Performance optimization of neural-network based colour measurement tools for food applications

Colour is the first attribute subject to consumer perception in determining food quality and, in many cases, this is the only possible mean to qualify product at purchase. For this reason, the description of colour by analytical methods is fundamental in food processing control. Computer vision systems acquire RGB data which are device-dependent and sensitive to the different lightning. Therefore, they are not directly useful for colour evaluation to mimic human vision. On the contrary, traditional colorimeters, which adopt CIELab coordinates, work in human-oriented colour space where euclidean distance between two different colours (ΔE) is well related to the difference perceived by human sight. Nevertheless, vision systems have many advantages as the capability of acquiring larger areas of the food surface and the easiness of implementation in automated plants at low costs. Neural networks, trained on a set of selected colour samples, can approximate RGB to L*a*b* relationships to characterise the colour of food samples under test. The aim of this paper is to present a rapid method based on neural networks for the calibration of a CCD (Charge-Coupled Device) camera colour acquisition system to obtain reliable L*a* b* information. Preliminary results concerning the influence of the composition of the training test and the camera settings (aperture and time of exposure) on the reliability and accuracy of the colour measurement system are also discussed.