Mathematical Modelling of the Drying Characteristics of Indian Spinach Using Machine Learning Approach

Abstract

This study employs machine learning techniques to investigate the drying characteristics of Indian spinach leaves. Drying experiments were conducted at varying air temperatures (50°C, 60°C, 70°C) and velocities (1.2m/s and 1.4m/s) while monitoring drying time, moisture content, and important parameters. Artificial neural networks and support vector machine were used to predict drying rate and final moisture content based on conditions. Higher temperatures and air velocities lead to shorter drying times and lower moisture contents, with higher air velocities enhancing moisture diffusion and reducing energy intensity. Nine drying models were fitted, with the Page and Hii, et al. models showing strong performance. The MultiLayer Perceptron (MLP) 3-8-1 configuration achieves high accuracy, and Support Vector Machine (SVM) models with the Radial Basis Function (RBF) kernel perform well in predicting moisture ratio. These findings offer insights into Indian spinach drying behavior and highlight the effectiveness of machine learning models. The obtained values aid in optimizing drying conditions, and the robust performance of MLP and SVM models holds potential for applications beyond drying, including diabetic retinopathy diagnosis.