Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopy
In the Pu’er tea market, the ubiquity of blending different varieties and the fraudulent representation of vintage years present a persistent challenge. Traditional sensory evaluation and experience are often inadequate for discerning the true variety and vintage of tea, highlighting the need for mo...
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| Format: | Article |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2025.1546702/full |
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| author | Zhenfa Yang Zhenfa Yang Xiaoping Lu Lucheng Chen |
| author_facet | Zhenfa Yang Zhenfa Yang Xiaoping Lu Lucheng Chen |
| author_sort | Zhenfa Yang |
| collection | DOAJ |
| description | In the Pu’er tea market, the ubiquity of blending different varieties and the fraudulent representation of vintage years present a persistent challenge. Traditional sensory evaluation and experience are often inadequate for discerning the true variety and vintage of tea, highlighting the need for more sophisticated analytical methods to ensure authenticity and quality. Fourier transform near infrared diffuse reflectance spectroscopy combined with radial basis function neural network (RBFNN) was applied for determination of the varieties and vintages of Pu’er tea. For vintage identification, the accuracy, precision, recall, and F1-score of the RBFNN model for the prediction set were 99.2%, 98.2%, 98.0%, and 98.0%, respectively. For identification of varieties adulteration, the corresponding parameters were 98.9%, 97.2%, 96.7%, and 96.6%, respectively. These results illustrated the feasibility to identify the adulteration of varieties and misrepresentation of vintages of Pu’er tea with near infrared spectra and RBFNN model, proving an efficient alternative for Pu’er tea quality inspection, and offering a robust method for combating the pervasive issues within the market. |
| format | Article |
| id | doaj-art-f88d5343ba8f4c45918bd5aca8917aca |
| institution | Kabale University |
| issn | 2296-2646 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Chemistry |
| spelling | doaj-art-f88d5343ba8f4c45918bd5aca8917aca2025-02-05T07:32:43ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462025-02-011310.3389/fchem.2025.15467021546702Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopyZhenfa Yang0Zhenfa Yang1Xiaoping Lu2Lucheng Chen3State Key Laboratory of Massive Personalized Customization System and Technology, Qingdao, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, ChinaState Key Laboratory of Massive Personalized Customization System and Technology, Qingdao, ChinaState Key Laboratory of Massive Personalized Customization System and Technology, Qingdao, ChinaIn the Pu’er tea market, the ubiquity of blending different varieties and the fraudulent representation of vintage years present a persistent challenge. Traditional sensory evaluation and experience are often inadequate for discerning the true variety and vintage of tea, highlighting the need for more sophisticated analytical methods to ensure authenticity and quality. Fourier transform near infrared diffuse reflectance spectroscopy combined with radial basis function neural network (RBFNN) was applied for determination of the varieties and vintages of Pu’er tea. For vintage identification, the accuracy, precision, recall, and F1-score of the RBFNN model for the prediction set were 99.2%, 98.2%, 98.0%, and 98.0%, respectively. For identification of varieties adulteration, the corresponding parameters were 98.9%, 97.2%, 96.7%, and 96.6%, respectively. These results illustrated the feasibility to identify the adulteration of varieties and misrepresentation of vintages of Pu’er tea with near infrared spectra and RBFNN model, proving an efficient alternative for Pu’er tea quality inspection, and offering a robust method for combating the pervasive issues within the market.https://www.frontiersin.org/articles/10.3389/fchem.2025.1546702/fullnear infrared spectroscopyradial basis function neural networkPu’er teaadulteration of varietiesmisrepresentation of vintages |
| spellingShingle | Zhenfa Yang Zhenfa Yang Xiaoping Lu Lucheng Chen Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopy Frontiers in Chemistry near infrared spectroscopy radial basis function neural network Pu’er tea adulteration of varieties misrepresentation of vintages |
| title | Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopy |
| title_full | Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopy |
| title_fullStr | Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopy |
| title_full_unstemmed | Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopy |
| title_short | Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopy |
| title_sort | discriminating the adulteration of varieties and misrepresentation of vintages of pu er tea based on fourier transform near infrared diffuse reflectance spectroscopy |
| topic | near infrared spectroscopy radial basis function neural network Pu’er tea adulteration of varieties misrepresentation of vintages |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2025.1546702/full |
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