Non-destructive measurement of ripening index of 'Kent' mango using hyperspectral imaging techniques

Mango is a tropical fruit with high added value and has been interest by food industry in last years, which is increasing its efforts to determine the internal quality of this fruit by non-destructive techniques. In this study, 131 mangoes of 'Kent' variety were used and divided into three batches (unripe, ripe and overripe) and stored under controlled temperature and humidity. Mango images were taken by two sides with a hyperspectral systems based on two liquid crystal tunable filters (LCTF), sensitive in the spectral range 420-1080 nm. After images capturing, firmness, acidity and soluble solids content were analyzed by reference destructive techniques. A ripening index (RPI) was established with the combination of these properties. Hyperspectral images were analyzed by extracting the surface reflectance of both sides of mangoes. The data analysis was performed by two models of partial least squares regression (PLS) to establish the relationship between reflectance spectra of the surface of the mangoes and RPI obtained. 70% of the samples was used to build the model, obtaining a R-2 = 0,852 with all the bands for the calibration set. Later, the set was reduced to six wavelengths (520, 560, 730, 760, 870 and 1050 nm) with R-2 = 0,849. The model with six wavelengths were validated with 30% of the remaining samples to give a R-2 = 0,739 indicating that it is possible to predict the maturity of this variety of mango using a very limited set of wavelengths in the visible and near infrared.