Modeling and optimization of protein extraction from sunflower seed Cake: RSM and ANN-GA approaches - Pooja Vartiya; Piyush Kashyap; Bhagya Raj; Maanas Sharma; Mohd Adnan; Syed Amir Ashraf; Rahul Mehra (Journal of Agriculture and Food Research, 2025)
Sunflower seed cake (SSC) is an agricultural waste that is rich in proteins and numerous biologically active compounds. Moreover, SSC is devoid of any anti-nutritional compounds. Therefore, it can be leveraged for many food applications. In this work, SSC was explored for the extraction of proteins. Preliminary experiments on defatted and de-phenolized SSC were carried out using one-factor analysis. Protein extraction from sunflower seed cake was performed following isoelectric precipitation, and the amino acid profile was analyzed. Response surface methodology (RSM) and artificial neural networks (ANNs) were used to optimize the extraction conditions, i.e., pH (8.5–10.5), temperature (25–45 °C), solvent-solid ratio (10–30 mL/g) and time (1–3 h). Under optimized conditions, the protein yield and protein content were 24.24 % (RSM), 28.03 % (ANN-GA), 87.17 % (RSM), and 88.69 % (ANN-GA). Compared to RSM, the ANN model demonstrated a notably more significant coefficient of determination in the overall output values (RSM (0.969 (protein yield), 0.989 (protein content) and ANN-GA (0.996 (protein yield), 0.996 (protein content)), showing superior performance during validation. The SSC protein isolates contained considerable quantities of all the essential amino acids except lysine. Its good nutritional index (61.87) and high crucial amino acid score (70.98) make it a high-quality protein. According to FAO standards, the amino acid score indicates the abundance of all essential amino acids, making it a helpful protein supplement for meeting dietary requirements. Furthermore, this research highlights the potential to utilize industrial byproducts as economical substrates for protein isolation efficiently.