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The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Overview
Journal Molecules
Publisher MDPI
Specialty Biology
Date 2022 Feb 25
PMID 35208985
Authors
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Abstract

Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.

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References
1.
Rust A, Marini F, Allsopp M, Williams P, Manley M . Application of ANOVA-simultaneous component analysis to quantify and characterise effects of age, temperature, syrup adulteration and irradiation on near-infrared (NIR) spectral data of honey. Spectrochim Acta A Mol Biomol Spectrosc. 2021; 253:119546. DOI: 10.1016/j.saa.2021.119546. View

2.
Allen B, Gaskin K, Stewart P . Measurement of body composition by in-vivo neutron-activation analysis. Med J Aust. 1986; 145(7):307. DOI: 10.5694/j.1326-5377.1986.tb113832.x. View

3.
Kucharska M, Grabka J . A review of chromatographic methods for determination of synthetic food dyes. Talanta. 2009; 80(3):1045-51. DOI: 10.1016/j.talanta.2009.09.032. View

4.
Jiang H, Xu W, Chen Q . Determination of tea polyphenols in green tea by homemade color sensitive sensor combined with multivariate analysis. Food Chem. 2020; 319:126584. DOI: 10.1016/j.foodchem.2020.126584. View

5.
Huang G, Yuan L, Shi W, Chen X, Chen X . Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum. Food Chem. 2021; 372:131219. DOI: 10.1016/j.foodchem.2021.131219. View