Fecha de publicación: -- Fuente: PubMed "industrial biotechnology" Bioresour Technol. 2026 Nov 9:136213. doi: 10.1016/j.biortech.2026.136213. Online ahead of print.ABSTRACTEfficient glucose production from sugarcane bagasse requires pretreatment that balances cellulose retention with hemicellulose and lignin removal, followed by effective enzymatic hydrolysis of the recovered solid. Here, an integrated experimental-design, machine-learning, and density functional theory framework was developed for p-toluenesulfonic acid (TsOH)/glycerol pretreatment followed by enzymatic saccharification. Fractional factorial and central composite designs defined the experimental domain, and four regression algorithms were evaluated for predicting cellulose retention, hemicellulose removal, delignification, and glucose yield. Random Forest Regression provided balanced test-set correlation coefficients of 0.73-0.95 and guided the selection of conditions for experimental validation. Across separately validated pretreatment and hydrolysis conditions, cellulose retention reached 85.1-89.7%, hemicellulose removal 80.1-86.2%, delignification 80.3-87.1%, and glucose yield 80.6-92.1%. Model interpretation associated hemicellulose removal and delignification primarily with acidity, whereas glucose-yield predictions were influenced mainly by substrate loading, enzyme loading, and hydrolysis time. Molecular calculations indicated hydrogen-bonding and other noncovalent interactions between the TsOH/glycerol medium and representative lignin- and lignin-carbohydrate-complex motifs, providing a plausible molecular context for the observed fractionation behavior. This experimentally validated framework supports data-guided conversion of sugarcane bagasse into fermentable sugars.PMID:43003318 | DOI:10.1016/j.biortech.2026.136213