Augmented smelling to reveal Brazilian Extra Virgin Olive Oil aroma blueprint: accurate quantification of key-aroma compounds by comprehensive two-dimensional gas chromatography and parallel detection by MS and FID

Extra virgin olive oil (EVOO) is a valuable food commodity that is widely consumed worldwide. The aroma of extra virgin olive oil is affected by various factors such as the type of olive tree, the conditions in which the trees are grown, the stage at which the olives are harvested, and the method of oil extraction [1]. Despite being the third-largest importer of olive oil in the world, Brazil's production of olives and oil is relatively new and small compared to its domestic market's size. To assess characteristic patterns of odorants for different cultivars (Arbequina and Koroneiki), different harvest years (2021 and 2022), and production regions (Rio Grande do Sul and Serra da Mantiqueira), comprehensive two-dimensional gas chromatography coupled with parallel detectors (MS and FID) was chosen. In fact, GC×GC-MS/FID leads to a high-performance analysis strategy capable of fully exploit the information encrypted on the volatile fraction including also those key- analytes responsible of the EVOOs aroma blueprint. Moreover, the complementary characteristics of MS and FID open the possibility of performing multi-target quantitative profiling by predicted relative response factors with great accuracy [2]. Untargeted/targeted fingerprinting workflow was carried out combining template matching strategies on the 2D-patterns of volatiles. Quantification of target volatiles was achieved via Multiple Headspace SPME, external standard calibration and FID predicted relative response factors (PRRF). The combination of HS-SPME with GC×GC-MS/FID and PRRF resulted to be a great tool in the quality assessment of EVOO samples. By effective exploration of the information encrypted in EVOOs volatilome, the impact of functional variables is reliably correlated to diagnostic patterns with great classification and identitation attitudes [3]. By the accurate quantification of key- odorants, an Artificial Intelligence smelling machine is realized, an Augmented smelling with unique comparative possibilities for EVOOs aroma qualities.