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A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms

Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) t...

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Autores principales: Zhang, Lingzhi, Dai, Haomin, Zhang, Jialin, Zheng, Zhiqiang, Song, Bo, Chen, Jiaya, Lin, Gang, Chen, Linhai, Sun, Weijiang, Huang, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914092/
https://www.ncbi.nlm.nih.gov/pubmed/36766027
http://dx.doi.org/10.3390/foods12030499
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author Zhang, Lingzhi
Dai, Haomin
Zhang, Jialin
Zheng, Zhiqiang
Song, Bo
Chen, Jiaya
Lin, Gang
Chen, Linhai
Sun, Weijiang
Huang, Yan
author_facet Zhang, Lingzhi
Dai, Haomin
Zhang, Jialin
Zheng, Zhiqiang
Song, Bo
Chen, Jiaya
Lin, Gang
Chen, Linhai
Sun, Weijiang
Huang, Yan
author_sort Zhang, Lingzhi
collection PubMed
description Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.
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spelling pubmed-99140922023-02-11 A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms Zhang, Lingzhi Dai, Haomin Zhang, Jialin Zheng, Zhiqiang Song, Bo Chen, Jiaya Lin, Gang Chen, Linhai Sun, Weijiang Huang, Yan Foods Article Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea. MDPI 2023-01-21 /pmc/articles/PMC9914092/ /pubmed/36766027 http://dx.doi.org/10.3390/foods12030499 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Lingzhi
Dai, Haomin
Zhang, Jialin
Zheng, Zhiqiang
Song, Bo
Chen, Jiaya
Lin, Gang
Chen, Linhai
Sun, Weijiang
Huang, Yan
A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms
title A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms
title_full A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms
title_fullStr A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms
title_full_unstemmed A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms
title_short A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms
title_sort study on origin traceability of white tea (white peony) based on near-infrared spectroscopy and machine learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914092/
https://www.ncbi.nlm.nih.gov/pubmed/36766027
http://dx.doi.org/10.3390/foods12030499
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