Sustainable valorization of Pleurotus pulmonarius via green dual-enzyme technology: Novel optimization for functional protein hydrolysates
Year: 2025
Type: article
Abstract: This research established a sustainable dual-enzyme hydrolysis method for generating nutrient-dense hydrolysates from grey oyster mushrooms (Pleurotus pulmonarius), a native resource of Vietnam. Process optimization integrated a quadratic regression model using response surface methodology (RSM) and an artificial neural network combined with a genetic algorithm (ANN–GA). The objective was to enhance nitrogen recovery, amino acids, peptides, glutamic acid, and lysine, while minimizing NH 3 formation. ANN–GA demonstrated superior predictive accuracy compared to RSM, with RMSE values of 1–2 % versus 2–5 %. In contrast, RSM offered precise quantitative insights regarding the effects of factors. The optimal conditions (50.9 °C, 6.45 h, pH 6.12) resulted in a nitrogen recovery of 27.23 %, with 6.926 g/100 g of amino acids, 11.41 g/100 g of peptides, 6.398 g/100 g of glutamic acid, 0.0845 g/100 g of lysine, and 0.0089 g/100 g of NH 3 . Levels of essential amino acids were comparable to or higher than those documented in previous studies. The sustainability of the method not only reduces waste and benefits farmers but also provides a practical route to producing mushroom-based functional food and seasoning ingredients due to their high amino acid and peptide content. • First ANN–GA and RSM optimization of dual-enzyme hydrolysis for P. sajor-caju . • Maximized peptides and essential amino acids with reduced ammonia content. • ANN–GA showed higher prediction accuracy than RSM in process optimization. • Green processing of Vietnamese mushrooms adds value for local farmers. • Hydrolysate suitable for functional foods and seasoning applications
Source: Food Chemistry X
Authors: Nguyen Thi Ngoc Giang, Tran Van Khai
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,330
Award: G7208601756
OpenAlex ID: W4416757446
