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

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.

Automated summary

This study proposes classification models for predicting olive maturity index based on FT-NIR spectra. The reliability of the IA method was confirmed by significant correlation with visual evaluation. PLS-DA models developed can provide a fast, green, non-destructive sorting method for the olive sector.