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dc.contributor.authorMelado-Herreros, Angela-
dc.contributor.authorNieto-Ortega, Sonia-
dc.contributor.authorOlabarrieta, Idoia-
dc.contributor.authorGutierrez, Monica-
dc.contributor.authorVillar, Alberto-
dc.contributor.authorZufia, Jaime-
dc.contributor.authorGorretta, Nathalie; Roger, Jean-Michel-
dc.date.accessioned2022-01-04T11:31:09Z-
dc.date.available2022-01-04T11:31:09Z-
dc.date.issued2021-
dc.identifierWOS:000692237700005-
dc.identifier.issn0925-5214-
dc.identifier.urihttp://dspace.azti.es/handle/24689/1196-
dc.description.abstractA classification model using Vis-NIR spectroscopy (380-2000 nm) coupled with partial least square discriminant analysis (PLS-DA) was developed to segregate avocados in three classes, predefined by a warehouse using destructive FF tests performed on a small number of samples. This classification showed a satisfactory general accuracy of 62 \%, with 100 \% well-classified samples in Class 1, 20 \% in Class 2 and 65 \% in Class 3. To improve classification, a ripening index (RI) was developed, which combines FF and DMC. The discrimination ability in the three classes was tested using Wilk's lambda, calculated as between-class variance to the total variance ratio. Results showed values of 0.648 for RI, 0.516 for FF and 0.038 for DMC. A regression model was subsequently developed using Partial Least Squares (PLS) regression to predict RI using Vis-NIR spectroscopy in an independent dataset. The PLS results were satisfactory with the whole spectrum wavelength range (380-2000 nm), with R-2 of 0.62, SEP of 0.69 (), but presented a large bias value of 1.22 (). The same occurs in models developed in the wavelength range from 400 to 1100 nm, with R-2 of 0.63, SEP of 0.68 () and bias of 1.03 (). This could be corrected using a bias and slope correction algorithm. Study of the correlation coefficients of the PLS regression models showed that the region 400-1100 nm has a huge influence in the model, which indicates the potential of using cost-effective short Vis-NIR spectrophotometers for RI prediction.-
dc.language.isoEnglish-
dc.publisherELSEVIER-
dc.subjectDry matter content-
dc.subjectFlesh firmness-
dc.subjectClassification-
dc.subjectChemometrics-
dc.subjectPLS regression-
dc.subjectNon-destructive-
dc.subjectFRUIT MATURITY-
dc.subjectDRY-MATTER-
dc.subjectPLS-REGRESSION-
dc.subjectQUALITY-
dc.subjectHETEROGENEITY-
dc.subjectCALIBRATIONS-
dc.subjectPREDICTION-
dc.titlePostharvest ripeness assessment of `Hass' avocado based on development of a new ripening index and Vis-NIR spectroscopy-
dc.typeArticle-
dc.identifier.journalPOSTHARVEST BIOLOGY AND TECHNOLOGY-
dc.format.volume181-
dc.contributor.funderBasque Government - ELKARTEK 2017 ProgramBasque Government [KK-2017/00089]-
dc.identifier.e-issn1873-2356-
dc.identifier.doi10.1016/j.postharvbio.2021.111683-
Aparece en las tipos de publicación: Artículos científicos



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