dc.contributor.author | Ezenarro, Jokin | |
dc.contributor.author | García Pizarro, Angel | |
dc.contributor.author | Busto, Olga | |
dc.contributor.author | de Juan, Anna | |
dc.contributor.author | Boqué, Ricard | |
dc.contributor.other | Producció Vegetal | ca |
dc.date.accessioned | 2024-04-05T09:52:53Z | |
dc.date.available | 2024-04-05T09:52:53Z | |
dc.date.issued | 2023-10-09 | |
dc.identifier.citation | Ezenarro, Jokin, Ángel García-Pizarro, Olga Busto, Anna De Juan, and Ricard Boqué. 2023. “Analysing Olive Ripening With Digital Image RGB Histograms.” Analytica Chimica Acta 1280: 341884. doi:10.1016/j.aca.2023.341884. | ca |
dc.identifier.issn | 0003-2670 | ca |
dc.identifier.uri | http://hdl.handle.net/20.500.12327/2907 | |
dc.description.abstract | Digital images are commonly used to monitor processes that are based on colour changes due to their simplicity
and easy capture. Colour information in these images can be analysed objectively and accurately using colour
histograms. One such process is olive ripening, which is characterized by changes in chemical composition,
sensory properties and can be followed by changes in physical appearance, mainly colour. The reference method
to quantify the ripeness of olives is the Maturity Index (MI), which is determined by trained experts assigning
individual olives into a colour scale through visual inspection. Instead, this study proposes a methodology based
on Chemometrics Assisted Colour Histogram-based Analytical Systems (CACHAS) to automatically assess the MI
of olives based on R, G, and B colour histograms derived from digital images. The methodology was shown to be
easily transferable for routine analysis and capable of controlling the ripening of olives. The study also confirms
the high potential of digital images to understand the ripening process of olives (and potentially other fruits) and
to predict the MI with satisfactory accuracy, providing an objective and reproducible alternative to visual inspection of trained experts. | ca |
dc.description.sponsorship | Grant PID2019-104269RR-C33 funded by MCI/AEI/10.13039/ 501100011033. Grant URV Martí i Franqu´ es –Banco Santander (2021PMF-BS-12). Grant URV-IRTA Martí i Franqu´ es (2020PMF-PIPF- 6). A.J. acknowledges funding from grant PID2019-1071586B-IOO. | ca |
dc.format.extent | 8 | ca |
dc.language.iso | eng | ca |
dc.publisher | Elsevier | ca |
dc.relation.ispartof | Analytica Chimica Acta | ca |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Analysing olive ripening with digital image RGB histograms | ca |
dc.type | info:eu-repo/semantics/article | ca |
dc.description.version | info:eu-repo/semantics/publishedVersion | ca |
dc.rights.accessLevel | info:eu-repo/semantics/openAccess | |
dc.embargo.terms | cap | ca |
dc.relation.projectID | MICINN/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I/PID2019-104269RR-C33/ES/Productos innovadores a base de frutas y uva para aumentar el consumo de frutas, promover la salud y reducir los residuos de alimentos/ALLFRUIT4ALL | ca |
dc.relation.projectID | MICINN/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I/PID2019-107158GB-I00/ES/Superando límites en la fusión de datos: hacia la descripción integral de imágenes hiperespectrales y procesos bioanalíticos e industriales/ | ca |
dc.subject.udc | 633 | ca |
dc.subject.udc | 663/664 | ca |
dc.identifier.doi | https://doi.org/10.1016/j.aca.2023.341884 | ca |
dc.contributor.group | Fructicultura | ca |