Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice

Abstract Identification of geographical origin is of great importance for protecting the authenticity of valuable agri-food products with designated origins. In this study, a robust and accurate analytical method that could authenticate the geographical origin of Geographical Indication (GI) product...

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Autores principales:	Fei Xu, Fanzhou Kong, Hong Peng, Shuofei Dong, Weiyu Gao, Guangtao Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Nutrition. Foods and food supply TX341-641 Food processing and manufacture TP368-456
Acceso en línea:	https://doaj.org/article/b65f6fbe740d4dac912a39d4c09b304e
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spelling	oai:doaj.org-article:b65f6fbe740d4dac912a39d4c09b304e2021-12-02T18:34:00ZCombing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice10.1038/s41538-021-00100-82396-8370https://doaj.org/article/b65f6fbe740d4dac912a39d4c09b304e2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41538-021-00100-8https://doaj.org/toc/2396-8370Abstract Identification of geographical origin is of great importance for protecting the authenticity of valuable agri-food products with designated origins. In this study, a robust and accurate analytical method that could authenticate the geographical origin of Geographical Indication (GI) products was developed. The method was based on elemental profiling using inductively coupled plasma mass spectrometry (ICP-MS) in combination with machine learning techniques for model building and feature selection. The method successfully predicted and classified six varieties of Chinese GI rice. The elemental profiles of 131 rice samples were determined, and two machine learning algorithms were implemented, support vector machines (SVM) and random forest (RF), together with the feature selection algorithm Relief. Prediction accuracy of 100% was achieved by both Relief-SVM and Relief-RF models, using only four elements (Al, B, Rb, and Na). The methodology and knowledge from this study could be used to develop reliable methods for tracing geographical origins and controlling fraudulent labeling of diverse high-value agri-food products.Fei XuFanzhou KongHong PengShuofei DongWeiyu GaoGuangtao ZhangNature PortfolioarticleNutrition. Foods and food supplyTX341-641Food processing and manufactureTP368-456ENnpj Science of Food, Vol 5, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Nutrition. Foods and food supply TX341-641 Food processing and manufacture TP368-456
spellingShingle	Nutrition. Foods and food supply TX341-641 Food processing and manufacture TP368-456 Fei Xu Fanzhou Kong Hong Peng Shuofei Dong Weiyu Gao Guangtao Zhang Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice
description	Abstract Identification of geographical origin is of great importance for protecting the authenticity of valuable agri-food products with designated origins. In this study, a robust and accurate analytical method that could authenticate the geographical origin of Geographical Indication (GI) products was developed. The method was based on elemental profiling using inductively coupled plasma mass spectrometry (ICP-MS) in combination with machine learning techniques for model building and feature selection. The method successfully predicted and classified six varieties of Chinese GI rice. The elemental profiles of 131 rice samples were determined, and two machine learning algorithms were implemented, support vector machines (SVM) and random forest (RF), together with the feature selection algorithm Relief. Prediction accuracy of 100% was achieved by both Relief-SVM and Relief-RF models, using only four elements (Al, B, Rb, and Na). The methodology and knowledge from this study could be used to develop reliable methods for tracing geographical origins and controlling fraudulent labeling of diverse high-value agri-food products.
format	article
author	Fei Xu Fanzhou Kong Hong Peng Shuofei Dong Weiyu Gao Guangtao Zhang
author_facet	Fei Xu Fanzhou Kong Hong Peng Shuofei Dong Weiyu Gao Guangtao Zhang
author_sort	Fei Xu
title	Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice
title_short	Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice
title_full	Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice
title_fullStr	Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice
title_full_unstemmed	Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice
title_sort	combing machine learning and elemental profiling for geographical authentication of chinese geographical indication (gi) rice
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b65f6fbe740d4dac912a39d4c09b304e
work_keys_str_mv	AT feixu combingmachinelearningandelementalprofilingforgeographicalauthenticationofchinesegeographicalindicationgirice AT fanzhoukong combingmachinelearningandelementalprofilingforgeographicalauthenticationofchinesegeographicalindicationgirice AT hongpeng combingmachinelearningandelementalprofilingforgeographicalauthenticationofchinesegeographicalindicationgirice AT shuofeidong combingmachinelearningandelementalprofilingforgeographicalauthenticationofchinesegeographicalindicationgirice AT weiyugao combingmachinelearningandelementalprofilingforgeographicalauthenticationofchinesegeographicalindicationgirice AT guangtaozhang combingmachinelearningandelementalprofilingforgeographicalauthenticationofchinesegeographicalindicationgirice
_version_	1718377941698609152

Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice

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