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...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Fei Xu, Fanzhou Kong, Hong Peng, Shuofei Dong, Weiyu Gao, Guangtao Zhang
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Accès en ligne:https://doaj.org/article/b65f6fbe740d4dac912a39d4c09b304e
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
id oai:doaj.org-article:b65f6fbe740d4dac912a39d4c09b304e
record_format dspace
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