Olive fruit ripening characterisation based on electrical capacitance measurements

dc.contributor.authorArgüello Morán, Daniel
dc.contributor.authorNoguera Manzano, Miguel
dc.contributor.authorMejías Borrero, Andrés Manuel
dc.contributor.authorEnrique Gómez, Juan Manuel
dc.contributor.authorAquino Martín, Arturo
dc.date.accessioned2024-12-18T09:12:11Z
dc.date.available2024-12-18T09:12:11Z
dc.date.issued2024-12
dc.description.abstractOlive fruit ripening involves the accumulation of fat content through lipogenesis. The completion of this complex process is considered a reliable indicator of the optimal time for harvesting. While chemical and magnetic resonance analyses, among other, can accurately determine fat content, these methods are costly and require specialized personnel, making them impractical for large-scale testing. Alternatively, visual grading methods are widely used, although recent studies have shown that the external appearance of fruits may not always reliably indicate ripeness. This paper investigates the influence of olive fruit maturation on their electrical behaviour, specifically on their ability to store electrical charge. To this end, a low-cost and portable field meter capable of measuring the electrical capacitance of olive fruits was designed and developed. Subsequently, 110 olive samples were measured weekly from September to harvest time in November. These samples were also subjected to chemical analysis for reference. The analysis of this data revealed high intra-sample variability, consistent with recent studies. Notably, strong correlations of up to 0,9741 emerged between capacitance measurements and gold-standard fat content on dry matter values after accounting for intra-sample variability. In this case, a Root- Mean-Square-Error value of 2,66% was calculated when using the regression line to estimate fat content on dry matter from capacitance values. Furthermore, no significant differences were observed between the distributions of the reference values and the estimated values. These findings pave the way for the development of an affordable tool to accurately assess the ripening stage directly in the field, and to expand our understanding about the ripening processes_ES
dc.description.departmentIngeniería Eléctrica y Térmica, de Diseño y Proyectoses_ES
dc.description.departmentIngeniería Electrónica, de Sistemas Informáticos y Automáticaes_ES
dc.description.sponsorshipThis work was supported by the Cooperation Program VI-A SPAIN- PORTUGAL (POCTEP) 2021–2027 and co-financed with ERDF [0067_OLIVAR_IA_5_E]; The Research Consolidation Programme 2022 of the Ministry of Science and Innovation (Spain) [CNS2022–136137]; and the Andalusian Plan for Research, Development and Innovation, PAIDI (Andalusia, Spain) [P18-RTJ-4539]es_ES
dc.identifier.citationArgüello, D., Noguera, M., Mejías, A., Enrique, J. M., & Aquino, A. (2024). Olive fruit ripening characterisation based on electrical capacitance measurements. In Smart Agricultural Technology (Vol. 9, p. 100696). Elsevier BV. https://doi.org/10.1016/j.atech.2024.100696es_ES
dc.identifier.doi10.1016/j.atech.2024.100696
dc.identifier.issn2772-3755
dc.identifier.urihttps://hdl.handle.net/10272/24691
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherOlive fruit ripeninges_ES
dc.subject.otherElectrical capacitancees_ES
dc.subject.otherOlive fat contentes_ES
dc.subject.otherIntra-sample variabilityes_ES
dc.subject.otherElectrical modellinges_ES
dc.subject.unesco33 Ciencias Tecnológicases_ES
dc.titleOlive fruit ripening characterisation based on electrical capacitance measurementses_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication72059c89-9878-4eba-9628-944f97fcf5e6
relation.isAuthorOfPublicationed01cb9f-89ca-44ad-a5f3-46a279254b77
relation.isAuthorOfPublication6ec526cb-3be1-4fd9-ab95-70469255e9a7
relation.isAuthorOfPublication.latestForDiscovery72059c89-9878-4eba-9628-944f97fcf5e6

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