Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods

One of the most important problems associated with the rice industry is the authenticity, mainly the identification of varieties by providing a reliable, fast, yet accurate method. To overcome these limitations, the development of fast and non-destructive methodologies for different rice type classification is, nowadays, a huge challenge for producers. The near-infrared (NIR) spectroscopy associated to principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and support vector machines (SVM) for discrimination and classification of rice varieties ( Indica and Japonica ) were explored after different spectra processing steps such as multiplicative scatter correction (MSC), first derivative and second derivative. The SVM model developed after the MSC processing procedure, showed a significant fitting accuracy (97%), cross-validation (93%) and prediction (91%). These data support the robustness of the model for efficient rice types classification. In terms of spectral analysis, the major differences between both rice types are present at range 7476–7095 cm −1 , 7046 cm −1 and 4264–4153 cm −1 , which can be used for its discrimination. This study showed that NIR spectroscopy associated to PLS-DA and SVM techniques allowed an efficient discrimination of rice samples, being considered as a suitable strategy for a competent system for fully automated classification and sorting of rice types grouping with a high level of accuracy, representing a valuable approach for discrimination and anti-fraud procedure for food control as well as in terms of security issues of any product.