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Title: Postharvest ripeness assessment of `Hass' avocado based on development of a new ripening index and Vis-NIR spectroscopy
Authors: Melado-Herreros, Angela; Nieto-Ortega, Sonia; Olabarrieta, Idoia and Gutierrez, Monica; Villar, Alberto; Zufia, Jaime; Gorretta, Nathalie; Roger, Jean-Michel
Abstract: A 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.
Keywords: Dry matter content; Flesh firmness; Classification; Chemometrics; PLS regression; Non-destructive; FRUIT MATURITY; DRY-MATTER; PLS-REGRESSION; QUALITY; HETEROGENEITY; CALIBRATIONS; PREDICTION
Issue Date: 2021
Publisher: ELSEVIER
Type: Article
DOI: 10.1016/j.postharvbio.2021.111683
ISSN: 0925-5214
E-ISSN: 1873-2356
Funder: Basque Government - ELKARTEK 2017 ProgramBasque Government [KK-2017/00089]
Appears in Publication types:Artículos científicos

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