Optimization of the silica-gel adsorption technique for the extraction of phytosterol glycosides from soybean lecithin powder using response surface methodology and artificial neural network models

摘要

Phytosterol glycosides (PGs), comprising both acylated steryl glycosides (ASGs) and steryl glycosides (SGs), are active ingredients with benefits for human use. Here, we aimed to optimize the silica-gel adsorption technique for the extraction of PGs from soybean lecithin powder, which contains 5 to 10% of these glycolipids. Both response surface methodology (RSM) and artificial neural networks (ANNs) were applied to optimize the PG extraction parameters (X_1 = silica-gel dosage, X_2 = adsorption temperature, and X_3 = lecithin concentration) for high-purity phospholipid and PG production, and their prediction and optimization accuracies were compared. Although both models fitted well with the experimental data, the ANN model demonstrated better accuracy for predicting and optimizing the conditions using four interrelated dependent variables (Y_1 = phospholipid yield, Y_2 = ASG recovery, Y_3 = SG recovery, and Y_4 = PG purity) and had a higher coefficient of determination and lower root mean square error and absolute average deviation. After digitally setting the percentages of the four dependent variables for phospholipid and PG production, the ANN-optimized phospholipid product (Y_1 = 88.07%, Y_2 = 98.89%, Y_3 = 100%, and Y_4 = 49.03%) was acquired at X_1 = 3.54 g/g, X_2 = 26 °C, and X_3 = 43 mg/mL, whereas the PG product (Y_1 = 83.83%, Y_2 = 97.64%, Y_3 = 100%, and Y_4 = 59.21%) was obtained at X_1 = 2.00 g/g, X_2 = 28.38 °C, and X_3 = 41 mg/mL. In conclusion, the ANN method was better than RSM for the optimization of the silica-gel adsorption technique for PG extraction from soybean lecithin powder.