Abstract
The analysis of the quality of olive oil is a task that is having 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 mobility spectrometry). The aim of this study is to show that Deep Learning techniques can be a great alternative to traditional oil classification methods 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.
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References
Chollet, F., et al.: Keras (2015). https://keras.io
Contreras, M.D.M., Jurado-Campos, N., Arce, L., Arroyo-Manzanares, N.: A robustness study of calibration models for olive oil classification: target and untargeted fingerprint approaches based on GC-IMS (2019, in press)
Debska, B., Guzowska-Świder, B.: Application of artificial neural network infood classification. Analytica Chimica Acta 705(1), 283–291 (2011). A selection of papers presented at the 12th International Conference on Chemometrics in Analytical Chemistry. https://doi.org/10.1016/j.aca.2011.06.033. http://www.sciencedirect.com/science/article/pii/S0003267011008622
EEC: European Commission Regulation (EEC). European Commission Regulation EEC/2568/91 of 11 July on the characteristics of olive and pomace oils and on their analytical methods. Off. J. Eur. Communities L248(640), 1–82 (1991)
Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018). https://doi.org/10.1016/j.compag.2018.01.009. http://www.sciencedirect.com/science/article/pii/S0168169917311742
Galili, E., Stanley, D.J., Sharvit, J., Weinstein-Evron, M.: Evidence for earliest olive-oil production in submerged settlements off the Carmel coast, Israel. J. Archaeol. Sci. 24(12), 1141–1150 (1997). https://doi.org/10.1006/jasc.1997.0193. http://www.sciencedirect.com/science/article/pii/S030544039790193X
Hussain, M., Bird, J.J., Faria, D.R.: A study on CNN transfer learning for image classification. In: Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C., McGinnity, M. (eds.) UKCI 2018. AISC, vol. 840, pp. 191–202. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-97982-3_16
Kanu, A.B., Hill, H.H.: Ion mobility spectrometry detection for gas chromatography. J. Chromatogr. A 1177(1), 12–27 (2008). https://doi.org/10.1016/j.chroma.2007.10.110
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Riul, A., et al.: Wine classification by taste sensors made from ultra-thin films and using neural networks. Sens. Actuators B: Chem. 98(1), 77–82 (2004). https://doi.org/10.1016/j.snb.2003.09.025. http://www.sciencedirect.com/science/article/pii/S0925400503007512
Soomro, N., Wang, M.: Superpixel segmentation: a benchmark. Sig. Process. Image Commun. 56 (2017). https://doi.org/10.1016/j.image.2017.04.007
Vega-Márquez, B., Nepomuceno-Chamorro, I., Jurado-Campos, N., Martín-Gómez, A., Arce, L., Rubio-Escudero, C.: Deep Learning Techniques to Improve the Performance of Olive Oil Classification (2019, in press)
Wu, J.: Introduction to convolutional neural networks. Technical report (2017). https://doi.org/10.1007/978-3-642-28661-2-5
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Vega-Márquez, B. et al. (2019). Convolutional Neural Networks for Olive Oil Classification. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_14
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