Machine learning workflow for the oil uptake prediction of rice flour in a batter-coated fried system

Abstract

Machine learning algorithms were applied to predict the oil uptake of rice flour in batter-coated fried foods, depending on its physicochemical features before frying (amylose content, pasting parameters, and batter viscosity). Low coefficients of Pearson correlation (0.29–0.61) were observed between the oil uptake of rice frying batters and other physicochemical parameters. Based on an exhaustive search method by the regsubsets function, the four experimental features (amylose content, peak time, peak temperature, and final viscosity) were selected as the best subset to affect the oil uptake of rice batters after frying, and then subjected to two machine learning algorithms – multivariable linear regression and multilayer perceptron neural network. Based on K-fold cross-validation, the experimental results were divided into 5 datasets consisting of each 80% training and 20% testing dataset. Compared to the multivariable linear regression (R2 = 0.6204–0.7219), the iterative application of the multilayer perceptron model made a relatively higher prediction (R2 = 0.7388–0.7781) of the oil uptake of rice flour frying batter. Thus, the multilayer perception model with a hidden layer outperformed the multivariable linear regression by showing higher R2 and lower relative error.