Convolutional neural networks for olive oil classification
- Belén Vega-Márquez
- Andrea Carminati
- Natividad Jurado-Campos
- Andrés Martínez Gómez
- Lourdes Arce Jiménez
- Cristina Rubio-Escudero
- Isabel Nepomuceno-Chamorro
- José Manuel Ferrández Vicente (dir. congr.)
- José Ramón Álvarez-Sánchez (dir. congr.)
- Félix de la Paz López (dir. congr.)
- Javier Toledo Moreo (dir. congr.)
- Hojjat Adeli (coord.)
Editorial: Springer Suiza
ISBN: 978-3-030-19651-6
Año de publicación: 2019
Páginas: 137-145
Tipo: Capítulo de Libro
Resumen
The analysis of the quality of olive oil is a task that is havinga lot of impact nowadays due to the large frauds that have beenobserved 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 mobility spectrometry). The aim of this study is to show that Deep Learning techniques can be a great alternative to traditional oil classificationmethods based on the subjectivity of the standardized sensory analysis according to the panel test method, and also to novel techniques provided by the chemical field, such as chemometric markers. This technique 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 harvests 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 alternative to the panel test method, implying even better accuracy than results obtained in previous work