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An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test

Sensory evaluation, carried out by panel tests, is essential for quality classification of virgin olive oils (VOOs), but is time consuming and costly when many samples need to be assessed; sensory evaluation could be assisted by the application of screening methods. Rapid instrumental methods based...

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Autores principales: Valli, Enrico, Panni, Filippo, Casadei, Enrico, Barbieri, Sara, Cevoli, Chiara, Bendini, Alessandra, García-González, Diego L., Gallina Toschi, Tullia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278584/
https://www.ncbi.nlm.nih.gov/pubmed/32443697
http://dx.doi.org/10.3390/foods9050657
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author Valli, Enrico
Panni, Filippo
Casadei, Enrico
Barbieri, Sara
Cevoli, Chiara
Bendini, Alessandra
García-González, Diego L.
Gallina Toschi, Tullia
author_facet Valli, Enrico
Panni, Filippo
Casadei, Enrico
Barbieri, Sara
Cevoli, Chiara
Bendini, Alessandra
García-González, Diego L.
Gallina Toschi, Tullia
author_sort Valli, Enrico
collection PubMed
description Sensory evaluation, carried out by panel tests, is essential for quality classification of virgin olive oils (VOOs), but is time consuming and costly when many samples need to be assessed; sensory evaluation could be assisted by the application of screening methods. Rapid instrumental methods based on the analysis of volatile molecules might be considered interesting to assist the panel test through fast pre-classification of samples with a known level of probability, thus increasing the efficiency of quality control. With this objective, a headspace gas chromatography-ion mobility spectrometer (HS-GC-IMS) was used to analyze 198 commercial VOOs (extra virgin, virgin and lampante) by a semi-targeted approach. Different partial least squares-discriminant analysis (PLS-DA) chemometric models were then built by data matrices composed of 15 volatile compounds, which were previously selected as markers: a first approach was proposed to classify samples according to their quality grade and a second based on the presence of sensory defects. The performance (intra-day and inter-day repeatability, linearity) of the method was evaluated. The average percentages of correctly classified samples obtained from the two models were satisfactory, namely 77% (prediction of the quality grades) and 64% (prediction of the presence of three defects) in external validation, thus demonstrating that this easy-to-use screening instrumental approach is promising to support the work carried out by panel tests.
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spelling pubmed-72785842020-06-12 An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test Valli, Enrico Panni, Filippo Casadei, Enrico Barbieri, Sara Cevoli, Chiara Bendini, Alessandra García-González, Diego L. Gallina Toschi, Tullia Foods Article Sensory evaluation, carried out by panel tests, is essential for quality classification of virgin olive oils (VOOs), but is time consuming and costly when many samples need to be assessed; sensory evaluation could be assisted by the application of screening methods. Rapid instrumental methods based on the analysis of volatile molecules might be considered interesting to assist the panel test through fast pre-classification of samples with a known level of probability, thus increasing the efficiency of quality control. With this objective, a headspace gas chromatography-ion mobility spectrometer (HS-GC-IMS) was used to analyze 198 commercial VOOs (extra virgin, virgin and lampante) by a semi-targeted approach. Different partial least squares-discriminant analysis (PLS-DA) chemometric models were then built by data matrices composed of 15 volatile compounds, which were previously selected as markers: a first approach was proposed to classify samples according to their quality grade and a second based on the presence of sensory defects. The performance (intra-day and inter-day repeatability, linearity) of the method was evaluated. The average percentages of correctly classified samples obtained from the two models were satisfactory, namely 77% (prediction of the quality grades) and 64% (prediction of the presence of three defects) in external validation, thus demonstrating that this easy-to-use screening instrumental approach is promising to support the work carried out by panel tests. MDPI 2020-05-20 /pmc/articles/PMC7278584/ /pubmed/32443697 http://dx.doi.org/10.3390/foods9050657 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Valli, Enrico
Panni, Filippo
Casadei, Enrico
Barbieri, Sara
Cevoli, Chiara
Bendini, Alessandra
García-González, Diego L.
Gallina Toschi, Tullia
An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test
title An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test
title_full An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test
title_fullStr An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test
title_full_unstemmed An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test
title_short An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test
title_sort hs-gc-ims method for the quality classification of virgin olive oils as screening support for the panel test
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278584/
https://www.ncbi.nlm.nih.gov/pubmed/32443697
http://dx.doi.org/10.3390/foods9050657
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