Assessment of eggplant freshness using non-destructive techniques

Eggplant fruit is a chilling injury sensitive vegetable, which should be stored at temperature of 12 degrees C; however, at this temperature, the metabolism of the fruit is still intensively active and therefore significant quality deterioration may be induced. Since these quality losses can be difficultly detected by eyes, objective of this study was to develop a novel non-destructive method to estimate freshness of eggplants. Eggplant fruits ('Fantasy') were harvested from a commercial farm in Lecce, Italy, during July 2017. Fruits were stored at 12 degrees C for 10 days. Every 2 days, fruits from were sampled and left at room temperature (20 degrees C), for one additional day, simulating one-day shelf life at the market. Color spectra (360-740 nm), Fourier Transform (FT)-NIR spectra (800-2777 nm) and hyperspectral images (HSI) in the Vis-NIR range (400-1000 nm) were also acquired on each fruit. Partial least square regression analyses were carried out between the data collected and the storage days and appropriate models were built, allowing safe assessment of the freshness of the fruits. According to the results based on whole wavelength ranges, storage days correlated very well with both the FT-NIR spectra and the hyperspectral data extracted from the Vis-NIR imaging system (RC>0.98, RCV>0.94, RMSEC<0.4 and RMSECV<0.8), in contrast to the color measurements with lower RC and RCV values and significantly high root means square errors (1.5 and 1.8, respectively). Moreover, after conducting SPA as a variable selection method, classification models could almost keep the same performance. The results of this study may set the basis to develop a protocol allowing a rapid screening and sorting of eggplants according to their postharvest freshness at distribution center or even upon the reception in the retail market.