Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Shuhan, Li, Hongyi, Chen, Chen, Chen, Cheng, Zhao, Deyi, Dong, Bingyu, Lv, Xiaoyi, Zhang, Kai, Xie, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
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
_version_ 1784661847836721152
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
work_keys_str_mv AT hushuhan ramanspectroscopycombinedwithmachinelearningalgorithmstodetectadulteratedsuichangnativehoney
AT lihongyi ramanspectroscopycombinedwithmachinelearningalgorithmstodetectadulteratedsuichangnativehoney
AT chenchen ramanspectroscopycombinedwithmachinelearningalgorithmstodetectadulteratedsuichangnativehoney
AT chencheng ramanspectroscopycombinedwithmachinelearningalgorithmstodetectadulteratedsuichangnativehoney
AT zhaodeyi ramanspectroscopycombinedwithmachinelearningalgorithmstodetectadulteratedsuichangnativehoney
AT dongbingyu ramanspectroscopycombinedwithmachinelearningalgorithmstodetectadulteratedsuichangnativehoney
AT lvxiaoyi ramanspectroscopycombinedwithmachinelearningalgorithmstodetectadulteratedsuichangnativehoney
AT zhangkai ramanspectroscopycombinedwithmachinelearningalgorithmstodetectadulteratedsuichangnativehoney
AT xieyi ramanspectroscopycombinedwithmachinelearningalgorithmstodetectadulteratedsuichangnativehoney