Modeling Starch Digestograms: Computational Characteristics of Kinetic Models for in vitro Starch Digestion in Food Research.

Starch digestion is mostly investigated with in vitro techniques, and time-course measurements are common. These yield digestograms that are modeled by theoretical, semitheoretical, and empirical kinetic equations, many of which are reviewed here. The Duggleby model has Michaelis-Menten functions, and its dependent variable is on both sides of the equation with no apparent parameter for maximum digestible starch (D ). The Gaouar and Peleg models are equivalent. They predict both the initial digestible starch (D ) and D , and an average digestion rate, but they can reveal "biratial" digestions. The first-order kinetic model exhibits diverse predictabilities and, when linearized, D is sometimes equated to 100 g/100 g dry starch (100%), it yields an average rate of digestion and can predict negative D . The log of slope (LOS) model is unique in revealing the rapid-to-slow digestion rate phenomenon, but without guidelines to identify such. The LOS model does not sometimes use all the digestogram data, can predict D greater than 100%, and returns zero digestion rate for some digestograms. However, some starchy materials exhibit a slow-to-rapid digestion rate phenomenon, as demonstrated with an example. The modified first-order kinetic model uses all the digestogram data with practical constraints (D  ≥ 0 g/100 g dry starch; D  ≤ 100 g/100 g dry starch), describes all digestograms, and yields an average digestion rate, but it can also be used for "biratial" digestions. In addition, the logistic and Weibull models are discussed. Using some published data, the computational characteristics of these commonly used models are presented with objective parameters to guide choices.