Kiwifruit flesh firmness determination by a NIR sensitive device and image multivariate data analyses

A prototype based on a NIR sensitive camera and a Xenon lamp was set up and used to capture 8 bit gray scale (from 0 = black to 255 = white) image of the radiation that passes through the fruit. The count of the pixels with different gray tone was used to build statistical-mathematical models to correlate and predict the kiwifruit flesh firmness. One hundred sixteen fruits conveniently stored to obtain firmness within a range of penetrometric force from 0.8 N to 87 N, were submitted to the optical measurements. Simple regression between the gray tone having the maximum number of pixels and the firmness showed an exponential correlation with R2 values of 0.717. On the contrary, the tone uniformity (maximum number of pixels with the same gray tone) resulted linearly correlated with hardness (R2 = 0.687). PLS algorithm allowed prediction of the flesh firmness with R2 of 0.777 (RMSE = 13 N). Artificial neural networks produced similar results. Although the current technique does not fully satisfies the need of an accurate selection, it could be considered for on-line applications by improving performances (e.g. acting on lamp spectral emissions and camera detection) and with easy mechanical modifications of the sorting lines.