Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/2088
Title: Prediction of Fruity Aroma Intensity and Defect Presence in Virgin Olive Oil Using an Electronic Nose
Authors: Cano Marchal, Pablo
Sanmartin, Chiara
Satorres Martínez, Silvia
Gómez Ortega, Juan
Mencarelli, Fabio
Gámez García, Javier
Abstract: 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.
Keywords: virgin olive oil
quality
electronic nose
Issue Date: 25-Mar-2021
Publisher: MDPI
Citation: Cano Marchal, P.; Sanmartin, C.; Satorres Martínez, S.; Gómez Ortega, J.; Mencarelli, F.; Gámez García, J. Prediction of Fruity Aroma Intensity and Defect Presence in Virgin Olive Oil Using an Electronic Nose. Sensors 2021, 21, 2298. https://doi.org/10.3390/s21072298
Appears in Collections:DIEA-Artículos

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