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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Sweetness prediction of natural compounds.

Jean-Baptiste Chéron1, Iuri Casciuc1, Jérôme Golebiowski1

  • 1Université Côte d'azur, CNRS, Institut de Chimie de Nice UMR7272, 06108 Nice, France.

Food Chemistry
|December 17, 2016
PubMed
Summary
This summary is machine-generated.

Researchers developed a new model to predict sweetener properties, identifying potent natural compounds with saponin or stevioside scaffolds. This discovery aids in designing novel low-calorie sweeteners.

Keywords:
Chemical spaceNatural compoundsStructure-activity relationshipSweeteners

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Area of Science:

  • Food Science
  • Computational Chemistry
  • Pharmacology

Background:

  • Sweeteners are crucial for low-calorie food products.
  • Predicting sweetener properties like sweetness, bitterness, and toxicity is challenging.
  • Existing databases lack comprehensive structure-activity relationship (SAR) data.

Purpose of the Study:

  • To develop a quantitative structure-activity relationship (QSAR) model for predicting sweetener properties.
  • To screen a large database of natural compounds for potential intense sweeteners.
  • To identify novel natural compounds with desirable sweetness profiles.

Main Methods:

  • Compiled an exhaustive database of sweeteners with known sweetness values.
  • Analyzed physico-chemical properties of potent sweeteners (hydrophobic scaffold, limited H-bond sites, moderate MW).
  • Performed in silico screening of natural compounds using the developed QSAR model.

Main Results:

  • Established a new QSAR model for sweetness prediction.
  • Identified key structural features of potent sweeteners.
  • Predicted sweetness, bitterness, and toxicity for a large natural compound database.
  • Revealed that saponin or stevioside scaffolds are common in predicted natural intense sweeteners.
  • Sweetness potency of identified compounds is comparable to known natural sweeteners.

Conclusions:

  • The developed QSAR model accurately predicts sweetener properties.
  • Natural compounds with saponin or stevioside scaffolds show promise as intense sweeteners.
  • This research provides a foundation for designing and analyzing new low-calorie sweeteners.