RT Journal Article
T1 Deep thermal imaging to compute the adulteration state of extra virgin olive oil
A1 Izquierdo, Manuel
A1 Lastra Mejías, Miguel
A1 González Flores, Ester
A1 Cancilla, John C.
A1 Aroca Santos, Regina
A1 Torrecilla Velasco, José Santiago
AB A new and reliable method to detect adulterations is presented in this work by comparing the thermal evolution of pure and adulterated extra virgin olive oil (EVOO) during its cooling process. For this purpose, algorithms based on convolutional neural networks have been tested for the analysis and classification of different thermographic images of pure and adulterated EVOO containing amounts ≤ 8% in weight of different adulterants (refined olive oil, olive pomace oil, and sunflower oil). Eight convolutional neural network models have been designed for the classification of EVOO samples and for the estimation of adulterant concentrations. Finally, other similar models were developed that included all the samples, containing any of the adulterants, to reach a global more versatile tool. The statistical performance of the convolutional neural networks in terms of classification accuracy range from 97 to 100%.
PB Elsevier
YR 2020
FD 2020-04-01
LK https://hdl.handle.net/20.500.14352/129845
UL https://hdl.handle.net/20.500.14352/129845
LA eng
NO anuel Izquierdo, Miguel Lastra-Mejías, Ester González-Flores, John C. Cancilla, Regina Aroca-Santos, José S. Torrecilla, Deep thermal imaging to compute the adulteration state of extra virgin olive oil, Computers and Electronics in Agriculture, Volume 171, 2020, 105290, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2020.105290.
NO Complutense University of Madrid
DS Docta Complutense
RD 30 mar 2026