Modelo predictivo del crecimiento microbiano durante la fermentación de cacao fino de aroma

Abstract

CONDITION FOR PROJECT PUBLICATION. Cocoa fermentation is a biotechnological process dependent on microbial activity and the control of environmental variables, particularly temperature. This study aimed to model the growth kinetics of acetic acid bacteria and lactic acid bacteria in order to develop predictive tools to optimize the fermentation of fine-flavor cocoa. The hypothesis proposed that microbial behavior can be described using mathematical models capable of predicting the optimal fermentation time under different thermal conditions. Microbial growth was quantified through colony-forming unit (CFU/g) counts, logarithmic transformation, and nonlinear modeling in RStudio using the Gompertz model with 95% confidence intervals. Additionally, a response surface analysis was performed to evaluate the interaction between temperature and time on microbial dynamics. The results showed significant variations in the specific growth rate and bacterial lag phase depending on temperature, enabling the development of reliable predictive models. As a technological solution, a mobile application integrating temperature sensors was developed to estimate fermentation time and provide technical recommendations to producers. It is concluded that the integration of microbiology, mathematical modeling, and digital technology enhances management and decision-making in cocoa fermentation processes. Keywords: cocoa, fermentation, predictive model, digital agriculture, biotechnology