Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and...

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Autores principales:	Muhammad Hilal Kabir, Mahamed Lamine Guindo, Rongqin Chen, Fei Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	millet near-infrared spectroscopy geographic origin machine learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/e986624f781649fe8f97a3318a2597c5
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id	oai:doaj.org-article:e986624f781649fe8f97a3318a2597c5
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spelling	oai:doaj.org-article:e986624f781649fe8f97a3318a2597c52021-11-25T17:35:24ZGeographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques10.3390/foods101127672304-8158https://doaj.org/article/e986624f781649fe8f97a3318a2597c52021-11-01T00:00:00Zhttps://www.mdpi.com/2304-8158/10/11/2767https://doaj.org/toc/2304-8158Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (<i>n</i> = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.Muhammad Hilal KabirMahamed Lamine GuindoRongqin ChenFei LiuMDPI AGarticlemilletnear-infrared spectroscopygeographic originmachine learningChemical technologyTP1-1185ENFoods, Vol 10, Iss 2767, p 2767 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	millet near-infrared spectroscopy geographic origin machine learning Chemical technology TP1-1185
spellingShingle	millet near-infrared spectroscopy geographic origin machine learning Chemical technology TP1-1185 Muhammad Hilal Kabir Mahamed Lamine Guindo Rongqin Chen Fei Liu Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques
description	Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (<i>n</i> = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.
format	article
author	Muhammad Hilal Kabir Mahamed Lamine Guindo Rongqin Chen Fei Liu
author_facet	Muhammad Hilal Kabir Mahamed Lamine Guindo Rongqin Chen Fei Liu
author_sort	Muhammad Hilal Kabir
title	Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques
title_short	Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques
title_full	Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques
title_fullStr	Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques
title_full_unstemmed	Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques
title_sort	geographic origin discrimination of millet using vis-nir spectroscopy combined with machine learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e986624f781649fe8f97a3318a2597c5
work_keys_str_mv	AT muhammadhilalkabir geographicorigindiscriminationofmilletusingvisnirspectroscopycombinedwithmachinelearningtechniques AT mahamedlamineguindo geographicorigindiscriminationofmilletusingvisnirspectroscopycombinedwithmachinelearningtechniques AT rongqinchen geographicorigindiscriminationofmilletusingvisnirspectroscopycombinedwithmachinelearningtechniques AT feiliu geographicorigindiscriminationofmilletusingvisnirspectroscopycombinedwithmachinelearningtechniques
_version_	1718412201072525312

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

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