This work proposes classification models for the prediction of olive maturity index based on Fourier Transform-Near Infrared (FT-NIR) spectra of intact drupes. An image analysis (IA) method was purposely developed for the objective evaluation of the maturity index. Thirteen cultivars at different ripening stages were harvested along three years. The reliability of the IA method was confirmed by the highly significant correlation with the common visual evaluation of maturity index. Classification models were developed with Partial Least Square-Discriminant Analysis (PLS-DA), using IA results and FT-NIR spectra of olives collected in diffuse reflectance. Most PLS-DA models calculated separately for olive origin gave sensitivity and specificity values in prediction higher than 81%. The global model performed slightly worse (sensitivity, 79%; specificity, 75%), but it is definitely more robust and can provide the olive sector with a fast, green and non-destructive olive sorting method for the production of high quality virgin oil.

Prediction of olive ripening degree combining image analysis and FT-NIR spectroscopy for virgin olive oil optimisation / C. Alamprese, S. Grassi, A. Tugnolo, E. Casiraghi. - In: FOOD CONTROL. - ISSN 0956-7135. - 123(2021 May). [10.1016/j.foodcont.2020.107755]

Prediction of olive ripening degree combining image analysis and FT-NIR spectroscopy for virgin olive oil optimisation

C. Alamprese
Primo
;
S. Grassi
Secondo
;
A. Tugnolo
Penultimo
;
E. Casiraghi
Ultimo
2021

Abstract

This work proposes classification models for the prediction of olive maturity index based on Fourier Transform-Near Infrared (FT-NIR) spectra of intact drupes. An image analysis (IA) method was purposely developed for the objective evaluation of the maturity index. Thirteen cultivars at different ripening stages were harvested along three years. The reliability of the IA method was confirmed by the highly significant correlation with the common visual evaluation of maturity index. Classification models were developed with Partial Least Square-Discriminant Analysis (PLS-DA), using IA results and FT-NIR spectra of olives collected in diffuse reflectance. Most PLS-DA models calculated separately for olive origin gave sensitivity and specificity values in prediction higher than 81%. The global model performed slightly worse (sensitivity, 79%; specificity, 75%), but it is definitely more robust and can provide the olive sector with a fast, green and non-destructive olive sorting method for the production of high quality virgin oil.
olive quality; maturity index; intact fruit; image analysis; near infrared spectroscopy.
Settore AGR/15 - Scienze e Tecnologie Alimentari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/828910
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