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Prediction of dry matter content of recently harvested 'Hass' avocado fruits using hyperspectral imaging.
Vega Díaz, Jhon Jairo; Sandoval Aldana, Angélica Piedad; Reina Zuluaga, Deici Viviana.
Afiliação
  • Vega Díaz JJ; Doctorado en ciencia aplicada, Universidad Antonio Nariño, Bogotá, Colombia.
  • Sandoval Aldana AP; Facultad de ingeniería agronómica, Universidad del Tolima, Ibagué, Colombia.
  • Reina Zuluaga DV; Facultad de ingeniería agronómica, Universidad del Tolima, Ibagué, Colombia.
J Sci Food Agric ; 101(3): 897-906, 2021 Feb.
Article em En | MEDLINE | ID: mdl-32737875
BACKGROUND: 'Hass' avocado consumption is increasing due to its organoleptic properties, so it is necessary to develop new technologies to guarantee export quality. Avocado fruits do not ripen on the tree, and the visual classification of its maturity is not accurate. The most commonly used fruit maturity indicator is the percentage of dry matter (DM). The aim of this research was to investigate a non-destructive method with hyperspectral images to predict the percentage of DM of fruits across the spectral range of 400-1000 nm. RESULTS: No correlation between fruit weight and color with the percentage of DM was found in the study area. Cross-validation efficiency of different data sources, including the spectrum extraction zone (the center, a line from the peduncle to the base, and the whole fruit) and the average of one or two fruit faces, was compared. Four linear regression models were compared. Data of the whole fruit and average of both sides per fruit using a support vector machine regression were selected for the prediction test. Following the cross-validation concept, five sets of calibration and test data were selected and optimized for calibration. The best test prediction set comprised an R2 = 0.9, a root-mean-square error of 2.6 g kg-1 DM, a Pearson correlation of 0.95, and a ratio of prediction to deviation of 3.2. CONCLUSIONS: The results of the study indicate that hyperspectral images allow classifying export fruits and making harvesting decisions. © 2020 Society of Chemical Industry.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Persea / Frutas / Imageamento Hiperespectral Tipo de estudo: Evaluation_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Sci Food Agric Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Colômbia País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Persea / Frutas / Imageamento Hiperespectral Tipo de estudo: Evaluation_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Sci Food Agric Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Colômbia País de publicação: Reino Unido