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Sensors 2018, 18(11), 3826; https://doi.org/10.3390/s18113826

Fast and Reliable Determination of Virgin Olive Oil Quality by Fruit Inspection Using Computer Vision

Robotics, Automation and Computer Vision Group, Electronic and Automation Engineering Department, University of Jaen, ES-23071 Jaen, Spain
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Received: 18 September 2018 / Revised: 25 October 2018 / Accepted: 5 November 2018 / Published: 8 November 2018
(This article belongs to the Section Intelligent Sensors)
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Abstract

The presence of minor compounds in virgin olive oils has been proven to play multiple positive roles in health protection, encouraging its production. The key factors that influence the oil quality are ripening stages and the state of health of the fruit. For this reason, at the oil mill’s reception yard, fruits are visually inspected and separated according to their external appearance. In this way, the process parameters can be better adjusted to improve the quantity and/or quality of olive oil. This paper presents a proposal to automatically determine the oil quality before being produced from a previous inspection of the incoming fruits. Expert assessment of the fruit conditions guided the image processing. The proposal has been validated through the analysis of 74 batches of olives coming from an oil mill. Best correlation results between the image processing and the analytical data were found in the acidity index, peroxide values, ethyl ester, polyphenols, chlorophylls, and carotenoids. View Full-Text
Keywords: olive; fruit; olive oil; computer vision; olive oil production process olive; fruit; olive oil; computer vision; olive oil production process
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Navarro Soto, J.; Satorres Martínez, S.; Martínez Gila, D.; Gómez Ortega, J.; Gámez García, J. Fast and Reliable Determination of Virgin Olive Oil Quality by Fruit Inspection Using Computer Vision. Sensors 2018, 18, 3826.

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