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Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors
Californian-style black olives can undergo different chemical changes during the sterilization process that can affect their sensory and phenol characteristics. Thus, these olives were stuffed with flavoured hydrocolloids and submitted to different thermal sterilization treatments to assess sensory...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093727/ https://www.ncbi.nlm.nih.gov/pubmed/37048198 http://dx.doi.org/10.3390/foods12071377 |
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author | Cascos, Gema Barea-Ramos, Juan Diego Montero-Fernández, Ismael Ruiz-Canales, Antonio Lozano, Jesús Martín-Vertedor, Daniel |
author_facet | Cascos, Gema Barea-Ramos, Juan Diego Montero-Fernández, Ismael Ruiz-Canales, Antonio Lozano, Jesús Martín-Vertedor, Daniel |
author_sort | Cascos, Gema |
collection | PubMed |
description | Californian-style black olives can undergo different chemical changes during the sterilization process that can affect their sensory and phenol characteristics. Thus, these olives were stuffed with flavoured hydrocolloids and submitted to different thermal sterilization treatments to assess sensory categories. The triangular test indicated that the panellists were able to discriminate between samples from different categories according to their aromas with more than 85% success. The results indicated that the negative aroma detected by tasters was related to burn defects. The highest level of defects was found in standard olives, while the lowest was identified in the extra category. Furthermore, olives submitted to the lowest thermal sterilization treatment (extra) presented significantly higher phenol profile content, such as for hydroxytyrosol, tyrosol, oleuropein and procyanidin B1. The electronic nose (E-nose) discriminated between samples from different categories according to the specific aroma (PC1 = 82.1% and PC2 = 15.1%). The PLS-DA classified the samples with 90.9% accuracy. Furthermore, the volatile organic compounds responsible for this discrimination were creosol, copaene, benzaldehyde and diallyl disulphide. Finally, the models established by the PLS analysis indicated that the E-nose could predict olives according to their aroma and total phenol profile [Formula: see text] values were 0.89 and 0.92, respectively). Thus, this device could be used at the industrial level to discriminate between olives with different sensory aromas to determine those with the highest quality. |
format | Online Article Text |
id | pubmed-10093727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100937272023-04-13 Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors Cascos, Gema Barea-Ramos, Juan Diego Montero-Fernández, Ismael Ruiz-Canales, Antonio Lozano, Jesús Martín-Vertedor, Daniel Foods Article Californian-style black olives can undergo different chemical changes during the sterilization process that can affect their sensory and phenol characteristics. Thus, these olives were stuffed with flavoured hydrocolloids and submitted to different thermal sterilization treatments to assess sensory categories. The triangular test indicated that the panellists were able to discriminate between samples from different categories according to their aromas with more than 85% success. The results indicated that the negative aroma detected by tasters was related to burn defects. The highest level of defects was found in standard olives, while the lowest was identified in the extra category. Furthermore, olives submitted to the lowest thermal sterilization treatment (extra) presented significantly higher phenol profile content, such as for hydroxytyrosol, tyrosol, oleuropein and procyanidin B1. The electronic nose (E-nose) discriminated between samples from different categories according to the specific aroma (PC1 = 82.1% and PC2 = 15.1%). The PLS-DA classified the samples with 90.9% accuracy. Furthermore, the volatile organic compounds responsible for this discrimination were creosol, copaene, benzaldehyde and diallyl disulphide. Finally, the models established by the PLS analysis indicated that the E-nose could predict olives according to their aroma and total phenol profile [Formula: see text] values were 0.89 and 0.92, respectively). Thus, this device could be used at the industrial level to discriminate between olives with different sensory aromas to determine those with the highest quality. MDPI 2023-03-24 /pmc/articles/PMC10093727/ /pubmed/37048198 http://dx.doi.org/10.3390/foods12071377 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cascos, Gema Barea-Ramos, Juan Diego Montero-Fernández, Ismael Ruiz-Canales, Antonio Lozano, Jesús Martín-Vertedor, Daniel Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors |
title | Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors |
title_full | Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors |
title_fullStr | Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors |
title_full_unstemmed | Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors |
title_short | Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors |
title_sort | burn defect and phenol prediction for flavoured californian-style black olives using digital sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093727/ https://www.ncbi.nlm.nih.gov/pubmed/37048198 http://dx.doi.org/10.3390/foods12071377 |
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