![\"Olives](\"https:\/\/d1v1e13ebw3o15.cloudfront.net\/data\/92836\/pool_and_spa_master\/..jpg\" "\\"Olives")
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University of C\u00f3rdoba (UCO) researchers in Spain\u00a0have developed a\u00a0\u2018lab-on-a-fruit\u2019 approach for predicting the chemical\u00a0profile of extra virgin olive oil (EVOO) directly from the olives prior to extraction.<\/p>\n
Each olive is basically a laboratory of three chemical families that ensures the quality of olive oil: fatty acids, phenols\u00a0and volatile compounds. These chemicals are responsible for the oil’s health benefits, organoleptic properties (such as its fruity aroma)\u00a0and its oxidative stability.<\/p>\n
The usual process to determine the presence of these compounds in a given EVOO\u00a0is to firstly extract the oil and then conduct a laboratory analysis.\u00a0But, what if it weren\u2019t necessary to press the olives to determine the composition of their oil?<\/p>\n
With this idea in mind, researchers Feliciano Priego, Enrique Cabanas, Carlos Ledesma, and M\u00f3nica Calder\u00f3n at the UCO’s Department of Analytical Chemistry developed the\u00a0novel method for predicting the chemical profile of olive oil before its extraction, using only the olives.<\/p>\n
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Image credit: University of C\u00f3rdoba<\/p>\n
\u201cThe idea was to use a simple and quick sampling method to assess the three main chemical families in olive oil, and we tried the simplest approach: we removed the olive pit and placed a solid-phase microextraction fibre inside, which absorbs the volatile compounds and gives us the sensory profile. Then, using the material extracted in the syringe we use for pitting, we determine the phenolic profile and fatty acids,\u201d\u00a0Ledesma said.<\/p>\n
The results showed that the most abundant compounds could be identified based on a single olive. A total of 79 metabolites were identified (13 fatty acids, 21 phenols\u00a0and 45 volatile compounds).<\/p>\n
\u201cFor fatty acids, we have profiles of 17\/18 and were able to detect 13 of them, including the most abundant ones,\u201d\u00a0Ledesma said.<\/p>\n
Calder\u00f3n was responsible for modelling the predictive panels used to classify the oils, a tool that combines different compounds and different cut-off points to determine the classification of the oils based on two or three compounds. \u201cIt was surprising because we achieved 100% accuracy,\u201d\u00a0said Calder\u00f3n.\u00a0The method’s robustness was also validated by performing the same tests on olives and on oil and correlating the results.<\/p>\n
Eight olive varieties were studied by the researchers, with four samples of each variety, obtained from the UCO\u2019s World Olive Germplasm Bank. Prior knowledge of the chemical profiles of each variety and the metabolic pathways studied by the research group also laid the foundation for developing and validating this analysis method.<\/p>\n
At the industrial level, this method not only offers speed and simplicity, but also makes it possible to determine optimal\u00a0harvest times. By monitoring how the chemical profile of the olives evolves, producers can choose the ideal harvest time to produce the sensory properties desired and the phenolic compounds with the highest demand on the market.<\/p>\n
The researchers said the method could therefore be used to predict the correct point in the ripening process for oil that matches a certain\u00a0criteria. For example, some oil manufacturers may be looking for specific antioxidants, such as oleocanthal, or for a high phenol content to meet health claim requirements\u00a0on the label.\u00a0It also\u00a0paves the way to exploring other varieties, and could even be applied to other fruits.<\/p>\n
The next challenge for the research team is to\u00a0conduct a long-term ripening study to see how the compounds change, or to take the method into fields so that olives can be extracted and analysed directly.<\/p>\n