Comparison of ANN and ANFIS modeling for predicting drying kinetics of Stevia rebaudiana leaves in a hot-air dryer and characterization of dried powder

Abstract

ABSTRACT In this investigation, the drying of Stevia rebaudiana leaves was carried out in a lab scale convective hot-air dryer at a varying temperature of 30–80°C to analyze the drying behavior, fit mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy System (ANFIS) models to predict the drying kinetics of leaves. Further, dried leaf powders were analyzed for color properties, ascorbic acid and total phenol contents, antioxidant activity, water activity (aw), water solubility index (WSI), hygroscopicity (HG), density (bulk, tapped, and particle), bulk porosity, and flowability indices (Hausner ratio (HR), Carr index (CI), and angle of repose (α)). The results showed that ANFIS model with R2 of 0.9998, offers a more accurate forecast of the drying kinetics of leaves dried in a convective hot-air dryer in comparison to mathematical and ANN modeling. The convective drying significantly (p < .05) effected the L*, a*, b*, hue angle and chroma values of dried leaves. Increase in the drying temperature from 30 to 80°C resulted in a decrement of 50.90% in aw, 10.10% in tapped density, while enhancement of 23.26% in WSI, 32.93% in HG, 54% in particle density, and 10.59% in bulk porosity of dried leaf powder. Notably, ascorbic acid and antioxidant activity decreased with rising temperatures, while total phenols enhanced up to 50°C. The bulk density of dried samples remained largely unchanged with increasing temperature, while the flowability of the Stevia powder improved. Thus, these findings provide valuable insights for producers regarding the drying characteristics and properties of Stevia leaf powder.