Convolutional Neural Networks for Olive Oil Classification

Abstract

The analysis of the quality of olive oil is a task that is hav-ing a lot of impact nowadays due to the large frauds that have been observed in the olive oil market. To solve this problem we have trained a Convolutional Neural Network (CNN) to classify 701 images obtained using GC-IMS methodology (gas chromatography coupled to ion mobil-ity spectrometry). The aim of this study is to show that Deep Learn-ing techniques can be a great alternative to traditional oil classification methods based on the subjectivity of the standardized sensory analy-sis according to the panel test method, and also to novel techniques provided by the chemical field, such as chemometric markers. This tech-nique is quite expensive since the markers are manually extracted by an expert. The analyzed data includes instances belonging to two different crops, the first covers the years 2014–2015 and the second 2015–2016. Both har-vests have instances classified in the three categories of existing oil, extra virgin olive oil (EVOO), virgin olive oil (VOO) and lampante olive oil (LOO). The aim of this study is to demonstrate that Deep Learning techniques in combination with chemical techniques are a good alterna-tive to the panel test method, implying even better accuracy than results obtained in previous work