Raman spectroscopy and chemometrics for rice quality control and fraud detection

Abstract

A rapid and straightforward classification of rice qualities or detection of food adulteration is necessary to meet the increasing demand of high quality rice, and to protect the consumers and supply chains from food fraud. Raman spectroscopy coupled with chemometrics have been used for multivariate analysis of rice quality and fraud detection. Supervised Kohonen Map (SKM) can classify different rice samples with low errors of Venetian-Blind (= 0.04) and Monte-Carlo (= 0.05) cross validation using the Raman spectral region of 200–1600 cm⁻¹. The classification performance of the FT-IR was examined and compared with those of Raman. For comparison, principal component analysis–linear discriminant analysis (PCA-LDA), classification and regression trees (CART), soft independent modeling by class analogy (SIMCA), and partial least squares-discriminant analysis (PLS-DA) techniques were also used for both Raman and FT-IR spectra. The top-5 classification models are “SKM + multiplicative scatter correction (MSC)” > “SKM + standard normal variate (SNV)” ~ “CART + MSC” > “SIMCA + MSC” > “SIMCA + SNV”. The proposed procedure showed better results than previous studies which can help both the industry and regulatory quality control to rapidly detect rice integrity and food fraud.