Process Design and Microbial Growth Modelling of Self-Induced Anaerobic Fermentation for Mixed-Maturity Arabica Coffee Processing

Abstract

Mixed-maturity Arabica coffee cherries constitute a heterogeneous raw material requiring postharvest processing capable of improving process consistency and product quality. This study aimed to develop a self-induced anaerobic fermentation (SIAF) process and model microbial population development during spontaneous and L. plantarum-inoculated solid-state fermentation. Process development followed an iterative approach comprising natural processing, submerged SIAF, and solid-state SIAF. Microbial growth modelling of solid-state SIAF used observations at 0, 12, 24, 36, and 48 h for total plate count (TPC), lactic acid bacteria (LAB), and mold and yeast, supported by pH, titratable acidity, and temperature measurements. Microbial populations were fitted using modified Gompertz and Logistic models. Inoculation was associated with earlier and greater LAB development, reaching 9.00 log CFU/g at 48 h compared with 7.65 log CFU/g under spontaneous fermentation. The modified Gompertz model provided an equal or lower prediction error across all microbial trajectories than the Logistic model. However, sparse sampling, unobserved stationary phases, and population fluctuations limited the reliability of several model parameters. The resulting models provide exploratory reference profiles for fermentation monitoring and future scale-up validation