A complementary analytical classification method to categorize olive oil

Olive oil is a noble product with a very long tradition, with particular concern to the Mediterranean area. It is a high-value agricultural product and is considered a healthy product, for example, it may reduce the risk of cardiovascular disease and cancer.

Authentication and characterization of olive oil have an important issue related to organoleptic properties because they are influenced by the environmental and processing conditions.

Currently, olive oil classification is very costly and slow. Categorizing oil into extra virgin (EVOO), virgin (VOO) and lampante olive oil (LOO) requires an official method, which involves a physicochemical analysis and a sensory analysis in the end.

During the end process, expert tasters try each olive oil one by one in order to determine its category. This process is very costly for the bottlers. Moreover, there are very few expert olive oil tasters in other countries, hence the urgency to find another way to categorize olive oil that does not involve sensory analysis. For this reason, scientists are keen on developing a complementary analytical classification method.

Thus, scientists at the University of Cordoba headed by Analytical Chemistry Professor Lourdes Arce, has been working on a solution to this issue since 2011. They now have come up with a new methodology that is based on analyzing the oil’s aromatic fraction -that is to say volatile organic compounds- as if it were the nose of a human taster. This is done by using gas chromatography and ion mobility spectrometry, which is a technique that separates ions when in the gas state.

This instrument generates 3D graphics of each volatile chemical compound in each sample of olive oil, resulting in a large number of data to process, making it difficult for companies to adopt this methodology.

To ease its implementation, the group studied two strategies for dealing with data: the first used spectral fingerprints (as in all the chemical information in each olive oil) and the second used a series of specific signals, 113 of over 200,000 chemical data that make up a spectral fingerprint.

During trials, the team analyzed 701 heterogeneous olive oil samples from different kinds of olives at different degrees of ripeness, from different geographical areas and that had been processed and stored in different ways. These samples were originally provided by the Spanish Olive Oil Interprofessional Organization in partnership with the Spanish Ministry of Agriculture, Food and the Environment along with the Andalusian Regional Government’s Department of Agriculture, Fishing and Rural Development.

Scientists found that their technique was efficiently predicting the classification of olive oil samples, in addition to being easier to implement within the industry than the strategy of using the whole spectral fingerprint.

In any case, the models should be recalibrated each year, and include new oil samples from the current season. The research group continues to work on this line of research in order to determine the minimum number of samples needed for recalibration without losing predictive ability to categorize olive oil.

The research was funded by the non-profit Spanish Olive Oil Interprofessional Organization (in Spanish Interprofesional del Aceite de Oliva Español). The study is published in the journal Food Chemistry.