A parsimonious mechanistic model of aroma synthesis during wine fermentation
Abstract
Wine fermentation relies on the conversion of sugar and nitrogen into ethanol, carbon dioxide, and aroma compounds, the latter contributing to the sensory profile of the wine. Although this process is conducted industrially in batch fermenters, it remains poorly optimized. Key control parameters, such as temperature profiles and nitrogen supplementation, could be used to adapt the aromatic profile to consumer preferences. In this context, mechanistic models of aroma synthesis are highly relevant: they enable deeper understanding and prediction of fermentation dynamics, identification of optimal conditions, and design of real-time control strategies. However, existing models are scarce and overly simplistic, often relying on empirical or constant yield coefficients to describe sugar-to-aroma conversion, and thus fail to capture the impact of nitrogen addition or temperature variations on aroma synthesis. This study presents a parsimonious, fully mechanistic model of wine fermentation integrating the dynamics of sugar, ethanol, nitrogen, biomass and isoamyl acetate—a key aroma compound highly sensitive to nitrogen addition. The model represents two key nitrogen-dependent processes—sugar transport and enzyme-mediated aroma synthesis—whose dynamics successfully reproduce the experimentally observed shift in isoamyl acetate yield. The model is calibrated using a robust sequential calibration strategy designed to exploit its hierarchical structure. We use a comprehensive dataset of 81 fermentations conducted at varying initial sugar and nitrogen concentrations, with different temperature profiles and nitrogen supplementation regimes, and for which high-frequency measurements are available. Finally, model performance is assessed using error metrics and is compared with that of an existing model from the literature.
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BibTeX
@article{casenave2026parsimonious,
title = {{A parsimonious mechanistic model of aroma synthesis during wine fermentation}},
author = {Céline Casenave and Agustín G. Yabo and William Dangelser and Jean-Roch Mouret},
journal = {Journal of Food Engineering},
year = {2026},
month = sep,
doi = {10.1016/j.jfoodeng.2026.113363},
url = {https://hal.inrae.fr/hal-05771433v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - A parsimonious mechanistic model of aroma synthesis during wine fermentation
AU - Céline Casenave
AU - Agustín G. Yabo
AU - William Dangelser
AU - Jean-Roch Mouret
PY - 2026
DA - 2026/09/30
JO - Journal of Food Engineering
DO - 10.1016/j.jfoodeng.2026.113363
UR - https://hal.inrae.fr/hal-05771433v1
AB - Wine fermentation relies on the conversion of sugar and nitrogen into ethanol, carbon dioxide, and aroma compounds, the latter contributing to the sensory profile of the wine. Although this process is conducted industrially in batch fermenters, it remains poorly optimized. Key control parameters, such as temperature profiles and nitrogen supplementation, could be used to adapt the aromatic profile to consumer preferences. In this context, mechanistic models of aroma synthesis are highly relevant: they enable deeper understanding and prediction of fermentation dynamics, identification of optimal conditions, and design of real-time control strategies. However, existing models are scarce and overly simplistic, often relying on empirical or constant yield coefficients to describe sugar-to-aroma conversion, and thus fail to capture the impact of nitrogen addition or temperature variations on aroma synthesis. This study presents a parsimonious, fully mechanistic model of wine fermentation integrating the dynamics of sugar, ethanol, nitrogen, biomass and isoamyl acetate—a key aroma compound highly sensitive to nitrogen addition. The model represents two key nitrogen-dependent processes—sugar transport and enzyme-mediated aroma synthesis—whose dynamics successfully reproduce the experimentally observed shift in isoamyl acetate yield. The model is calibrated using a robust sequential calibration strategy designed to exploit its hierarchical structure. We use a comprehensive dataset of 81 fermentations conducted at varying initial sugar and nitrogen concentrations, with different temperature profiles and nitrogen supplementation regimes, and for which high-frequency measurements are available. Finally, model performance is assessed using error metrics and is compared with that of an existing model from the literature.
SN - 0260-8774
ER -