RT Journal Article
T1 Convolutional capture of the expansion of extra virgin olive oil droplets to  quantify adulteration
A1 Torrecilla Velasco, José Santiago
A1 Pradana López, Sandra
A1 Perez Calabuig, Ana M.
A1 Cancilla, John C.
A1 Garcia Rodriguez, Yolanda
AB This record corresponds to the peer-reviewed journal article “Convolutional capture of the expansion of extra virgin olive oil droplets to quantify adulteration”, published in Food Chemistry (Volume 368, 2022), a Journal Citation Reports (JCR) indexed journal.The work presents the development and validation of a computer vision–based methodology for the detection and quantification of extra virgin olive oil (EVOO) adulterations using convolutional neural networks (CNNs). The proposed approach is based on the analysis of the temporal expansion of EVOO droplets captured under controlled conditions, generating a large-scale image dataset comprising more than 302,000 images of pure and adulterated samples.Several deep learning models were designed and evaluated for (i) the classification of different EVOO types, (ii) the detection and quantification of adulterations with sunflower and corn oils at concentrations ranging from 2.5% to 10% (w/w), and (iii) a global CNN model integrating all EVOOs and adulteration scenarios. The optimized models achieved overall classification accuracies above 96%, demonstrating a high sensitivity to subtle physicochemical differences associated with oil composition.The results confirm that droplet image analysis combined with deep learning constitutes a rapid, non-destructive, and reliable tool for food quality control and fraud detection, with potential applicability throughout the olive oil distribution chain.
PB Elsevier
YR 2022
FD 2022-01-30
LK https://hdl.handle.net/20.500.14352/129966
UL https://hdl.handle.net/20.500.14352/129966
LA eng
NO Sandra Pradana-Lopez, Ana M. Perez-Calabuig, John C. Cancilla, Yolanda Garcia-Rodriguez, José S. Torrecilla, Convolutional capture of the expansion of extra virgin olive oil droplets to quantify adulteration, Food Chemistry, Volume 368, 2022, 130765, ISSN 0308-8146, https://doi.org/10.1016/j.foodchem.2021.130765. (https://www.sciencedirect.com/science/article/pii/S0308814621017714)
NO Universidad Complutense de Madrid
DS Docta Complutense
RD 19 ene 2026