A Practical Framework for Textural Categorization as a Guide to Food Bar Formulation

Model food bars were produced with different cooking temperatures and compression distance in order to determine if changing these processing parameters could produce meaningful differences in texture. The food bars were characterized by instrumental texture analysis using a cutting test and three-point bending test, and texture parameters were extracted from the resulting force curves. In order to determine if the changes in texture were meaningful, a selected set of texture-parameters were used to propose a novel approach to the classification of the texture of different food bars. The novel approach is to coarse-grain each texture parameter into a discrete number of bins, such as "high" and "low" value bins, based on some algorithm for defining the number of bins and the cutoff value between bins; thereby reducing the texture characterization from essentially infinite variability, to one with a fixed number of texture categories (e.g., 32 or 243, depending of the number of bins and parameters selected). Using a set of 30 commercial food bars, seven classification algorithms were evaluated for their utility in showing similarities and differences between the texture of the various food bars. When applied to the model food bars, the results showed that both the cooking temperature and the compression distance were able to alter the texture, though the former had a stronger effect. We conclude that this novel approach to texture classification is an easy-to-implement framework that can provide valuable insights for market research and product formulation of food bars.