Convolutional capture of the expansion of extra virgin olive oil droplets to quantify adulteration

Abstract

In this research, more than 302,000 images of five different types of extra virgin olive oils (EVOOs) have been collected to train and validate a system based on convolutional neural networks (CNNs) to carry out their classification. Furthermore, comparable deep learning models have also been trained to detect and quantify the adulteration of these EVOOs with other vegetable oils. In this work, three groups of CNN models have been tested for (i) the classification of all EVOOs, (ii) the detection and quantification of adulterated samples for each individual EVOO, and (iii) a global version of the previous models combining all EVOOs into a single quantifying CNN. This last model was successfully validated using 30,195 images that were initially isolated from the initial database. The result was an algorithm capable of detecting and accurately classifying the five types of EVOO and their respective adulteration concentrations with an overall hit rate of >96%. Therefore, EVOO droplet analyses via CNNs have proven to be a convincing quality control tool for the evaluation of EVOO, which can be carried by producers, distributors, or even final consumers, to help locate adulterations.

Keywords: Adulteration; Convolutional neural network; Drops; Extra virgin olive oil; Food quality; Images.