Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion

The present work proposes to detect adulteration in rice flour using mineral profiles. Eighty-seven flour samples from two rice kinds (Indica and Japonica) plus thirty adulterated flour samples were analyzed by ICP OES. After obtaining the quantitative elemental fingerprint of the samples, PCA and L...

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Autores principales: Pérez Rodríguez, Michael, Dirchwolf, Pamela Maia, Rodríguez Negrín, Zenaida, Pellerano, Roberto Gerardo
Formato: Artículo
Lenguaje:Inglés
Publicado: Elsevier 2025
Materias:
Rice flour
Adulteration
Mineral profiles
LDA
PCA based data fusion
Acceso en línea:http://repositorio.unne.edu.ar/handle/123456789/59134
Aporte de:
RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) de Universidad Nacional del Nordeste
id I48-R184-123456789-59134
record_format dspace
spelling I48-R184-123456789-591342025-12-05T11:20:02Z Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion Pérez Rodríguez, Michael Dirchwolf, Pamela Maia Rodríguez Negrín, Zenaida Pellerano, Roberto Gerardo Rice flour Adulteration Mineral profiles LDA PCA based data fusion The present work proposes to detect adulteration in rice flour using mineral profiles. Eighty-seven flour samples from two rice kinds (Indica and Japonica) plus thirty adulterated flour samples were analyzed by ICP OES. After obtaining the quantitative elemental fingerprint of the samples, PCA and LDA were applied. Binary and multiclass associations were considered to assess rice flour authenticity through fraud identification. Models based on element predictors showed accuracies ranging from 72 to 88% to distinguish adulterated and unadulterated samples. The fusion of the mineral features with the principal components (PCs) obtained from PCA provided classification rates of 100% in training samples, and 91–100% in test samples. The proposed method proved to be a useful tool for quality control in the rice industry since a perfect success rate was achieved for rice flour fraud detection. 2025-12-05T11:11:37Z 2025-12-05T11:11:37Z 2020-09-17 Artículo Pérez Rodríguez, Michael, et al., 2021. Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion. Food Chemistry. Ámsterdam: Países Bajos, vol. 339, p. 1-7. E-ISSN 2772-753X. DOI https://doi.org/10.1016/j.foodchem.2020.128125 http://repositorio.unne.edu.ar/handle/123456789/59134 en https://doi.org/10.1016/j.foodchem.2020.128125 restrictedAccess http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ application/pdf p. 1-7 application/pdf Elsevier Food Chemistry, 2021, vol. 339, p. 1-7.
institution Universidad Nacional del Nordeste
institution_str I-48
repository_str R-184
collection RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
language Inglés
topic Rice flour
Adulteration
Mineral profiles
LDA
PCA based data fusion
spellingShingle Rice flour
Adulteration
Mineral profiles
LDA
PCA based data fusion
Pérez Rodríguez, Michael
Dirchwolf, Pamela Maia
Rodríguez Negrín, Zenaida
Pellerano, Roberto Gerardo
Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion
topic_facet Rice flour
Adulteration
Mineral profiles
LDA
PCA based data fusion
description The present work proposes to detect adulteration in rice flour using mineral profiles. Eighty-seven flour samples from two rice kinds (Indica and Japonica) plus thirty adulterated flour samples were analyzed by ICP OES. After obtaining the quantitative elemental fingerprint of the samples, PCA and LDA were applied. Binary and multiclass associations were considered to assess rice flour authenticity through fraud identification. Models based on element predictors showed accuracies ranging from 72 to 88% to distinguish adulterated and unadulterated samples. The fusion of the mineral features with the principal components (PCs) obtained from PCA provided classification rates of 100% in training samples, and 91–100% in test samples. The proposed method proved to be a useful tool for quality control in the rice industry since a perfect success rate was achieved for rice flour fraud detection.
format Artículo
author Pérez Rodríguez, Michael
Dirchwolf, Pamela Maia
Rodríguez Negrín, Zenaida
Pellerano, Roberto Gerardo
author_facet Pérez Rodríguez, Michael
Dirchwolf, Pamela Maia
Rodríguez Negrín, Zenaida
Pellerano, Roberto Gerardo
author_sort Pérez Rodríguez, Michael
title Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion
title_short Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion
title_full Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion
title_fullStr Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion
title_full_unstemmed Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion
title_sort assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion
publisher Elsevier
publishDate 2025
url http://repositorio.unne.edu.ar/handle/123456789/59134
work_keys_str_mv AT perezrodriguezmichael assessingmineralprofilesforriceflourfrauddetectionbyprincipalcomponentanalysisbaseddatafusion
AT dirchwolfpamelamaia assessingmineralprofilesforriceflourfrauddetectionbyprincipalcomponentanalysisbaseddatafusion
AT rodrigueznegrinzenaida assessingmineralprofilesforriceflourfrauddetectionbyprincipalcomponentanalysisbaseddatafusion
AT pelleranorobertogerardo assessingmineralprofilesforriceflourfrauddetectionbyprincipalcomponentanalysisbaseddatafusion
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