Rice is among the most important food crops, and feeds more than half of the world’s population. The freshness of rice decreases with storage time, therefore a fast and easy system for determining quality is greatly needed. We have investigated the potential of near-infrared spectroscopy (NIRS), combined with chemometric methods, for distinguishing rice samples of one, two, and three years of storage. A total of 240 rice samples were analyzed in this study. Principal components analysis (PCA) was initially conducted to look for possible clustering. Next, two pattern recognition methods (partial least-squares discriminant analysis (PLS-DA), and nearest neighbor (KNN)) were compared for their usefulness in building classification models. All two scored high for sensitivity and specificity, but a difference was seen for predictive accuracy. PLS-DA achieved a 97% predictive accuracy, whereas the KNN model, built after first derivative spectral pretreatment, scored 100%. Therefore, NIRS coupled with chemometric methods can be considered as an effective method to classify rice from different years of storage. This article provides a new technology for the evaluation of rice freshness in the market.
