RT Journal Article
T1 Deep quantification of a refined adulterant blended into pure avocado oil
A1 Torrecilla Velasco, José Santiago
A1 Pérez Calabuig, Ana M.
A1 Pradana Lopez, Sandra
A1 Ramayo Muñoz, Andrea
A1 Cancilla, John C.
AB This record corresponds to the peer-reviewed journal article “Deep quantification of a refined adulterant blended into pure avocado oil”, published in Food Chemistry (Volume 404, 2023), a Journal Citation Reports (JCR) indexed journal.The work presents the development and validation of an innovative, non-destructive methodology for the detection and quantification of avocado oil adulteration with refined olive oil by combining optical image acquisition and deep learning techniques. A comprehensive image database comprising 1,800 photographs of pure and adulterated samples (1–15% v/v) was generated under controlled lighting conditions using different shutter speeds to simulate real-world inspection scenarios.Convolutional neural networks based on transfer learning (ResNet34 architecture) were trained and optimized to perform both qualitative and quantitative classification tasks. The proposed models achieved high classification accuracies (~95%) during blind validation, demonstrating strong robustness, sensitivity, and quantitative discrimination capability, even at low adulterant concentrations.Compared to conventional analytical techniques, the proposed approach offers a cost-effective, rapid, and non-invasive alternative that does not require complex sample preparation or specialized laboratory equipment. The methodology shows strong potential for in-situ quality control, food fraud detection, and real-time monitoring across different stages of the production and distribution chain.
PB Elsevier
YR 2023
FD 2023-03-15
LK https://hdl.handle.net/20.500.14352/129753
UL https://hdl.handle.net/20.500.14352/129753
LA eng
NO Ana M. Pérez-Calabuig, Sandra Pradana-López, Andrea Ramayo-Muñoz, John C. Cancilla, José S. Torrecilla, Deep quantification of a refined adulterant blended into pure avocado oil, Food Chemistry, Volume 404, Part A, 2023, 134474, ISSN 0308-8146, https://doi.org/10.1016/j.foodchem.2022.134474. (https://www.sciencedirect.com/science/article/pii/S0308814622024360)
NO Universidad Complutense de Madrid
DS Docta Complutense
RD 19 mar 2026