Moreno-Zambrano Mauricio, Grimbs Sergio, Ullrich Matthias S, Hütt Marc-Thorsten
Department of Life Sciences and Chemistry, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany.
R Soc Open Sci. 2018 Oct 17;5(10):180964. doi: 10.1098/rsos.180964. eCollection 2018 Oct.
Cocoa bean fermentation relies on the sequential activation of several microbial populations, triggering a temporal pattern of biochemical transformations. Understanding this complex process is of tremendous importance as it is known to form the precursors of the resulting chocolate's flavour and taste. At the same time, cocoa bean fermentation is one of the least controlled processes in the food industry. Here, a quantitative model of cocoa bean fermentation is constructed based on available microbiological and biochemical knowledge. The model is formulated as a system of coupled ordinary differential equations with two distinct types of state variables: (i) metabolite concentrations of glucose, fructose, ethanol, lactic acid and acetic acid and (ii) population sizes of yeast, lactic acid bacteria and acetic acid bacteria. We demonstrate that the model can quantitatively describe existing fermentation time series and that the estimated parameters, obtained by a Bayesian framework, can be used to extract and interpret differences in environmental conditions. The proposed model is a valuable tool towards a mechanistic understanding of this complex biochemical process, and can serve as a starting point for hypothesis testing of new systemic adjustments. In addition to providing the first quantitative mathematical model of cocoa bean fermentation, the purpose of our investigation is to show how differences in estimated parameter values for two experiments allow us to deduce differences in experimental conditions.
可可豆发酵依赖于多个微生物群体的顺序激活，引发一系列生化转化的时间模式。了解这一复杂过程至关重要，因为它是最终巧克力风味和口感的前体形成过程。与此同时，可可豆发酵是食品工业中控制最少的过程之一。在此，基于现有的微生物学和生化知识构建了一个可可豆发酵的定量模型。该模型被表述为一个耦合常微分方程组，具有两种不同类型的状态变量：（i）葡萄糖、果糖、乙醇、乳酸和乙酸的代谢物浓度，以及（ii）酵母、乳酸菌和醋酸菌的种群数量。我们证明该模型能够定量描述现有的发酵时间序列，并且通过贝叶斯框架获得的估计参数可用于提取和解释环境条件的差异。所提出的模型是深入理解这一复杂生化过程的宝贵工具，可作为新系统调整假设检验的起点。除了提供首个可可豆发酵的定量数学模型外，我们研究的目的是展示两个实验估计参数值的差异如何使我们推断实验条件的差异。
