Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion
•Mineral profiles measured by ICP OES were used to identify fraud in rice flour.•LDA models were fitted to assess the authenticity of the different flour types analyzed.•Models based on elemental features achieved correct predictions ranging from 72 to 88%.•PCA based data fusion approach allowed to...
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| Published in: | Food chemistry Vol. 339; p. 128125 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01-03-2021
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| Abstract | •Mineral profiles measured by ICP OES were used to identify fraud in rice flour.•LDA models were fitted to assess the authenticity of the different flour types analyzed.•Models based on elemental features achieved correct predictions ranging from 72 to 88%.•PCA based data fusion approach allowed to detect rice flour adulteration with 100% success rate.•The proposed method is reliable to distinguish among adulterated and unadulterated flour samples.
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. |
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| AbstractList | •Mineral profiles measured by ICP OES were used to identify fraud in rice flour.•LDA models were fitted to assess the authenticity of the different flour types analyzed.•Models based on elemental features achieved correct predictions ranging from 72 to 88%.•PCA based data fusion approach allowed to detect rice flour adulteration with 100% success rate.•The proposed method is reliable to distinguish among adulterated and unadulterated flour samples.
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. 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. |
| ArticleNumber | 128125 |
| Author | Dirchwolf, Pamela Maia Rodríguez-Negrín, Zenaida Pérez-Rodríguez, Michael Pellerano, Roberto Gerardo |
| Author_xml | – sequence: 1 givenname: Michael surname: Pérez-Rodríguez fullname: Pérez-Rodríguez, Michael email: michaelpr1984@gmail.com organization: Centre of Chemical Bioactive (CBQ), Central University of Las Villas – UCLV, Highway to Camajuaní Km 5½, 54830 Santa Clara, VC, Cuba – sequence: 2 givenname: Pamela Maia surname: Dirchwolf fullname: Dirchwolf, Pamela Maia organization: Faculty of Agricultural Sciences, UNNE, Sgto. Cabral 2131, 3400 Corrientes, Argentina – sequence: 3 givenname: Zenaida surname: Rodríguez-Negrín fullname: Rodríguez-Negrín, Zenaida organization: Centre of Chemical Bioactive (CBQ), Central University of Las Villas – UCLV, Highway to Camajuaní Km 5½, 54830 Santa Clara, VC, Cuba – sequence: 4 givenname: Roberto Gerardo surname: Pellerano fullname: Pellerano, Roberto Gerardo organization: Institute of Basic and Applied Chemistry of the Northeast of Argentina (IQUIBA-NEA), National Scientific and Technical Research Council (CONICET), Faculty of Exact and Natural Science and Surveying, National University of the Northeast – UNNE, Av. Libertad 5470, 3400 Corrientes, Argentina |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33152892$$D View this record in MEDLINE/PubMed |
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| Keywords | LDA PCA based data fusion Rice flour Mineral profiles Adulteration |
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| Snippet | •Mineral profiles measured by ICP OES were used to identify fraud in rice flour.•LDA models were fitted to assess the authenticity of the different flour types... The present work proposes to detect adulteration in rice flour using mineral profiles. Eighty-seven flour samples from two rice kinds (Indica and Japonica)... |
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| StartPage | 128125 |
| SubjectTerms | Adulteration Flour - analysis Food Analysis - methods Food Analysis - statistics & numerical data Food Contamination - analysis Food Contamination - statistics & numerical data LDA Mineral profiles Minerals - analysis Oryza - chemistry PCA based data fusion Principal Component Analysis Rice flour |
| Title | Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion |
| URI | https://dx.doi.org/10.1016/j.foodchem.2020.128125 https://www.ncbi.nlm.nih.gov/pubmed/33152892 |
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