Assessment of surface aspect of foods using ImagJ plugins

Surface aspect, also called surface texture, is an important characteristic of foods as well as color, shape, consistency and taste. This work shows the ability of different image analysis techniques to characterize the surface texture of two cereal products: corn flakes (3 commercial samples) and plum cakes (5 experimental samples). In particular, two ImageJplugins were used: the Grey Level Co-occurrence Matrix (GLCM) and the Angle Measure Technique (AMT). A quantitative analysis of different GLCM descriptors was used to characterize the surface texture of each sample, and significant differences (p<0.05) were highlighted within each group. By applying the Principal Component Analysis, followed by the Partial Least Squares Discriminant Analysis on the AMT spectra obtained from each image dataset, the samples class modeling (sensitivity>0.83; specificity>0.69) only as a function of the surface texture features was found. These results showed the high potential of the ImageJ-plugins in the evaluation of the surface properties of foods. As the surface texture depends on many factors and can influence many other properties of foods, its evaluation is important especially to predict some phenomena related to changes in formulations or process conditions.