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Predicting Antioxidant Synergism via Artificial Intelligence and Benchtop Data.

Lucas Ayres1, Tomás Benavidez2, Armelle Varillas3

  • 1Department of Chemistry, Clemson University, Clemson, South Carolina 29634, United States.

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Predicting antioxidant mixture interactions is challenging. An AI model improved by experimental data accurately forecasts synergistic, additive, or antagonistic effects in lipid oxidation control.

Keywords:
antioxidantlipid oxidationmachine learningsynergism

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Area of Science:

  • Food Science
  • Chemistry
  • Artificial Intelligence

Background:

  • Lipid oxidation in unsaturated fatty acids causes off-flavors and odors.
  • Antioxidant mixtures are used to enhance efficacy and minimize negative effects.
  • Predicting antioxidant interactions (synergistic, additive, antagonistic) is complex and often empirical.

Purpose of the Study:

  • To develop an artificial intelligence (AI) model for predicting antioxidant mixture interactions.
  • To improve the accuracy of AI predictions by incorporating experimental data.

Main Methods:

  • A deep learning AI model was initially trained to predict antioxidant interactions based on combination index values.
  • The model was tested on a dataset of 140 antioxidant mixtures.
  • The AI algorithm was enhanced with experimental data from the TBARS assay to improve prediction accuracy.

Main Results:

  • Initial AI model predictions were inaccurate for phenolic antioxidant mixtures in lard.
  • Enhancing the AI model with chemically relevant experimental data significantly improved prediction performance.
  • The enhanced model provided statistically relevant and accurate predictions of antioxidant interactions.

Conclusions:

  • Chemically relevant experimental data is crucial for enhancing AI model performance in predicting antioxidant behavior.
  • The proposed AI-driven method offers a novel, auxiliary tool for rational prediction of antioxidant mixture interactions.
  • This approach can aid in optimizing antioxidant formulations for controlling lipid oxidation.