Modeling the effects of assimilable nitrogen and temperature on fermentation kinetics in enological conditions

Abstract

We propose a dynamic model of alcoholic fermentation in wine-making conditions. In this model, the speed at which CO(2) is released is related to the effects of the main factors involved in fermentation in wine-making conditions: temperature (which can vary within a predefined range) and nitrogen additions (which must not exceed the maximal authorized level). The resulting model consists of ordinary differential equations including numerous parameters that need to be identified and important interactions between explicative variables. These parameters were identified by uncoupling the effects of variables during specific experiments. The results were validated on another series of experiments in different conditions.

Reconciling Competing Models: A Case Study of Wine Fermentation Kinetics

“…In our study we considered experimental data of three papers: [22], [15] and [16]. The experimental measures of the Pizarro team correspond to a wide range of data.…”

The virtual food system: Innovative models and experiential feedback in technologies for winemaking, the cereals chain, food packaging and eco-designed starter production

“…This explanatory model is based on physiological considerations and takes into account the main phenomena directly affecting kinetics in enological conditions, including the effects of: (i) nitrogen in yeast synthesis (growth phase) and activation of metabolism (stationary phase); (ii) the transport of sugar into yeast cells; (iii) ethanol inhibition; and (iv) temperature. The results obtained were very satisfactory: mean error between experimental and simulated data was lower than 5% (Malherbe et al, 2004). MOMAF was licensed to a private company and is currently marketed as a simulation software.…”

“…Based on fermentations performed at different temperatures and initial nitrogen concentrations, we modeled the impact of these two fermentation parameters on the production yields of five fermentative aromas representative of the different chemical families. We then integrated these yields into the MOMAF model (Malherbe et al, 2004) to obtain a dynamic model predicting the production kinetics of these five volatile compounds throughout alcoholic fermentation (called MODAPEC, for "modeling of aroma production in enological conditions"), as shown in Fig. 1 (Mouret et al, 2015).…”

“…These kinetic data are of primary importance for a better understanding of yeast metabolism, but they also have practical interest-instantaneous CO 2 production rate, for instance, is directly proportional to the energy required for temperature control in fermentation tanks (Colombie, Malherbe, & Sablayrolles, 2007;Goelzer, Charnomordic, Colombié, Fromion, & Sablayrolles, 2009). The online data collected were used to build a model predicting the fermentation kinetics (Malherbe, Fromion, Hilgert, & Sablayrolles, 2004), gas-liquid ratio (Morakul et al, 2011) and production kinetics of five fermentative aromas (Mouret, Farines, Sablayrolles, & Trelea, 2015). These data were also used to implement an information system called ALFIS (alcoholic fermentation information system) that allows the acquisition, annotation and consultation of online and offline measurements and provides a rich digital resource for sharing data across technologies and disciplines.…”

“…Using 125 fermentations corresponding to 49 natural and synthetic musts and 20 strains, we built a dynamic model-consisting of ordinary differential equations-to predict the main reaction of the fermentation (Malherbe et al, 2004). This model (called MOMAF, for "modeling of the main reaction of alcoholic fermentation") predicts cell growth, fermentation rate and sugar consumption (proportional to ethanol and CO 2 production) from two main fermentation parameters: temperature (including non-isothermal profiles, which are widely used in the industry) and nitrogen additions.…”