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Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils
Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electro...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6458368/ https://www.ncbi.nlm.nih.gov/pubmed/31032057 http://dx.doi.org/10.1098/rsos.190002 |
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author | Zhou, Qi Liu, Shaomin Liu, Ye Song, Huanlu |
author_facet | Zhou, Qi Liu, Shaomin Liu, Ye Song, Huanlu |
author_sort | Zhou, Qi |
collection | PubMed |
description | Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction–gas chromatography–mass spectrometry corresponding to 4-methyl-2-pentanol, (E)-2-hexenal, 1-tridecene, hexyl acetate, (Z)-3-hexenyl acetate, (E)-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control. |
format | Online Article Text |
id | pubmed-6458368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-64583682019-04-26 Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils Zhou, Qi Liu, Shaomin Liu, Ye Song, Huanlu R Soc Open Sci Chemistry Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction–gas chromatography–mass spectrometry corresponding to 4-methyl-2-pentanol, (E)-2-hexenal, 1-tridecene, hexyl acetate, (Z)-3-hexenyl acetate, (E)-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control. The Royal Society 2019-03-27 /pmc/articles/PMC6458368/ /pubmed/31032057 http://dx.doi.org/10.1098/rsos.190002 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Chemistry Zhou, Qi Liu, Shaomin Liu, Ye Song, Huanlu Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils |
title | Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils |
title_full | Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils |
title_fullStr | Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils |
title_full_unstemmed | Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils |
title_short | Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils |
title_sort | comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6458368/ https://www.ncbi.nlm.nih.gov/pubmed/31032057 http://dx.doi.org/10.1098/rsos.190002 |
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