RT Journal Article
T1 Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition
A1 Sayago Gómez, Ana
A1 González Domínguez, Raúl
A1 Beltrán Lucena, Rafael
A1 Fernández Recamales, María Ángeles
AB This work explores the potential of multi-element fingerprinting in combination with advanced data mining strategies to assess the geographical origin of extra virgin olive oil samples. For this purpose, the concentrations of 55 elements were determined in 125 oil samples from multiple Spanish geographic areas. Several unsupervised and supervised multivariate statistical techniques were used to build classification models and investigate the relationship between the mineral composition of olive oils and their provenance. Results showed that Spanish extra virgin olive oils exhibit characteristic element profiles, which can be differentiated based on their origin by three geographical areas: the Atlantic coast (Huelva province), the Mediterranean coast, and inland regions. Furthermore, statistical modeling yielded high sensitivity and specificity, principally when random forest and support vector machines were employed, thus demonstrating the utility of these techniques in food traceability and authenticity research.
PB Elsevier
SN 0308-8146
YR 2018
FD 2018-04
LK https://hdl.handle.net/10272/23021
UL https://hdl.handle.net/10272/23021
LA eng
NO Sayago, A., González-Domínguez, R., Beltrán, R., & Fernández-Recamales, Á. (2018). Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition. In Food Chemistry (Vol. 261, pp. 42–50). Elsevier BV. https://doi.org/10.1016/j.foodchem.2018.04.019
NO This work was supported by the Consejería de Innovación, Ciencia y Empresa, Junta de Andalucía (grant number P10-FQM-6185). Authors also thank to Prof. Jesús de la Rosa for his selfless assistance in the interpretation of results.
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026