Olive oil is highly appreciated due to its nutritional and organoleptic characteristics. However, a huge compositional variation is observed between olive oils, requiring the use of diverse analytical techniques for its classification including titration, spectrophotometry and chromatography, as well as sensory analysis. Chemical analysis is usually time-consuming, expensive and require skilled technicians, while the sensorial ones are dependent upon individual subjective evaluations, even if performed by trained panellists. This work evaluated and demonstrated the feasibility of using a potentiometric electronic tongue, comprising non-specific lipid polymeric and cross-sensitive sensor membranes, coupled with chemometric tools based on different sub-sets of sensors (from 11 to 14 sensors), to predict key quality parameters of olive oils based on single-run assays. The multivariate linear models established for 23 centenarian olive trees from different cultivars allowed predicting peroxide value, oxidative stability, total phenols and tocopherols contents, CIELAB scale parameters (L*, a* and b* values), as well as 11 gustatory-retronasal positive attributes (green, sweet, bitter, pungent, tomato and tomato leaves, apple, banana, cabbage, fresh herbs and dry fruits) with satisfactory accuracy (0.90 ± 0.07 ≤ R2 ≤ 0.98 ± 0.02 for the repeated K-fold-CV procedure, which ensured that 25% of the data was used for internal-validation purposes). The electronic tongue device had an accuracy statistically similar to that achieved with standard analytical techniques, pointing out the versatility of the device for the fast and simultaneous chemical and sensory analysis of olive oil.
This work was financially supported by Project POCI-01-0145-
FEDER-006984 – Associate Laboratory LSRE-LCM, Project UID/BIO/
04469/2013 – CEB, Project UID/QUI/50006/2013 - REQUIMTE-LAQV
and strategic project PEst-OE/AGR/UI0690/2014 – CIMO all funded by
European Regional Development Fund (ERDF) through COMPETE2020
– Programa Operacional Competitividade e Internacionalização (POCI)
– and by national funds through FCT – Fundação para a Ciência e a
Tecnologia I.P. Ítala G. Marx also acknowledges the research grant
provided by Project UID/EQU/50020/2013 and POCI-01-0145-FEDER-
006984.
