The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

• Published in: Molecules (Basel, Switzerland) Vol. 29; no. 3; p. 682
• Main Authors: Kang, Zhiliang, Fan, Rongsheng, Zhan, Chunyi, Wu, Youli, Lin, Yi, Li, Kunyu, Qing, Rui, Xu, Lijia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2024
• Subjects: Algorithms; Authenticity; Consumers; Fatty acids; Fluorescence; fluorescence hyperspectral technology; Food safety; Identification; Machine Learning; Methods; non-destructive; Oryza; Rice; Spectroscopy, Near-Infrared - methods; Spectrum analysis; Support Vector Machine; variety classification; Viscosity; Thailand; China; variety classification; rice; fluorescence hyperspectral technology; non-destructive
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules29030682

A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.