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Identification of tea varieties by mid‐infrared diffuse reflectance spectroscopy coupled with a possibilistic fuzzy c‐means clustering with a fuzzy covariance matrix
Author(s) -
Wu Xiaohong,
Zhou Jing,
Wu Bin,
Sun Jun,
Dai Chunxia
Publication year - 2019
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/jfpe.13298
Subject(s) - fuzzy logic , cluster analysis , principal component analysis , fuzzy clustering , covariance matrix , diffuse reflectance infrared fourier transform , matrix (chemical analysis) , diffuse reflection , mathematics , pattern recognition (psychology) , linear discriminant analysis , artificial intelligence , materials science , analytical chemistry (journal) , chemistry , computer science , statistics , optics , physics , chromatography , composite material , biochemistry , photocatalysis , catalysis
Mid‐infrared diffuse reflectance spectroscopy was used to rapidly and nondestructively identify tea varieties together with the proposed possibilistic fuzzy c‐means (PFCM) clustering with a fuzzy covariance matrix. The mid‐infrared diffuse reflectance spectra of 96 tea samples with three different varieties (Emeishan Maofeng, Level 1, and Level 6 Leshan trimeresurus) were acquired using the FTIR‐7600 infrared spectrometer. First, multiplicative scatter correction was implemented to pretreat the spectral data. Second, principal component analysis was employed to compress the mid‐infrared diffuse reflectance spectral data after preprocessing. Third, linear discriminant analysis was utilized for extracting the identification information required by the fuzzy clustering algorithms. Ultimately, the fuzzy c‐means (FCM) clustering, the allied fuzzy c‐means (AFCM) clustering, the PFCM clustering, and the PFCM clustering with a fuzzy covariance matrix were used to cluster the processed spectral data, respectively. The highest identification accuracy of the PFCM clustering with a fuzzy covariance matrix reached at 100% compared with those of FCM (96.7%), AFCM (94.9%), PFCM (96.3%), and partial least squares discrimination analysis (PLS‐DA) algorithm (33.3%). It is sufficiently demonstrated that the mid‐infrared diffuse reflectance spectroscopy coupled with the PFCM clustering with a fuzzy covariance matrix was a valid method for identifying tea varieties. Practical applications The variety of tea is vitally important to evaluate tea quality in the tea market. Mid‐infrared diffuse reflectance spectroscopy is deemed to be a convenient, rapid, accurate, and nondestructive detection technology in comparison with the traditional methods. In this article, the proposed PFCM clustering with a fuzzy covariance matrix coupled with mid‐infrared diffuse reflectance spectroscopy can be used to determine tea varieties quickly and correctly. The experimental results indicate the application potential in tea quality examination and fake tea products discrimination.