Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics
The commercial category of virgin olive oil is currently assigned on the basis of chemical-physical and sensory parameters following official methods. Considering the limited number of samples that can be analysed daily by a sensory panel, an instrumental screening tool could be supportive by reduci...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/9/10/1509 |
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| author | Beatriz Quintanilla-Casas Marco Marin Francesc Guardiola Diego Luis García-González Sara Barbieri Alessandra Bendini Tullia Gallina Toschi Stefania Vichi Alba Tres |
| author_facet | Beatriz Quintanilla-Casas Marco Marin Francesc Guardiola Diego Luis García-González Sara Barbieri Alessandra Bendini Tullia Gallina Toschi Stefania Vichi Alba Tres |
| author_sort | Beatriz Quintanilla-Casas |
| collection | DOAJ |
| description | The commercial category of virgin olive oil is currently assigned on the basis of chemical-physical and sensory parameters following official methods. Considering the limited number of samples that can be analysed daily by a sensory panel, an instrumental screening tool could be supportive by reducing the assessors’ workload and improving their performance. The present work aims to in-house validate a screening strategy consisting of two sequential binary partial least squares-discriminant analysis (PLS-DA) models that was suggested to be successful in a proof-of-concept study. This approach is based on the volatile fraction fingerprint obtained by HS-SPME–GC–MS from more than 300 virgin olive oils from two crop seasons graded by six different sensory panels into extra virgin, virgin or lampante categories. Uncertainty ranges were set for the binary classification models according to sensitivity and specificity by means of receiver operating characteristics (ROC) curves, aiming to identify boundary samples. Thereby, performing the screening approach, only the virgin olive oils classified as uncertain (23.3%) would be assessed by a sensory panel, while the rest would be directly classified into a given commercial category (78.9% of correct classification). The sensory panel’s workload would be reduced to less than one-third of the samples. A highly reliable classification of samples would be achieved (84.0%) by combining the proposed screening tool with the reference method (panel test) for the assessment of uncertain samples. |
| first_indexed | 2024-03-10T15:27:31Z |
| format | Article |
| id | doaj.art-5f8ae1ea556546bb89b42e906ce3ec00 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T15:27:31Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-5f8ae1ea556546bb89b42e906ce3ec002023-11-20T17:53:53ZengMDPI AGFoods2304-81582020-10-01910150910.3390/foods9101509Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and ChemometricsBeatriz Quintanilla-Casas0Marco Marin1Francesc Guardiola2Diego Luis García-González3Sara Barbieri4Alessandra Bendini5Tullia Gallina Toschi6Stefania Vichi7Alba Tres8Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, SpainDepartament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, SpainDepartament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, SpainInstituto de la Grasa (CSIC), 41013 Sevilla, SpainDepartment of Agricultural and Food Science, Alma Mater Studiorum-Università di Bologna, 47521 Cesena, ItalyDepartment of Agricultural and Food Science, Alma Mater Studiorum-Università di Bologna, 47521 Cesena, ItalyDepartment of Agricultural and Food Science, Alma Mater Studiorum-Università di Bologna, 47521 Cesena, ItalyDepartament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, SpainDepartament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, SpainThe commercial category of virgin olive oil is currently assigned on the basis of chemical-physical and sensory parameters following official methods. Considering the limited number of samples that can be analysed daily by a sensory panel, an instrumental screening tool could be supportive by reducing the assessors’ workload and improving their performance. The present work aims to in-house validate a screening strategy consisting of two sequential binary partial least squares-discriminant analysis (PLS-DA) models that was suggested to be successful in a proof-of-concept study. This approach is based on the volatile fraction fingerprint obtained by HS-SPME–GC–MS from more than 300 virgin olive oils from two crop seasons graded by six different sensory panels into extra virgin, virgin or lampante categories. Uncertainty ranges were set for the binary classification models according to sensitivity and specificity by means of receiver operating characteristics (ROC) curves, aiming to identify boundary samples. Thereby, performing the screening approach, only the virgin olive oils classified as uncertain (23.3%) would be assessed by a sensory panel, while the rest would be directly classified into a given commercial category (78.9% of correct classification). The sensory panel’s workload would be reduced to less than one-third of the samples. A highly reliable classification of samples would be achieved (84.0%) by combining the proposed screening tool with the reference method (panel test) for the assessment of uncertain samples.https://www.mdpi.com/2304-8158/9/10/1509virgin olive oilsensory qualityvolatile compoundsHS-SPME–GC–MSchemometricspanel test |
| spellingShingle | Beatriz Quintanilla-Casas Marco Marin Francesc Guardiola Diego Luis García-González Sara Barbieri Alessandra Bendini Tullia Gallina Toschi Stefania Vichi Alba Tres Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics Foods virgin olive oil sensory quality volatile compounds HS-SPME–GC–MS chemometrics panel test |
| title | Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics |
| title_full | Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics |
| title_fullStr | Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics |
| title_full_unstemmed | Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics |
| title_short | Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics |
| title_sort | supporting the sensory panel to grade virgin olive oils an in house validated screening tool by volatile fingerprinting and chemometrics |
| topic | virgin olive oil sensory quality volatile compounds HS-SPME–GC–MS chemometrics panel test |
| url | https://www.mdpi.com/2304-8158/9/10/1509 |
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