'Building-block coupling effect' in dimethylpyrazine formation: Mechanistic insights and predictive modeling.
Hui Xu1, Minmin Lian1, Qunna Yang1
1College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, PR China.
Food Research International (Ottawa, Ont.)
|April 6, 2026
Summary
Hydroxyacetone (HA) and ammonium acetate form dimethylpyrazines (DMPs) via a zero-order kinetic model. A machine learning model predicts DMP formation, identifying temperature as a key factor for flavor control.
Area of Science:
- Food Chemistry
- Organic Chemistry
- Chemical Kinetics
Background:
- Dimethylpyrazines (DMPs) are key flavor compounds.
- Understanding DMP formation mechanisms is crucial for food flavor design.
- Hydroxyacetone (HA) is a precursor in Maillard reactions.
Purpose of the Study:
- Investigate DMP formation patterns and mechanisms using hydroxyacetone (HA).
- Establish a machine learning model to predict DMP isomer content.
- Develop a framework for controlling DMP formation for flavor applications.
Main Methods:
- Optimized reaction conditions using HA and ammonium acetate.
- Analyzed DMP formation kinetics (zero-order model).
- Applied machine learning (ExtraTrees regressor) for prediction and SHAP analysis for feature importance.
Main Results:
- Identified optimal HA-ammonium acetate system for DMP formation.
- Elucidated DMP isomer selectivity via 'building-block coupling effect' (C3-C3, C4-C2 couplings).
- Achieved high predictive accuracy (R² ≈ 0.98) with temperature as the primary driver.
Conclusions:
- Refined the mechanistic understanding of DMP formation.
- Established an integrated mechanistic and data-driven framework for DMP control.
- Enabled rational flavor design by tailoring pyrazine profiles.
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