Comparative analysis of RSM and ANN-GA based modeling for protein extraction from cotton seed meal: Effect of extraction parameters on amino acid profile and nutritional characteristics

Abstract

Cottonseed meal (CSM), a residual biomass and valuable by-product, serves as a sustainable protein source, yielding approximately 10 million metric tons globally, enough to meet the annual protein requirements of over half a billion people. In this context, the study aimed to optimize protein extraction from CSM using response surface methodologies (RSM) and artificial neural networks with genetic algorithms (ANN-GA), while also examining its amino nutritional characteristics. The independent variables, pH (8.5–10.5), temperature (25–45 °C), solvent-solid ratio (10–30 mL/g) and time (1–3 h) were designed to optimize the responses protein yield and purity. Various statistical measures were computed to evaluate the errors and coefficients of determination for the projected models. The ANN model shows better results in forecasting protein production and purity, demonstrating superior accuracy and precision. The average mean percentage error (MPE) of the ANN model was lower for protein yield and purity as 0.673 % and 0.182 % compared to RSM 2.56 % and 0.685 % respectively. Under optimal conditions, ANN achieved higher protein yield and purity (28.03 %, 88.69 %) compared to RSM (23.24 %, 87.17 %). The CSM protein isolate contained all essential amino acids with high biological value (70.33) and essential amino acid score (75.26), indicating high-quality protein. This study offers significant insights into effective modeling approaches for protein extraction, highlights utility of ANN-GA in predictive assessments, and underscores the potential of agricultural waste as a cost-effective substrate for high-quality protein supplements in food products.