Prediction of Fruity Aroma Intensity and Defect Presence in Virgin Olive Oil Using an Electronic Nose

The organoleptic profile of a Virgin Olive Oil is a key quality parameter that is currently obtained by human sensory panels. The development of an instrumental technique capable of providing information about this profile quickly and online is of great interest. This work employed a general purpose e-nose, in lab conditions, to predict the level of fruity aroma and the presence of defects in Virgin Olive Oils. The raw data provided by the e-nose were used to extract a set of features that fed a regressor to predict the level of fruity aroma and a classifier to detect the presence of defects. The results obtained were a mean validation error of 0.5 units for the prediction of fruity aroma using lasso regression; and 88% accuracy for the defect detection using logistic regression. Finally, the identification of two out of ten specific sensors of the e-nose that can provide successful results paves the way to the design of low-cost specific electronic noses for this application.

Automated summary

This research employed a general purpose e-nose to predict the level of fruity aroma and the presence of defects in Virgin Olive Oils, achieving a mean validation error of 0.5 units for the prediction of fruity aroma and 88% accuracy for defect detection. Two out of ten specific sensors of the e-nose were identified to provide successful results, paving the way for the design of low-cost specific electronic noses for this application.