Infrared microspectroscopy and machine learning: A novel approach to determine the origin and variety of individual rice grains

Abstract

Accurately distinguishing the origin and variety of rice types is of paramount importance to conducting research on this staple crop. While various methods are currently employed for this purpose, few approaches can verify the identity of single grains rapidly and accurately. In this study, we present a method that integrates machine learning with infrared (IR) microspectroscopy for swift detection of the origin and variety of a single rice grain. To establish the validity of our approach, we assembled a diverse collection of rice samples, comprising 14 distinct types with different origins or varieties. Each rice sample yielded 100 microspectroscopy spectra, resulting in 1400 spectra. We applied two deep learning algorithms, deep neural network (DNN) and convolutional neural network (CNN), for spectral analysis. The 1400 spectra were randomly partitioned into calibration and validation sets at a ratio of 3:1. These datasets were subjected to both DNN and CNN analysis for classification of samples by origin and variety. Following 10,000 iterations, we selected optimal DNN and CNN models. The predication accuracies of the optimal DNN model for calibration and validation sets were 95.4% and 90.0%, respectively. In comparison, the optimal CNN model demonstrated superior accuracy, with 99.8% for the calibration set and 92.0% for the validation set. Based on these results, we selected the CNN model as the final model for field use in rice grain classification.