Surface texture characterization of an Italian pasta by means of univariate and multivariate feature extraction from their texture images

Surface texture is an important characteristic of foods, as well as color, shape, consistency and taste. It plays an important role in consumers' decision and it can affect the properties of a product during its preparation. This work shows the ability of three different image analysis techniques to characterize the surface texture of three Italian pasta samples. The first method is based on the evaluation of Heterogeneity (HTG); the second on the gray level co-occurrence matrix (GLCM) and Haralic statistics; the third, the angle measure technique (AMT), is based on image multivariate feature extraction. The results obtained showed that it is possible to highlight differences in the surface aspect of pasta samples both before and after cooking, and that it is also possible to correlate them to some of their chemical–physical characteristics (e.g., total starch and protein contents, solids lost in the cooking water, pasta adhesiveness; r>0.6, pb0.05). A partial least square discriminant analysis (PLS-DA) applied on GLCM and AMT results allowed the classification of the different pasta samples only on the basis of their surface texture features (sensitivity>0.963; specificity>0.648).