Modelling approaches to capture role of gelatinization in texture changes during thermal processing of food

TL;DR

This paper explores different modeling techniques to incorporate starch gelatinization into texture prediction models during thermal food processing, improving accuracy in predicting textural changes.

Contribution

It introduces three methods for integrating gelatinization physics into texture models, enhancing their ability to predict softening during heating.

Findings

Improved models better match experimental data for texture changes.

Incorporating gelatinization physics enhances prediction accuracy.

Models successfully capture initial softening due to gelatinization.

Abstract

While processing at elevated temperatures, starchy food products undergo gelatinization, which leads to softening related changes in textural characteristics. Study of role of gelatinization in texture development is limited; specifically, ignoring gelatinization physics can lead to erroneous prediction of Youngs modulus. In a recent work, we demonstrated a texture model that predicts local and effective Youngs moduli as functions of moisture content. While this model tracks experiments closely for drying, it deviates significantly from experiments for frying. In this paper, three different techniques for incorporating starch gelatinization are used to enhance the texture model, and the suitability of each technique is discussed. These improved models can capture the initial gelatinization induced softening and are in much better agreement with experiments. We expect that this work could contribute to better understanding and predictive capabilities of texture development.