Abstract
This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10−9 m2/s and 1.925 × 10−8 m2/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R2 values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R2 and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.
Highlights
Persimmon (Diospyros kaki) is an edible fruit with a sweet taste and rich in vitamin A, C, calcium, condensed tannins, carotenoid, phenolic compounds and iron [1,2,3]
The activation energy (Ea) of persimmon fruit samples was calculated from the values of effective moisture diffusivity, Deff
For vacuum drying (VD), the training data set at standardize filter with Pearson universal kernel type found the best result of R2 and root mean square error (RMSE) values of 1.0000 and 0.0004 as compared to those of normalize with polynomial, Pearson universal kernel and Radial Basis Function (RBF) kernel and standardize with polynomial and RBF kernel, respectively
Summary
Persimmon (Diospyros kaki) is an edible fruit with a sweet taste and rich in vitamin A, C, calcium, condensed tannins, carotenoid, phenolic compounds and iron [1,2,3]. Persimmon has high moisture content resulting in susceptibility to spoilage even at refrigerator temperatures. It has to be preserved by proper drying processes to increase the shelf life [5,6]. Drying of agricultural products causes the enzymatic reactions to be inactivated as a result of heat and mass transfer leading to a reduction of the moisture content inside the product [7]. It helps the extraction of bioactive compounds from food products. Drying methods such as hot-air drying (HAD), freeze-drying (FD), vacuum drying (VD), microwave drying (MWD) and infrared drying (IRD) have been used in drying agricultural crops [7,8,9,10]
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