzymolysis. Alkaline reaction and mechanical refining treatment had strong influence on the crystallinity. Multivariate data analysis revealed that pretreated corn stover samples with lower crystallinity were more easily hydrolyzed by enzyme and could get more final reducing sugar. This work could offer a new methodology to get further understanding of effect of crystallinity on the crop residue lignocellulosic biomass conversion process.
在这项工作中,采用多元数据分析方法对木质纤维素生物质预处理过程的变量进行关联分析。采用主成分分析和偏最小二乘法对机械研磨辅助氢氧化钠预处理后结晶度与玉米秸秆酶解的其他参数之间的内在关系和数据解释进行了分析。PCA 和 PLS 模型表明,氢氧化钠用量、机械研磨处理、木质素去除率和结晶度与预处理和酶解效率密切相关。碱性反应和机械研磨处理对结晶度有很强的影响。多元数据分析表明,结晶度较低的预处理玉米秸秆样品更容易被酶水解,并且可以得到更多的最终还原糖。这项工作为进一步了解结晶度对农作物残余木质纤维素生物质转化过程的影响提供了一种新的方法。