Evaluating the Guo–Campanella viscoelastic model

Biological materials are characterized by complex structures and compositions, leading to viscoelastic behavior. Their viscoelastic characterization is important for the evaluation, design, and optimization of processes to ensure high quality products. Proposals of methodologies of analysis and modeling are critical steps in studying the rheological properties of these materials. In this context, a new model, the Guo–Campanella Model, was recently proposed to describe the stress-relaxation behavior of biological material. This work is an independent and impartial evaluation of this new model. It considers 10 different samples, comprising in natura and processed foods, from both plant and animal bases. For comparison, a Generalized Maxwell Model and the Peleg Model were also evaluated. The Guo–Campanella Model fitted the stress-relaxation data of evaluated products well, demonstrating its validity for describing the viscoelastic behavior of biological materials with different structures, sources, and processing. Finally, the Guo–Campanella Model parameters were evaluated and their interpretations and possible uses described. It was shown that the Guo–Campanella Model can be successfully used for future studies. Practical applications: The stress-relaxation assay is a common technique for characterizing the viscoelastic properties of biological materials. The results obtained are generally evaluated using such compound models as the Generalized Maxwell model. Although this approach is interesting from a fundamental point of view, it results in many parameters to evaluate, thus increasing the complexity and limiting the interpretation. In this sense, the Guo–Campanella Model has only two parameters, which facilitates interpretation, especially for practical applications. This work validated this model, also contributing to its interpretation by discussing the meaning of its parameters. Consequently, this is potentially useful for future studies on food properties and process design.