Development of a Novel Kinetic Model for Cocoa Fermentation Applying the Evolutionary Optimization Approach

• Published in: International journal of food engineering Vol. 14; no. 5
• Main Authors: López-Pérez, Pablo A., Cuervo-Parra, Jaime A., Robles-Olvera, Víctor José, Del C Rodriguez Jimenes, Guadalupe, Pérez España, Victor H., Romero-Cortes, Teresa
• Format: Journal Article
• Language: English
• Published: De Gruyter 26.07.2018
• Subjects: evolutionary algorithm; parametric identification; temperature
• ISSN: 2194-5764; 1556-3758
• DOI: 10.1515/ijfe-2017-0206

Traditional Mexican cocoa fermentation performed in batch was studied by applying kinetic modelling with experimental validation. Similar microbiological behaviour was observed up to 60 h, with a temperature increase at 72 h that remained constant (50 °C) until 156 h. Metabolite-production kinetics (ethanol and acetic acid) from degradable mucilage (glucose) was explored. Exploration involved applying different combinations of unstructured growth models, in order to consider the effect of temperature when predicting the concentration of metabolites in these microorganisms. Two methods were used to optimize model parameters: the Levenberg–Marquardt optimization approach and Genetic Algorithms (GAs). GAs which could be used to scale up the fermentation process indicated the applicability of this model for predicting fermentation quality. The maximum specific rate average for μ and saturation constant (Ks) were 0.0961 h and 1.4 mg/g m.s., respectively. The results obtained indicate the expediency of this technique for future application in the design and control of batch fermentation.