Predicting the Glycemic Index of Biscuits Using Static In Vitro Digestion Protocols

Area of Science:

• Food Science
• Biochemistry
• Nutritional Science

Background:

• Accurate prediction of estimated glycemic index (eGI) for foods like biscuits is crucial for dietary management.
• Existing in vitro digestion methods require optimization for complex carbohydrate foods.
• Standard commercial biscuits with known clinical glycemic index (GI) values were used.

Purpose of the Study:

• To explore and validate in vitro digestion methods for predicting eGI values of biscuits.
• To compare the efficacy of INFOGEST and single-enzyme digestion protocols.
• To establish mathematical models for eGI prediction based on digestion kinetics.

Main Methods:

• Digestion of standard commercial biscuits using INFOGEST and single-enzyme protocols (porcine pancreatin or α-amylase).
• Acquisition of digestion kinetic parameters through mathematical fitting.
• Calculation of Area Under the Curve of Reducing-sugar (AUR180) over 180 minutes.
• Development of mathematical equations correlating AUR180 with clinical GI values.

Main Results:

• The AUR180 parameter from single-enzyme digestion (porcine pancreatin or α-amylase) showed superior potential in predicting eGI compared to INFOGEST.
• Established equations: GI = 1.834 + 0.009 × AUR180 (R²=0.952) with porcine pancreatin, and GI = 6.101 + 0.009 × AUR180 (R²=0.902) with α-amylase.
• The developed in vitro method achieved an error rate of less than 30% for most biscuit samples.

Conclusions:

• A rapid and accurate in vitro method for predicting biscuit eGI values has been developed.
• The AUR180 parameter derived from single-enzyme digestion provides a reliable predictor for eGI.
• The established mathematical models offer a valid approach for estimating the glycemic response of biscuits.