Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses

The multi-elemental composition of three typical Italian Pecorino cheeses, Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF), was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The ICP-OES method here...

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Autores principales:	Francesca Di Donato, Martina Foschi, Nadia Vlad, Alessandra Biancolillo, Leucio Rossi, Angelo Antonio D’Archivio
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Pecorino cheese geographical origin multi-elemental composition ICP-OES PLS-DA Organic chemistry QD241-441
Acceso en línea:	https://doaj.org/article/bd2b0434af3a456bbc8b4674f2b8ca66
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spelling	oai:doaj.org-article:bd2b0434af3a456bbc8b4674f2b8ca662021-11-25T18:28:01ZMulti-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses10.3390/molecules262268751420-3049https://doaj.org/article/bd2b0434af3a456bbc8b4674f2b8ca662021-11-01T00:00:00Zhttps://www.mdpi.com/1420-3049/26/22/6875https://doaj.org/toc/1420-3049The multi-elemental composition of three typical Italian Pecorino cheeses, Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF), was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The ICP-OES method here developed allowed the accurate and precise determination of eight major elements (Ba, Ca, Fe, K, Mg, Na, P, and Zn). The ICP-OES data acquired from 17 PR, 20 PS, and 16 PF samples were processed by unsupervised (Principal Component Analysis, PCA) and supervised (Partial Least Square-Discriminant Analysis, PLS-DA) multivariate methods. PCA revealed a relatively high variability of the multi-elemental composition within the samples of a given variety, and a fairly good separation of the Pecorino cheeses according to the geographical origin. Concerning the supervised classification, PLS-DA has allowed obtaining excellent results, both in calibration (in cross-validation) and in validation (on the external test set). In fact, the model led to a cross-validated total accuracy of 93.3% and a predictive accuracy of 91.3%, corresponding to 2 (over 23) misclassified test samples, indicating the adequacy of the model in discriminating Pecorino cheese in accordance with its origin.Francesca Di DonatoMartina FoschiNadia VladAlessandra BiancolilloLeucio RossiAngelo Antonio D’ArchivioMDPI AGarticlePecorino cheesegeographical originmulti-elemental compositionICP-OESPLS-DAOrganic chemistryQD241-441ENMolecules, Vol 26, Iss 6875, p 6875 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Pecorino cheese geographical origin multi-elemental composition ICP-OES PLS-DA Organic chemistry QD241-441
spellingShingle	Pecorino cheese geographical origin multi-elemental composition ICP-OES PLS-DA Organic chemistry QD241-441 Francesca Di Donato Martina Foschi Nadia Vlad Alessandra Biancolillo Leucio Rossi Angelo Antonio D’Archivio Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses
description	The multi-elemental composition of three typical Italian Pecorino cheeses, Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF), was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The ICP-OES method here developed allowed the accurate and precise determination of eight major elements (Ba, Ca, Fe, K, Mg, Na, P, and Zn). The ICP-OES data acquired from 17 PR, 20 PS, and 16 PF samples were processed by unsupervised (Principal Component Analysis, PCA) and supervised (Partial Least Square-Discriminant Analysis, PLS-DA) multivariate methods. PCA revealed a relatively high variability of the multi-elemental composition within the samples of a given variety, and a fairly good separation of the Pecorino cheeses according to the geographical origin. Concerning the supervised classification, PLS-DA has allowed obtaining excellent results, both in calibration (in cross-validation) and in validation (on the external test set). In fact, the model led to a cross-validated total accuracy of 93.3% and a predictive accuracy of 91.3%, corresponding to 2 (over 23) misclassified test samples, indicating the adequacy of the model in discriminating Pecorino cheese in accordance with its origin.
format	article
author	Francesca Di Donato Martina Foschi Nadia Vlad Alessandra Biancolillo Leucio Rossi Angelo Antonio D’Archivio
author_facet	Francesca Di Donato Martina Foschi Nadia Vlad Alessandra Biancolillo Leucio Rossi Angelo Antonio D’Archivio
author_sort	Francesca Di Donato
title	Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses
title_short	Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses
title_full	Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses
title_fullStr	Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses
title_full_unstemmed	Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses
title_sort	multi-elemental composition data handled by chemometrics for the discrimination of high-value italian pecorino cheeses
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bd2b0434af3a456bbc8b4674f2b8ca66
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Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses

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