Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

Abstract

Nowadays, the conventional biochemical methods used to differentiate and characterize rice types, biochemical properties, authentication, and contamination issues are difficult to implement due to the high cost of reagents, time requirement and environmental issues. Actually, the success of agri-food technology is directly related to the quality of analysis of experimental data acquired by sensors or techniques such as the infrared-spectroscopy. To overcome these technical limitations, a rapid and non-destructive methodology for discrimination and classification of rice has been investigated. Near-infrared spectroscopy is considered as fast, clean, and non-destructive analytical tools and its spectra present significant biomolecular information that must be analysed by sophisticated methodologies. Machine learning plays an important role in the analysis of the spectral data being used several methods such as Partial Least Squares, Principal Component Analysis, Partial Least Squares-Discriminant Analysis, Support Vector Machine, Artificial Neuronal Network, among others which can successfully be applied for food classification and discrimination as well as in terms of authentication and contamination issues. The quality control of rice is extremely important at every stage of production, beginning with estimation of raw agricultural materials and monitoring their quality during storage, estimating food quality during the production process and of the final products as well as the determination of their authenticity and the detection of adulterants.

Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain

“…The model which was best for hardness prediction was developed by using 7506-5446.3 and 4605.4-4242.9 cm −1 (1332.3-1836.1 nm and 2171.4-2356.9 nm), which included the amylose vibration band, which was 6834 cm −1 , while the toughness model was from 9403.8 to 6094.3 cm −1 (1063.4-1640.9 nm) and included 6834 and 8316 cm −1 . Amylose content is correlated with retrogradation behavior, which influences the textural properties of cooked rice [69,70].…”