Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm

This paper describes an approach for the colour-based classification of RGB images, taken with a common digital CCD camera oninhomogeneous food matrices. The aimwas that of elaborating a feature selection/classification method independent of the specific food matrixthat is analysed, in the sense that the variables that are the most relevant ones for the classification of the analysed samples are selected in a blindway, with no a priori assumptions on the basis of the nature of the considered food matrix.Aone-dimensional signal describing the colour contentof each acquired digital image, which we have called colourgram, is created as the contiguous sequence of the frequency distribution curves ofthe three red, green and blue colours values, of related parameters (also including hue, saturation and intensity) and of the scores values derivingfrom the PCA analysis of the unfolded 3D image array, together with the corresponding loadings values and eigenvalues. Once a sufficientnumber of digital images has been acquired, the corresponding colourgrams are then analysed by means of a feature selection/classificationalgorithm based on the wavelet transform, wavelet packet transform for efficient pattern recognition (WPTER). This approach was tested ona series of samples of “pesto”, a typical Italian vegetable pasta sauce, which presents high colour variability, mainly due to technologicalvariables (raw materials, processes) and to the degradation of chlorophylls during storage. Good classification results (100% of correctlyclassified objects with very parsimonious models) have been obtained, also in comparison with the visual evaluation results of a panel test.