Deep quantification of a refined adulterant blended into pure avocado oil

Abstract

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.