Quality control, classification, and authentication of Iranian rice varieties using FT-IR spectroscopy and sparse chemometric methods

Abstract

The authentication and classification of rice varieties have attracted much attention in food industry, in recent years. In this study, FTIR spectroscopy and sparse chemometric methods were used for discriminating different classes of Iranian rice samples and detecting adulteration in high-quality products. The sparse version of the linear discriminant analysis (sLDA) was used for developing interpretable classification models and detecting adulteration in rice samples. More than 400 samples from eight different classes of the most favorable Iranian rice varieties were used for developing sparse models. The performance of the sLDA model was compared with common chemometric methods, and the results revealed the superiority of the sLDA in terms of interpretability and prediction accuracy. Moreover, sparse multivariate regression methods including least absolute shrinkage and selection operator (lasso), ridge, and elastic net were used for quantitative analysis and fast assessment of rice adulteration. The overall accuracy for sLDA was 0.967 and the ranges of coefficient of determination (R2) for lasso models were 0.879–0.948, for the test sets. The results showed that the sparse chemometric methods combined with FTIR spectroscopy can be successfully applied as robust and interpretable tools for classification and adulteration detection of rice samples in food industry.