Artificial intelligence classification and amylose content prediction of rice flour varieties from their pasting features

Abstract

The pasting features of eight varieties of rice flour with varying amylose content were characterized and then subjected to machine learning analysis in order to classify the rice varieties and predict their amylose content. The waxy rice flour with the least amylose showed a peak viscosity in the early stage of heating, and the high amylose rice flours had the highest final viscosity. Principal component analysis showed that 87.2% of the total variability was explained by the two principal components, which were mainly related to peak temperature and final viscosity. When the pasting features were used as a machine learning dataset for classification, the support vector machine classifier was the most effective in correctly classifying the rice flour varieties by showing a high accuracy and F1-score, followed by the decision tree and stochastic gradient descent. In addition, the integration of pasting features with machine learning analysis showed the potential to predict the amylose content of rice flours. These prediction performances were confirmed by validating the models with independent datasets.