Application of hybrid ANFIS-based non-linear regression modeling to predict the %oil yield from grape peels: Effect of process parameters and FIS generation techniques

Abstract

The present study evaluates the extraction process parameters of grape peel oil using non-linear hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The experiments were conducted at temperature (80–100 °C), and time (5–9 h) at 5 levels with output parameter as oil yield. The experimental result and factor interaction analysis shows that temperature had the most significant effect on the oil yield. The oil yield estimation performance indicators for the Fuzzy Inference (FIS) generation techniques are: Fuzzy C-means Clustering (FCM), [R 2 = 0.9102, MSE = 0.278, RMSE = 0.527], Subtractive Clustering (SC), [R 2 = 0.9090, MSE = 0.300, RMSE = 0.548] and Grid Partitioning (GP), [R 2 = 0.827, MSE = 0.668, RMSE = 0.817]. This indicates that Fuzzy C-means FIS generation fits better than Subtractive Clustering and Grid Partitioning techniques. For future research, a substantial volume of data is recommended for successful ANFIS training. • Oil yield extraction from grape peels using Soxhlet method. • Prediction of oil yield using hybrid ANFIS non-linear regression model. • Analysis of the effect of process parameters on oil yield. • FIS generating strategies utilized in ANFIS modeling are compared.