The Influence of Xanthan and Balangu Seed Gums Coats on the Kinetics of Infrared Drying of Apricot Slices: GA-ANN and ANFIS Modeling

ABSTRACT

Polysaccharide-based edible coating can be useful as a pretreatment for drying since it prevents the oxidation of nutritional compounds, thereby improving the quality of dried products. In this study, the effects of polysaccharide coating (xanthan and balangu seed gums) on the drying kinetics of apricot slices were investigated. In addition, genetic algorithm–artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used for prediction of drying time (DT) and moisture content (MC) of coated apricot slices in an infrared (IR) dryer. The GA-ANN and ANFIS were fed with two inputs of IR radiation intensity (150, 250, and 375 W) and the distance of slices from lamp surface (5, 7.5, and 10 cm) for prediction of average DT. Also, to predict the MC, these models were fed with three inputs of IR power, lamp distance, and treatment time. The developed GA-ANN, which included seven hidden neurons, predicted the DT of apricot slices with a correlation coefficient (r) of 0.970. Also, the GA-ANN model with nine neurons in one hidden layer predicts the MC with a high r-value (r = 0.999). The calculated r-values for prediction of DT and MC using the ANFIS-based subtractive clustering algorithm were 0.986 and 0.999, respectively, which showed a high correlation between predicted and experimental values. Sensitivity analysis results showed that IR intensity and treatment time were the most sensitive factors for prediction of DT and MC of coated apricot slices, respectively. Both GA-ANN and ANFIS models’ predictions agreed well with testing data sets, and they could be useful for understanding and controlling the factors affecting on drying kinetics of apricot slices in an IR dryer.

KEYWORDS:

• Adaptive neuro-fuzzy inference system
• genetic algorithm–artificial neural network
• subtractive clustering