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dc.contributor.authorCortes, Victoria
dc.contributor.authorRodriguez, Alejandro
dc.contributor.authorBlasco, José
dc.contributor.authorRey, Beatriz
dc.contributor.authorBesada, Cristina
dc.contributor.authorCubero, Sergio
dc.contributor.authorSalvador, Alejandra
dc.contributor.authorTalens, Pau
dc.contributor.authorAleixos, Nuria
dc.date.accessioned2017-08-04T13:56:51Z
dc.date.available2017-08-04T13:56:51Z
dc.date.issued2017es
dc.identifier.citationCortés V, Rodríguez A, Blasco J, Rey B, Besada C, Cubero S, Salvador A, Talens P, Aleixos N (2017). Prediction of the level of astringency in persimmon using visible and near-infrared spectroscopy. Journal of Food Engineering, 204, 27-37.es
dc.identifier.urihttp://hdl.handle.net/20.500.11939/5727
dc.description.abstractEarly control of fruit quality requires reliable and rapid determination techniques. Therefore, the food industry has a growing interest in non-destructive methods such as spectroscopy. The aim of this study was to evaluate the feasibility of visible and near-infrared (NIR) spectroscopy, in combination with multivariate analysis techniques, to predict the level and changes of astringency in intact and in the flesh of half cut persimmon fruits. The fruits were harvested and exposed to different treatments with 95 % CO2 at 20 ºC for 0, 6, 12, 18 and 24 h to obtain samples with different levels of astringency. A set of 98 fruits was used to develop the predictive models based on their spectral data and another external set of 42 fruit samples was used to validate the models. The models were created using the partial least squares regression (PLSR), support vector machine (SVM) and least squares support vector machine (LS-SVM). In general, the models with the best performance were those which included standard normal variate (SNV) in the pre-processing. The best model was the PLSR developed with SNV along with the first derivative (1-Der) pre-processing, created using the data obtained at six measurement points of the intact fruits and all wavelengths (R2=0.904 and RPD=3.26). Later, a successive projection algorithm (SPA) was applied to select the most effective wavelengths (EWs). Using the six points of measurement of the intact fruit and SNV together with the direct orthogonal signal correction (DOSC) pre-processing in the NIR spectra, 41 EWs were selected, achieving an R2 of 0.915 and an RPD of 3.46 for the PLSR model. These results suggest that this technology has potential for use as a feasible and cost-effective method for the non-destructive determination of astringency in persimmon fruits.es
dc.language.isoenes
dc.publisherElsevieres
dc.subjectDiospyros kaki, fruit internal quality, soluble tannins, near-infrared spectroscopy, chemometricses
dc.titlePrediction of the level of astringency in persimmon using visible and near-infrared spectroscopyes
dc.entidadIVIACentro de Agroingenieríaes
dc.identifier.doi10.1016/j.jfoodeng.2017.02.017es
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/S0260877417300626es
dc.journal.issueNumber204es
dc.journal.titleJournal of Food Engineeringes
dc.page.final37es
dc.page.initial27es
dc.relation.projectIDINIA RTA2012-00062-C04-01/03es
dc.relation.projectIDINIA RTA2015-00078-00-00es
dc.source.typeelectronicoes


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