Food analysis by portable NIR spectrometer
Extra-virgin-olive oil, honey, milk, and yogurt have associated high nutritional and commercial value. Tampering/non-conformance in these products can damage consumer's health. Therefore, rigorous quality control over the ingredients purity and declaration is necessary. The Near-infrared (NIR)...
Main Authors: | , , , , , , , , , , |
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Format: | Article |
Language: | English |
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Elsevier
2022-10-01
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Series: | Food Chemistry Advances |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2772753X22000624 |
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author | Gabriely S. Folli Layla P. Santos Francine D. Santos Pedro H.P. Cunha Izabela F. Schaffel Flávia T. Borghi Iago H.A.S. Barros André A. Pires Araceli V.F.N. Ribeiro Wanderson Romão Paulo R. Filgueiras |
author_facet | Gabriely S. Folli Layla P. Santos Francine D. Santos Pedro H.P. Cunha Izabela F. Schaffel Flávia T. Borghi Iago H.A.S. Barros André A. Pires Araceli V.F.N. Ribeiro Wanderson Romão Paulo R. Filgueiras |
author_sort | Gabriely S. Folli |
collection | DOAJ |
description | Extra-virgin-olive oil, honey, milk, and yogurt have associated high nutritional and commercial value. Tampering/non-conformance in these products can damage consumer's health. Therefore, rigorous quality control over the ingredients purity and declaration is necessary. The Near-infrared (NIR) is used to identify/quantify food adulterants, however the developed analytical methodologies need multivariate analysis. The portable NIR instrument enables on-site analysis, requires a few seconds, small sample volume, no sample destruction, and presents low maintenance costs. In this paper we were to classify [one-class and multi-class Support Vectors Machine (SVM), Partial Least Squares Discriminant Analysis (PLS-DA)] and PLS to quantify food adulterants using a portable NIR. The generation of artificial outliers in the one-class SVM models showed satisfactory results for authenticity analysis. The results showed that SVM (Test accuracy = 0.90-1.00) obtained better metrics compared to PLS-DA (Test accuracy = 0.83-0.97). The PLS obtained excellent accuracy: honey (RMSEP = 0.57 wt%), EVOO (RMSEP = 2.06 wt%), milk (RMSEP = 0.20 wt%), and yogurt (RMSEP = 0.06 wt%). |
first_indexed | 2024-04-13T05:08:42Z |
format | Article |
id | doaj.art-f056297023964bd88531dac255a9e8b8 |
institution | Directory Open Access Journal |
issn | 2772-753X |
language | English |
last_indexed | 2024-04-13T05:08:42Z |
publishDate | 2022-10-01 |
publisher | Elsevier |
record_format | Article |
series | Food Chemistry Advances |
spelling | doaj.art-f056297023964bd88531dac255a9e8b82022-12-22T03:01:06ZengElsevierFood Chemistry Advances2772-753X2022-10-011100074Food analysis by portable NIR spectrometerGabriely S. Folli0Layla P. Santos1Francine D. Santos2Pedro H.P. Cunha3Izabela F. Schaffel4Flávia T. Borghi5Iago H.A.S. Barros6André A. Pires7Araceli V.F.N. Ribeiro8Wanderson Romão9Paulo R. Filgueiras10Laboratório de Petroleômica e Forense, Universidade Federal do Espírito Santo (UFES), Avenida Fernando Ferrari, 514, Goiabeiras, 29075-910 Vitória-ES, BrazilLaboratório de Petroleômica e Forense, Universidade Federal do Espírito Santo (UFES), Avenida Fernando Ferrari, 514, Goiabeiras, 29075-910 Vitória-ES, BrazilLaboratório de Petroleômica e Forense, Universidade Federal do Espírito Santo (UFES), Avenida Fernando Ferrari, 514, Goiabeiras, 29075-910 Vitória-ES, BrazilLaboratório de Petroleômica e Forense, Universidade Federal do Espírito Santo (UFES), Avenida Fernando Ferrari, 514, Goiabeiras, 29075-910 Vitória-ES, BrazilInstituto Federal do Espírito Santo (IFES), Av. Ministro Salgado Filho, 1000, Soteco, 29106-010 Vila Velha-ES, BrazilLaboratório de Petroleômica e Forense, Universidade Federal do Espírito Santo (UFES), Avenida Fernando Ferrari, 514, Goiabeiras, 29075-910 Vitória-ES, BrazilLaboratório de Petroleômica e Forense, Universidade Federal do Espírito Santo (UFES), Avenida Fernando Ferrari, 514, Goiabeiras, 29075-910 Vitória-ES, BrazilInstituto Federal do Espírito Santo (IFES), Av. Ministro Salgado Filho, 1000, Soteco, 29106-010 Vila Velha-ES, BrazilInstituto Federal do Espírito Santo (IFES), Av. Ministro Salgado Filho, 1000, Soteco, 29106-010 Vila Velha-ES, BrazilLaboratório de Petroleômica e Forense, Universidade Federal do Espírito Santo (UFES), Avenida Fernando Ferrari, 514, Goiabeiras, 29075-910 Vitória-ES, Brazil; Instituto Federal do Espírito Santo (IFES), Av. Ministro Salgado Filho, 1000, Soteco, 29106-010 Vila Velha-ES, BrazilLaboratório de Petroleômica e Forense, Universidade Federal do Espírito Santo (UFES), Avenida Fernando Ferrari, 514, Goiabeiras, 29075-910 Vitória-ES, Brazil; Corresponding author:Extra-virgin-olive oil, honey, milk, and yogurt have associated high nutritional and commercial value. Tampering/non-conformance in these products can damage consumer's health. Therefore, rigorous quality control over the ingredients purity and declaration is necessary. The Near-infrared (NIR) is used to identify/quantify food adulterants, however the developed analytical methodologies need multivariate analysis. The portable NIR instrument enables on-site analysis, requires a few seconds, small sample volume, no sample destruction, and presents low maintenance costs. In this paper we were to classify [one-class and multi-class Support Vectors Machine (SVM), Partial Least Squares Discriminant Analysis (PLS-DA)] and PLS to quantify food adulterants using a portable NIR. The generation of artificial outliers in the one-class SVM models showed satisfactory results for authenticity analysis. The results showed that SVM (Test accuracy = 0.90-1.00) obtained better metrics compared to PLS-DA (Test accuracy = 0.83-0.97). The PLS obtained excellent accuracy: honey (RMSEP = 0.57 wt%), EVOO (RMSEP = 2.06 wt%), milk (RMSEP = 0.20 wt%), and yogurt (RMSEP = 0.06 wt%).http://www.sciencedirect.com/science/article/pii/S2772753X22000624Tamperhandheldchemometricsartificial outliersportable NIRfoods |
spellingShingle | Gabriely S. Folli Layla P. Santos Francine D. Santos Pedro H.P. Cunha Izabela F. Schaffel Flávia T. Borghi Iago H.A.S. Barros André A. Pires Araceli V.F.N. Ribeiro Wanderson Romão Paulo R. Filgueiras Food analysis by portable NIR spectrometer Food Chemistry Advances Tamper handheld chemometrics artificial outliers portable NIR foods |
title | Food analysis by portable NIR spectrometer |
title_full | Food analysis by portable NIR spectrometer |
title_fullStr | Food analysis by portable NIR spectrometer |
title_full_unstemmed | Food analysis by portable NIR spectrometer |
title_short | Food analysis by portable NIR spectrometer |
title_sort | food analysis by portable nir spectrometer |
topic | Tamper handheld chemometrics artificial outliers portable NIR foods |
url | http://www.sciencedirect.com/science/article/pii/S2772753X22000624 |
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