Using microwave-assisted extraction with advanced artificial intelligence models for predicting tannins in black pepper ( Piper nigrum L.)

Black pepper ( Piper nigrum L.) is a widely used spice that provides great potential for research in the field of natural products. In this work, the recovery of tannins from black pepper was conducted using microwave- assisted extraction (MAE). The study involves four independent variables: power (from 100 to 300 W), extraction time (from 10 to 40 minutes), temperature (from 35 to 50 degrees C), and the ratio of food to solvent (from 0.25 to 0.5 g/10 mL). The response variable was the extraction yield, which is the total tannin content. A total of 30 different experimental runs were completed in the MAE system. An evaluation and comparison of two non-verbal modeling approaches and artificial intelligence-based models was conducted. In order to predict design performance and results, the three SVR-RSM, M5Tree, and RM5Tree models were compared to a proposed nonlinear regression model. Evaluations were conducted using health criteria such as RMSE and NSE. With an RMSE of 0.035 and an NSE of 0.91, the SVR-RSM algorithm showed the highest level of accuracy. A RMSE of 0.048 and an NSE of 0.83 is obtained from the RM5tree model, while a RMSE of 0.055 and an NSE of 0.78 is obtained from the M5Tree model. Also, an NSE of 0.65 and a RMSE of 0.068 were obtained for the proposed nonlinear model. The SVR-RSM algorithm had maximum accuracy, but tree models for systems requiring a quick response are the right options. Using the proposed non-error model, complex relationships between variables could also be modeled.