Optimization of organosolv pretreatment of rice straw for enhanced biohydrogen production using Enterobacter aerogenes

Biochemical conversion of lignocellulosic feedstocks to advanced biofuels and other commodities through a sugar-platform process involves a pretreatment step enhancing the susceptibility of the cellulose to enzymatic hydrolysis. A side effect of pretreatment is formation of lignocellulose-derived by-products that inhibit microbial and enzymatic biocatalysts. This review provides an overview of the formation of inhibitory by-products from lignocellulosic feedstocks as a consequence of using different pretreatment methods and feedstocks as well as an overview of different strategies used to alleviate problems with inhibitors. As technologies for biorefining of lignocellulose become mature and are transferred from laboratory environments to industrial contexts, the importance of management of inhibition problems is envisaged to increase as issues that become increasingly relevant will include the possibility to use recalcitrant feedstocks, obtaining high product yields and high productivity, minimizing the charges of enzymes and microorganisms, and using high solids loadings to obtain high product titers.

Twenty-one organosolv ethanol lignin samples were prepared from hybrid poplar (Populus nigra xP. maximowiczii) under varied conditions with an experimental matrix designed using response surface methodology (RSM). The lignin preparations were evaluated as potential antioxidants. Results indicated that the lignins with more phenolic hydroxyl groups, less aliphatic hydroxyl groups, low molecular weight, and narrow polydispersity showed high antioxidant activity. Processing conditions affected the functional groups and molecular weight of the extracted organosolv ethanol lignins, and consequently influenced the antioxidant activity of the lignins. In general, the lignins prepared at elevated temperature, longer reaction time, increased catalyst, and diluted ethanol showed high antioxidant activity. Regression models were developed to enable the quantitative prediction of lignin characteristics and antioxidant activity based on the processing conditions.