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

Xingguang Peng1, Hongsheng Liu1, Xuying Li2

  • 1School of Food Science and Engineering, South China University of Technology, Guangzhou 510000, China.

Foods (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel in vitro digestion method for accurately predicting the estimated glycemic index (eGI) of biscuits. The method, using the Area Under the Curve of Reducing-sugar (AUR180), offers a rapid and reliable alternative for food analysis.

Keywords:
INFOGESTbiscuitsglycemic indexin vitro digestionsingle enzyme

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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.