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dc.creatorPeraza Alemán, Carlos Migueles_ES
dc.creatorLópez Maestresalas, Ainaraes_ES
dc.creatorJarén Ceballos, Carmenes_ES
dc.creatorRubio Padilla, Niutones_ES
dc.creatorArazuri Garín, Silviaes_ES
dc.date.accessioned2024-02-23T17:34:55Z
dc.date.available2024-02-23T17:34:55Z
dc.date.issued2024
dc.identifier.citationPeraza-Alemán, C. M., López-Maestresalas, A., Jarén, C., Rubio-Padilla, N., Arazuri, S. (2024) A systematized review on the applications of hyperspectral imaging for quality control of potatoes. Potato Research, 1-23. https://doi.org/10.1007/s11540-024-09702-7.en
dc.identifier.issn0014-3065
dc.identifier.urihttps://hdl.handle.net/2454/47557
dc.description.abstractThe application of hyperspectral imaging (HSI) has gained signifcant importance in the past decade, particulary in the context of food analysis, including potatoes. However, the current literature lacks a comprehensive systematic review of the application of this technique in potato cultivation. Therefore, the aim of this work was to conduct a systematized review by analysing the most relevant compounds, diseases and stress factors in potatoes using hyperspectral imaging. For this purpose, scientifc studies were retrieved through a systematic keyword search in Web of Science and Scopus databases. Studies were only included in the review if they provided at least one set of quantitative data. As a result, a total of 52 unique studies were included in the review. Eligible studies were assigned an in-house developed quality scale identifying them as high, medium or low risk. In most cases the studies were rated as low risk. Finally, a comprehensive overview of the HSI applications in potatoes was performed. It has been observed that most of the selected studies obtained better results using linear methods. In addition, a meta-analysis of studies based on regression and classifcation was attempted but was not possible as not enough studies were found for a specifc variable.en
dc.description.sponsorshipThe funding of this work has been covered by the Ministry of Science and Innovation (Spain) project: PID2019-109790RR-C22 and the predoctoral grant (PRE2020-094533) associated to it. The Open Access funding was provided by Universidad Pública de Navarra.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherSpringeren
dc.relation.ispartofPotato Research 2024, 1-23en
dc.rightsCreative Commons BY-NC-ND 4.0 Attribution-NonCommercial-NoDerivs 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectFood quality controlen
dc.subjectHyperspectral imagingen
dc.subjectMachine learningen
dc.subjectNondestructive techniquesen
dc.subjectPotatoesen
dc.subjectSolanum tuberosum L.en
dc.subjectSystematized reviewen
dc.titleA systematized review on the applications of hyperspectral imaging for quality control of potatoesen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.date.updated2024-02-23T16:52:59Z
dc.contributor.departmentIngenieríaes_ES
dc.contributor.departmentIngeniaritzaeu
dc.contributor.departmentInstitute on Innovation and Sustainable Development in Food Chain - ISFOODen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.1007/s11540-024-09702-7
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109790RR-C22/ES/en
dc.relation.publisherversionhttps://doi.org/10.1007/s11540-024-09702-7
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoaes


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Creative Commons BY-NC-ND 4.0 Attribution-NonCommercial-NoDerivs 4.0 International
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El Repositorio ha recibido la ayuda de la Fundación Española para la Ciencia y la Tecnología para la realización de actividades en el ámbito del fomento de la investigación científica de excelencia, en la Línea 2. Repositorios institucionales (convocatoria 2020-2021).
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