1Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, Indiana, USA
Understanding the enzymatic reactivity of starch-based materials is essential to optimizing their functional properties and evaluating it in raw materials characteristics. This study investigates the interaction between banana starch and amylase, focusing on catalytic conversion of the substrate by the enzyme with kinetics and inhibition mechanisms. Starch hydrolysis by amylase involves a multistep process beginning with enzyme adsorption onto the starch surface, followed by catalysis that yields products such as maltose and oligosaccharides, evaluated as maltose equivalents. Digestibility assays used to quantify the carbohydrate composition of banana starches indicated significant levels of resistant starch (RS) and insoluble dietary fiber (IDF), components known to influence enzymatic accessibility and reactivity. To better understand the chemical surface properties of the starch granules, their properties were quantified using techniques such as inverse gas chromatography (iGC) and X-ray photoelectron spectroscopy (XPS). Kinetic evaluation using initial reaction rates revealed the presence of inhibitory effects. These findings indicate that enzymatic activity is inhibited by substrate or product, with additional contributions potentially arising from endogenous compounds in the raw materials. Overall, enzyme kinetics elucidated the inhibitory phenomena governing amylase and starch interactions, contributing to a deeper understanding of how material composition and surface properties regulate enzymatic hydrolysis, and the catalytic effect. This study provides mechanistic insights into the enzymatic degradation of native starch granules, elucidating the interplay between granular structure and reactivity. It further emphasizes the critical role of combining detailed surface characterization with quantitative enzymatic assays to accurately predict starch functionality in both formulation design and in vitro digestion modeling.