4.6 Article

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

Journal

MOLECULES
Volume 27, Issue 4, Pages -

Publisher

MDPI
DOI: 10.3390/molecules27041196

Keywords

tea; fluorescence hyperspectral technology; adulteration degree; non-destructive

Funding

  1. Sichuan Agricultural University [035-1921993093]

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This study developed a rapid and non-destructive method to detect adulterated tea and its degree using fluorescence spectral technology. Two- and six-class discriminant models were established, and the results showed that both classification methods achieved excellent performance. The fluorescence hyperspectral technology has broad applications in the non-destructive detection of adulterated tea and other fields.
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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