A particular aspect of quality control of virgin olive oil (VOO) is the mandatory application, together with chemical and instrumental determinations, of a standardized and official method for sensory assessment. The latter, known as Panel test, is carried out by trained assessors and contributes to the classification of VOOs into three commercial categories (extra virgin, virgin, and lampante). One drawback of this method is related to the large number of samples to be analyzed, compared to the work capacity of a sensory panel, especially during the selection for purchase by companies that blend and market virgin oils and the quality control conducted by the authorities to verify the declared commercial category. For this reason, it is helpful to develop and validate robust and rapid screening methods, based on volatile fingerprints, to preclassify each sample into one of the three commercial categories. Considering the strict relation between volatile compounds and the main sensory attributes (fruity and defects), a gas-chromatographic volatile fingerprint can be the right choice. In this paper, the comparison of two emerging techniques, namely, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and flash-gas chromatography (FGC), applied on a sample set of 49 VOOs, using calibrations previously built with a larger number of samples, is presented. The number of correctly classified samples, with respect to the commercial category determined by the Panel test, was satisfactory and comparable (92% for HS-GC-IMS, and 94% for FGC), confirming the effectiveness of both methods and the robustness of the predictive models. Practical Applications: The demand for rapid screening tools to reduce the number of samples to be assessed by the Panel test has increased in recent years. The validation of robust models and their joint adoption by companies that market VOOs as well as official control bodies could reduce nonconformities and increase the batches of VOO being controlled, thus better protecting the consumer. Therefore, it is desirable to have different tools available to analyze volatile compounds, together with the associated calibration models, along with detailed instructions for their application, to have different alternatives that suit the equipment of individual laboratories.

Screening tools combined with multivariate data analysis to predict or confirm virgin olive oil classification by the Panel test / Ilaria Grigoletto, Enrico Casadei, Filippo Panni, Enrico Valli, Chiara Cevoli, Alessandra Bendini, Diego Luis García-González, Francesca Focante, Angela Felicita Savino, Stefania Carpino, Tullia Gallina Toschi. - In: EUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY. - ISSN 1438-7697. - ELETTRONICO. - 00:(2024), pp. 2300211.1-2300211.12. [10.1002/ejlt.202300211]

Screening tools combined with multivariate data analysis to predict or confirm virgin olive oil classification by the Panel test

Ilaria Grigoletto;Enrico Casadei;Filippo Panni;Enrico Valli
;
Chiara Cevoli;Alessandra Bendini;Francesca Focante;Tullia Gallina Toschi
2024

Abstract

A particular aspect of quality control of virgin olive oil (VOO) is the mandatory application, together with chemical and instrumental determinations, of a standardized and official method for sensory assessment. The latter, known as Panel test, is carried out by trained assessors and contributes to the classification of VOOs into three commercial categories (extra virgin, virgin, and lampante). One drawback of this method is related to the large number of samples to be analyzed, compared to the work capacity of a sensory panel, especially during the selection for purchase by companies that blend and market virgin oils and the quality control conducted by the authorities to verify the declared commercial category. For this reason, it is helpful to develop and validate robust and rapid screening methods, based on volatile fingerprints, to preclassify each sample into one of the three commercial categories. Considering the strict relation between volatile compounds and the main sensory attributes (fruity and defects), a gas-chromatographic volatile fingerprint can be the right choice. In this paper, the comparison of two emerging techniques, namely, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and flash-gas chromatography (FGC), applied on a sample set of 49 VOOs, using calibrations previously built with a larger number of samples, is presented. The number of correctly classified samples, with respect to the commercial category determined by the Panel test, was satisfactory and comparable (92% for HS-GC-IMS, and 94% for FGC), confirming the effectiveness of both methods and the robustness of the predictive models. Practical Applications: The demand for rapid screening tools to reduce the number of samples to be assessed by the Panel test has increased in recent years. The validation of robust models and their joint adoption by companies that market VOOs as well as official control bodies could reduce nonconformities and increase the batches of VOO being controlled, thus better protecting the consumer. Therefore, it is desirable to have different tools available to analyze volatile compounds, together with the associated calibration models, along with detailed instructions for their application, to have different alternatives that suit the equipment of individual laboratories.
2024
Screening tools combined with multivariate data analysis to predict or confirm virgin olive oil classification by the Panel test / Ilaria Grigoletto, Enrico Casadei, Filippo Panni, Enrico Valli, Chiara Cevoli, Alessandra Bendini, Diego Luis García-González, Francesca Focante, Angela Felicita Savino, Stefania Carpino, Tullia Gallina Toschi. - In: EUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY. - ISSN 1438-7697. - ELETTRONICO. - 00:(2024), pp. 2300211.1-2300211.12. [10.1002/ejlt.202300211]
Ilaria Grigoletto, Enrico Casadei, Filippo Panni, Enrico Valli, Chiara Cevoli, Alessandra Bendini, Diego Luis García-González, Francesca Focante, Angela Felicita Savino, Stefania Carpino, Tullia Gallina Toschi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/967094
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