Journal
INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY
Volume 57, Issue 9, Pages 6211-6225Publisher
WILEY
DOI: 10.1111/ijfs.15913
Keywords
Fingerprint analysis; HS-GC-IMS; olive oil; partial least squares discriminant analysis; volatile flavour compounds
Categories
Funding
- National Key Research and Development Program of China [2019YFD1002403]
- Key Research and Development Program of Gansu Province [21ZD4NK045, 20YF3FA022]
- CAS `Light of West China' Program
- `One Three Five Science and Technology Strategy Program' of LICP
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This study successfully identified volatile flavor compounds in different grades of olive oil and established a classification model based on these compounds, which showed good prediction ability. The study also revealed characteristic flavor substances that can be used as markers to distinguish different grades of olive oil.
How to quickly and accurately identify different grades of olive oil has always been a difficult problem. In this study, fifty-four volatile flavour compounds were identified by HS-GC-IMS analysis of different grades of olive oil, and sensory analysis of volatile compounds was carried out based on literature, among which fifteen compounds were identified from olive oil for the first time. Volatile component fingerprint, principal component analysis and partial least squares discriminant analysis (PLS-DA) were adopted to establish the classification model of different grades of olive oil, which had good fitting and prediction ability. Combined with the PLS-DA model and literature, seven characteristic flavour substances having fruity and green properties, including (E)-2-hexenal-D, (E)-2-pentenal-M/D, 3-pentanone, 1-penten-3-one, 2,3-pentanedione, (E)-3-penten-2-one-D and 2-methylbutanal were confirmed. The contents of these characteristic flavour substances in extra virgin olive oil and other grades were significantly different, which could be regarded as characteristic markers to distinguish different grades of olive oil. In addition, the grades of 11 olive oils were predicted by this PLS-DA model. The results showed that the model had a good ability to discriminate different grades of olive oil.
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