Artificial neural network (ANNs) and mathematical modelling of hydration of green chickpea

Abstract

The present study was aimed to model the hydration characteristics of green chickpea (GC) using mathematical modelling and examine predictive ability of artificial neural network (ANN) modelling. Hydration of GC was performed at different temperatures 25, 35, 45, 55 and 65 °C. Different mathematical models were tested for the hydration at different temperatures. In ANN modelling, the hydration time and hydration temperature were used as input variables and moisture ratio, moisture content and hydration ratio were taken as output variables. Peleg model best described the hydration behavior at 25 °C; while hydration at high-temperature was better described by Page model and Ibarz et al. model. The optimum temperature obtained for hydration was 35 °C. Effective mass diffusion coefficient (De) increased from 1.55 × 10-11-1.79 × 10-9 m2/s with the increase in the hydration temperature. The low activation energy (39.66 kJ/moL) shows the low-temperature sensitiveness of GC. Low temperature hydration (25 °C) required higher time (>200 min) to achieve the equilibrium moisture content (EMC), however high temperature hydration (35–65 °C) reduced the EMC time (150 min). ANN was used to predict the hydration behavior and K fold cross validation was performed to check the over fitting of ANN model. Results show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and TANSIGMOID. TANSIGMOID was found suitable for moisture ratio (MR) and hydration ratio (HR) prediction, as opposed to PURELIN for moisture content (MC) data. Satisfactory model prediction was obtained when the number of neurons in the hidden layer for MC, MR and HR was 12, 8 and 15, respectively. Mathematical and ANN modelling results are useful to improve/predict the MC, MR and HR during hydration process of GC at different temperature and other similar process.