1. Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice
- Author
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Weiyu Gao, Guangtao Zhang, Shuofei Dong, Hong Peng, Fanzhou Kong, and Fei Xu
- Subjects
Computer science ,Feature selection ,Tracing ,Machine learning ,computer.software_genre ,01 natural sciences ,Article ,Food processing and manufacture ,0404 agricultural biotechnology ,Industry ,TX341-641 ,Profiling (computer programming) ,Authentication ,Mass spectrometry ,business.industry ,Nutrition. Foods and food supply ,010401 analytical chemistry ,Public Health, Environmental and Occupational Health ,Agriculture ,04 agricultural and veterinary sciences ,TP368-456 ,040401 food science ,0104 chemical sciences ,Random forest ,Support vector machine ,Identification (information) ,Artificial intelligence ,business ,computer ,Model building ,Food Science - 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.
- Published
- 2021