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

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