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Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey
Zhejiang Suichang native honey, which is included in the list of China’s National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adu...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891316/ https://www.ncbi.nlm.nih.gov/pubmed/35236873 http://dx.doi.org/10.1038/s41598-022-07222-3 |
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author | Hu, Shuhan Li, Hongyi Chen, Chen Chen, Cheng Zhao, Deyi Dong, Bingyu Lv, Xiaoyi Zhang, Kai Xie, Yi |
author_facet | Hu, Shuhan Li, Hongyi Chen, Chen Chen, Cheng Zhao, Deyi Dong, Bingyu Lv, Xiaoyi Zhang, Kai Xie, Yi |
author_sort | Hu, Shuhan |
collection | PubMed |
description | Zhejiang Suichang native honey, which is included in the list of China’s National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky–Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey. |
format | Online Article Text |
id | pubmed-8891316 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88913162022-03-03 Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey Hu, Shuhan Li, Hongyi Chen, Chen Chen, Cheng Zhao, Deyi Dong, Bingyu Lv, Xiaoyi Zhang, Kai Xie, Yi Sci Rep Article Zhejiang Suichang native honey, which is included in the list of China’s National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky–Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey. Nature Publishing Group UK 2022-03-02 /pmc/articles/PMC8891316/ /pubmed/35236873 http://dx.doi.org/10.1038/s41598-022-07222-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Hu, Shuhan Li, Hongyi Chen, Chen Chen, Cheng Zhao, Deyi Dong, Bingyu Lv, Xiaoyi Zhang, Kai Xie, Yi Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey |
title | Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey |
title_full | Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey |
title_fullStr | Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey |
title_full_unstemmed | Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey |
title_short | Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey |
title_sort | raman spectroscopy combined with machine learning algorithms to detect adulterated suichang native honey |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891316/ https://www.ncbi.nlm.nih.gov/pubmed/35236873 http://dx.doi.org/10.1038/s41598-022-07222-3 |
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