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dc.contributor.author | Munera, Sandra | es_ES |
dc.contributor.author | Rodríguez-Ortega, Alejandro | es_ES |
dc.contributor.author | Aleixos Borrás, María Nuria | es_ES |
dc.contributor.author | Cubero, Sergio | es_ES |
dc.contributor.author | Gómez-Sanchis, Juan | es_ES |
dc.contributor.author | Blasco, José | es_ES |
dc.date.accessioned | 2021-11-05T14:11:58Z | |
dc.date.available | 2021-11-05T14:11:58Z | |
dc.date.issued | 2021-09 | es_ES |
dc.identifier.issn | 2304-8158 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/176483 | |
dc.description.abstract | [EN] The main cause of flesh browning in 'Rojo Brillante' persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450-1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through an algorithm based on principal component analysis (PCA). The aim was to automatically separate intact and damaged fruit, and to detect the damage in the PC images when present. With this algorithm, 90.0% of intact fruit and 90.8% of damaged fruit were correctly detected. A model based on partial least squares-discriminant analysis (PLS-DA), was later calibrated using the mean spectrum of the pixels detected as damaged, to determine the moment when the fruit was damaged. The model differentiated fruit corresponding correctly to 0, 1, 2 and 3 days after damage induction, achieving a total accuracy of 99.4%. | es_ES |
dc.description.sponsorship | This work is co-funded by the projects AEI PID2019-107347RR-C31, PID2019-107347RR-C32, PID2019-107347RR-C33, IVIA-GVA 51918 and the European Union through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2014-2020. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Foods | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Diospyros kaki | es_ES |
dc.subject | Fruit quality | es_ES |
dc.subject | Browning | es_ES |
dc.subject | Nondestructive | es_ES |
dc.subject | Chemometrics | es_ES |
dc.subject | Computer vision | es_ES |
dc.subject.classification | EXPRESION GRAFICA EN LA INGENIERIA | es_ES |
dc.title | Detection of Invisible Damages in `Rojo Brillante¿ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/foods10092170 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107347RR-C31/ES/INSPECCION NO DESTRUCTIVA Y PREDICCION DE LA CALIDAD INTERNA Y PROPIEDADES DE LAS FRUTAS MEDIANTE ESPECTROSCOPIA VIS%2FNIR Y MODELOS BASADOS EN APRENDIZAJE PROFUNDO/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/IVIA//51918/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107347RR-C32/ES/INSPECCION Y PREDICCION NO DESTRUCTIVA DE CALIDAD INTERNA Y PROPIEDADES DE FRUTAS UTILIZANDO IMAGEN HIPERESPECTRAL VIS%2FNIR UTILIZANDO MODELOS BASADOS EN APRENDIZAJE PROFUNDO/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107347RR-C33/ES/ALGORITMOS DE INTELIGENCIA ARTIFICIAL BASADOS EN APRENDIZAJE PROFUNDO Y REDES GAN PARA EL ANALISIS DE DATOS ESPECTRALES EN PROBLEMAS DE INSPECCION DE FRUTA./ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica | es_ES |
dc.description.bibliographicCitation | Munera, S.; Rodríguez-Ortega, A.; Aleixos Borrás, MN.; Cubero, S.; Gómez-Sanchis, J.; Blasco, J. (2021). Detection of Invisible Damages in `Rojo Brillante¿ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics. Foods. 10(9):1-12. https://doi.org/10.3390/foods10092170 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/foods10092170 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 12 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 10 | es_ES |
dc.description.issue | 9 | es_ES |
dc.identifier.pmid | 34574280 | es_ES |
dc.identifier.pmcid | PMC8468948 | es_ES |
dc.relation.pasarela | S\445639 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Institut Valencià d'Investigacions Agràries | es_ES |