Convolutional neural networks for olive oil classification
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Autores: Belén Vega Márquez, Andrés Carminati, Natividad Jurado Campos, Andrés Martínez, Lourdes Arce Jiménez
, Cristina Rubio Escudero , Isabel de los Ángeles Nepomuceno Chamorro
- Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.) , José Ramón Álvarez Sánchez (dir. congr.) , Félix de la Paz López (dir. congr.) , Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 137-145
- Idioma: inglés
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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
