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)...

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Main Authors: 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
Format: Article
Language:English
Published: Elsevier 2022-10-01
Series:Food Chemistry Advances
Subjects:
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%).
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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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