Biorefining of lignocellulosic biomass has become one of the most valuable alternatives for the production of multi-products such as biofuels. Conversion to monosaccharides and then further to liquid fuel is usually achieved in a three-step process consisting of 1) pretreatment of the lignocellulosic feedstock; 2) cellulase-catalyzed saccharification and 3) microbial fermentation of the liberated monosaccharides. Pretreatment constitutes a critical step to increase the conversion of the highly hydrolysis-resistant lignocellulosic matrix.<br/>In the present work, three distinct biomasses (poplar, miscanthus and wheat straw) were selected for bioethanol production using industrial scale steam explosion pretreatment. Various reactive conditions were carried out by modulating both reactant concentration and reactor temperature/pressure. Detailed structural and chemical overview of each of the 30 pretreated biomass samples was achieved then correlated with their enzymatic saccharification potential.<br/>The aim was to gain more knowledge in the key parameters affecting the recalcitrance and therefore to predict the enzymatic digestibility of pretreated samples thanks to a subsequent modelling approach. We thus assembled a platform of experimental techniques (chemical analyses, 2D NMR, FTIR, fluorescence spectroscopy and microscopy, SEM, mercury porosimetry) to determine architecture, enzyme accessibility and interactions of plant cell wall polymers. In particular, fluorescence mapping is a straightforward, non-invasive and powerful tool to observe the impact of pretreatment on biomass cell wall structure and composition.<br/>By bringing the results altogether, we demonstrate that an increase of the steam explosion severity is directly correlated to a higher removal of hemicelluloses and an increase of the pore size and overall surface area, leading to better enzyme accessibility. The fluorescence mapping combined with quantitative NMR and FTIR reveals the formation of condensed lignin after pretreatment characterized by the significant decrease of the β-O-4 linkages content. We also show shifts in maximum of excitation and emission fluorescence. Based on the analysis of these 30 samples, the dataset obtained was introduced in a mathematical model developed to prioritize the biomass features responsible for enzymatic digestibility and finally to predict with accuracy the enzymatic reactivity of pretreated biomass samples.

Multimodal characterization of industrial steam exploded biomass samples: A modelling approach to predict enzymatic digestibility

251st National Meeting (2016)

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