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Classification of olive fruits and oils based on their fatty acid ethyl esters content using electronic nose technology

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Abstract

Among several parameters defined for the commercial classes of virgins olive oils (VOOs), there is one, the fatty acid ethyl ester (FAEE), that is only define for the best quality (EVOO). Fruit condition mainly determine these compounds, although, extraction process or deplorable storage condition could rise them up. The FAEE oxidation compound are originated by adding an alcohol chain into the oil molecule. Therefore, the hypothesis of this study is that the inherent constitution of FAEE entails a modification of the volatile profile of oils and olives and this is significant enough to be detected using an electronic nose. With this aim, different samples of olives and oils were analyzed in an accredited laboratory. On the other hand, volatiles from the same samples were captured by an electronic nose. The classification problem was analyzed from two points of view or models. The first was to classify fruits and oils based on whether they are within or outside the legal limits. And the second problem was oriented to classify fruits and oils based on their high or low FAEE content but being within the legal limits. To solve this problem, three classification algorithms were evaluated: Naïve Bayes (NB), Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO). For the first model, a well-classified sample rate of 80.3% was obtained for NB and 100% for SMO and MLP, for measurements on oils. The same model evaluated with measurements on olives yielded a success rate of 87.5% with NB, 87.7% with MLP and 82.1% with SMO. For the second model, the success rates remained within the same orders of magnitude. For measurements on oils, the results were 89.7% for NB, 92.5% for MLP and 100% for SMO. And for measurements on olives the results were 77.9% for NB, 88.6% for MLP and 90.9% for SMO. In all cases, the characteristics that worked best were those obtained from the first derivative of the electronic nose response. Based on these results, the e-nose demonstrate to be a non-invasive technology suitable for the classification of olive fruits and oils based on their FAEE content.

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Acknowledgements

This work has been partially supported by the project of the Ministry of Spain with reference PID2019-110291RB-I00. The authors thank the oil mill Picualia (www. piculia.com) for the olive samples provided to carry out this study. Also we are very thankful for the financial support provided by the Spanish Ministry of Economy and Competitiveness (Precommercial public procurement Innolivar) Exp. 2018/00001, co-funded by European FEDER funds, and the financial support provided by the Interprofessional Organization of Table Olive and Olive Oil, Spain https://interaceituna.com/.

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  1. Robotics, Automation and Computer Vision Group, University of Jaen, Campus Las Lagunillas s/n, 23071, Jaen, Spain

    Diego M. Martínez Gila, Javiera Navarro Soto, Juan Gómez Ortega & Javier Gámez García

  2. Dipartimento di Scienze Agrarie, Alimentari e Agroambientali, Università di Pisa, Via S.Michele degli Scalzi 4, Pisa, Italy

    Chiara Sanmartin & Fabio Mencarelli

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Martínez Gila, D.M., Sanmartin, C., Navarro Soto, J. et al. Classification of olive fruits and oils based on their fatty acid ethyl esters content using electronic nose technology. Food Measure 15, 5427–5438 (2021). https://doi.org/10.1007/s11694-021-01103-5

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