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Advisor(s)
Abstract(s)
Olive oil is a highly appreciated food product being very prone to frauds. Olive oils may be
graded as extra-virgin, virgin or lampante. This classification is attributed according to legal
requirements, including chemical parameters and sensorial analysis. Among the
organoleptic sensations, the capability of perceiving the presence or absence of sensory
defects plays a key role for olive oils grade classification. This task is time-consuming and
quite expensive, requiring the use of an official taste panel, which can only evaluate a low
number of samples per day. In this work, an electronic tongue is proposed to discriminate
olive oils according to the defect predominantly perceived (winey-vinegary, wet-wood,
rancid and fusty/muddy sediment), by a trained sensory panel. Sub-sets of potentiometric
signal profiles obtained from the lipid sensor membranes of the taste electrochemical
device were selected using a simulated annealing meta-heuristic algorithm, allowing
establishing classification linear discriminant model, which showed a predictive success
classification rate of 81% for leave-one-out or cross-validation procedure. The satisfactory
predictive performance achieved pointed out the practical potential of using this artificial
taste sensor as a complementary methodology for olive oil sensory analysis.
Description
Keywords
Pedagogical Context
Citation
Silva, Lucas M.; Dias, L.G.; Rodrigues, Nuno; Veloso, Ana C.A.; Rebello, Ligia P.G.; Pereira, J.A.; Peres, António M. (2017). Classification of olive oils according to sensory defects using a potentiometric electronic tongue. In XVIII Simposio Científico-Técnico de Expoliva 2017. Jaén