A mathematical model of cocoa bean fermentation. Issue 10 (17th October 2018)

• Record Type: Journal Article
• Authors: Moreno-Zambrano, Mauricio; Grimbs, Sergio; Ullrich, Matthias S.; Hütt, Marc-Thorsten
• Is Part Of: Royal Society open science. Volume 5:Issue 10(2018)
• Publication Date: 2018-10-17
• Subjects: theoretical biology -- Bayesian parameter estimation -- cocoa bean fermentation -- microbial fermentation products; Science -- Periodicals; 500
• DOI: 10.1098/rsos.180964
• Languages: English
• ISSNs: 2054-5703

Abstract

Abstract : 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.