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Discrimination of astringent and deastringed hard "Rojo Brillante" persimmon fruit using a sensory threshold by means of hyperspectral imaging

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Discrimination of astringent and deastringed hard "Rojo Brillante" persimmon fruit using a sensory threshold by means of hyperspectral imaging

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dc.contributor.author Munera, Sandra es_ES
dc.contributor.author Aleixos Borrás, María Nuria es_ES
dc.contributor.author Besada, Cristina es_ES
dc.contributor.author Gómez-Sanchis, Juan es_ES
dc.contributor.author Salvador, Alejandra es_ES
dc.contributor.author Cubero, Sergio es_ES
dc.contributor.author Talens Oliag, Pau es_ES
dc.contributor.author Blasco, José es_ES
dc.date.accessioned 2021-01-12T21:03:08Z
dc.date.available 2021-01-12T21:03:08Z
dc.date.issued 2019-12 es_ES
dc.identifier.issn 0260-8774 es_ES
dc.identifier.uri http://hdl.handle.net/10251/158850
dc.description.abstract [EN] Persimmon fruit cv. 'Rojo Brillante' is an astringent cultivar due to its content of soluble tannins, which are insolubilised during the ripening of the fruit. Traditionally, the consumption of this cultivar has only been possible when the fruit is overripe and the texture is soft. Postharvest treatments based on exposing fruits to high CO2 concentrations allow astringency removal while preserving high flesh firmness. However, the effectiveness of this treatment is controlled by means of slow destructive methods. The aim of this work is to study the application of hyperspectral imaging in the spectral range 450-1040 nm to discriminate astringent (A) and deastringed (DA) fruits non-destructively. To separate both type of fruit, it was used a threshold of soluble tannins based on sensorial perception (0.04% of fresh weight). The spectral information from three different areas of each fruit (calyx, middle and apex) was used to build models to predict the soluble tannins (ST) content using partial least squares regression (PLS-R). The results using this method indicated that it was not possible to accurately discriminate fruit with levels of ST below 0.04%, especially in the case of DA fruits (42.2%). Thus, another classification models were performed using partial least squares discriminant analysis (PLS-DA) that included other properties in order to discriminate between A and DA using the ST threshold. The most accurate models using all and optimal wavelengths selected were those which focused on the middle and apex areas of the fruit, a correct classification rate of 87.0% being achieved for A fruits and above 84.4% for DA fruits. To date, there are only subjective and destructive analytical methods to monitor the effectiveness of the astringency removal treatments in persimmon. The results obtained in this study indicate that hyperspectral images can therefore be considered as an objective and non-destructive alternative in the control of this process. es_ES
dc.description.sponsorship This work was partially funded by INIA and FEDER funds through project RTA2015-00078-00-00. Sandra Munera thanks INIA for the FPI-INIA grant num. 43 (CPR2014-0082), partially supported by European Union FSE funds. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Journal of Food Engineering es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Diospyros kaki es_ES
dc.subject Astringency es_ES
dc.subject Soluble tannins es_ES
dc.subject Computer vision es_ES
dc.subject Chemometrics es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.subject.classification TECNOLOGIA DE ALIMENTOS es_ES
dc.title Discrimination of astringent and deastringed hard "Rojo Brillante" persimmon fruit using a sensory threshold by means of hyperspectral imaging es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.jfoodeng.2019.06.008 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/INIA//CPR2014-0082/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//2745/ES/Desarrollo de Tecnicas No Destructivas en Vision por Computador y Tecnologias de Automatización para la Clasificacion de Productos Hortofruticolas en Base a sus Propiedades Opticas y Mecanicas/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RTA2012-00062-C04-03/ES/Nuevas técnicas de inspección basadas en visión por computador multiespectral para la estimación de propiedades y determinación automática de la calidad y sanidad de la producción agroalimentaria en líneas de inspección y manipulación (VIS-DACSA)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//AICO%2F2015%2F122/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària es_ES
dc.description.bibliographicCitation Munera, S.; Aleixos Borrás, MN.; Besada, C.; Gómez-Sanchis, J.; Salvador, A.; Cubero, S.; Talens Oliag, P.... (2019). Discrimination of astringent and deastringed hard "Rojo Brillante" persimmon fruit using a sensory threshold by means of hyperspectral imaging. Journal of Food Engineering. 263:173-180. https://doi.org/10.1016/j.jfoodeng.2019.06.008 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.jfoodeng.2019.06.008 es_ES
dc.description.upvformatpinicio 173 es_ES
dc.description.upvformatpfin 180 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 263 es_ES
dc.relation.pasarela S\408771 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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