Prediction of dry matter content of recently harvested 'Hass' avocado fruits using hyperspectral imaging

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 anR(2)= 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. (c) 2020 Society of Chemical Industry

Automated summary

The study found no correlation between fruit weight and color with the percentage of DM. Cross-validation efficiency of different data sources was compared, and the best prediction set for DM percentage of fruits was selected using support vector machine regression with hyperspectral images. The results suggest that hyperspectral images can classify export fruits and assist in making harvesting decisions.