Preprint Communication Version 1 Preserved in Portico This version is not peer-reviewed

A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives

Claudio Fredes
* ,
Constantino Valero
ORCID logo ,
Belen Diezma
ORCID logo ,
Marco Mora
* ,
José Naranjo-Torres
ORCID logo ,
Manuel Wilson
,
Gabriel Delgadillo
Version 1 : Received: 15 December 2020 / Approved: 16 December 2020 / Online: 16 December 2020 (11:18:07 CET)

How to cite: Fredes, C.; Valero, C.; Diezma, B.; Mora, M.; Naranjo-Torres, J.; Wilson, M.; Delgadillo, G. A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives. Preprints 2020, 2020120405. https://doi.org/10.20944/preprints202012.0405.v1 Fredes, C.; Valero, C.; Diezma, B.; Mora, M.; Naranjo-Torres, J.; Wilson, M.; Delgadillo, G. A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives. Preprints 2020, 2020120405. https://doi.org/10.20944/preprints202012.0405.v1

Abstract

The color and NIR spectrum are key to build an oil estimation model, thus it requires individual olives clustering before the Sohlext oil extraction method can be applied. The objective was to analyze an OC estimation model of individual olives, based on cluster of similar color and NIR spectrum in different combination of the first and/or the second season. This study was performed with Chilean Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the 3 color channels of c1, c2, c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient amount of olives to be able to perform the Sohlext analysis of OC, as reference value. The estimation models were based on the Support Vector Machine. The test was carried out with the Leave One-Out Cross Validation in different training-testing combinations. The best model predicted the OC with 6% and 13%deviation respect to the real value in one season by itself and when one season tested with another season, respectively. The use of clustering in estimation model is discussed.

Keywords

infrared spectroscopy; visible image; support vector machine; olive quality.

Subject

Engineering, Automotive Engineering

Copyright: 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.

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