Improved prediction of ‘Kent’ mango firmness during ripening by near-infrared spectroscopy supported by interval partial least square regression

Abstract

Mangoes (Mangifera indica L.) are tropical fruits, which are sourced worldwide to supply the consumer market in Europe. Often mangoes are transported over sea in refrigerated containers at 8–10 °C and in some cases under controlled atmosphere conditions. At arrival in European countries, a vast amount of fruit is ripened under specified conditions to deliver 'Ready to Eat' fruit to consumers. The latter is a challenge due to great variability in fruit maturity stage at arrival. There are currently no good methodologies to rapidly and nondestructively monitor and control the ripening process of mangoes. A major indicator of mango ripeness is fruit firmness. In the present study, a portable visible near-infrared (400–1130 nm) (VNIR) spectrometer was used to predict the firmness of individual mango undergoing ripening. Ripening of ‘Kent’ mango was for 10 days monitored at 20 °C and relative humidity (RH) of 85%. Every other day fruit firmness (measured with AWETA acoustic firmness analyzer) and NIR spectrum were determined on 2 opposite sides of the fruit. Interval partial-least square (iPLSR) regression was used for identifying the important wavelengths responsible for predicting firmness in mangoes. Results showed a change in the VNIR spectra with the change in firmness of mangoes. The model based on selected wavelengths performed significantly better compared to PLSR without pre-selecting wavelengths. iPLSR based regression provided a correlation of calibration and prediction as R2c = 0.75 and R2p = 0.75, and root means squared error of calibration and prediction as 6.02 Hz2g2/3 and 5.92 Hz2g2/3 respectively. The iPLSR model outperformed the standard PLSR model by over 12% in R2p and 14% reduction in prediction error. The predictions by the model provided an evolution of the firmness during the complete ripening experiment. Non-destructive access to mango firmness during ripening can assist in optimizing the process to better meet the market demand.