AI-Enhanced Digital Twins as Decision-Support Layers for Precision Management of Microbial Communication: The Case of Cocoa Pulp Juice Fermentation

Fermentation quality is shaped not just by what microbes produce but by how they communicate. Cocoa pulp juice fermentation is governed by quorum sensing (QS) — bacterial autoinducers (AHLs, AI-2, peptides) and fungal mediators (farnesol, tyrosol) — that coordinate microbial succession and flavour precursor formation. Yet QS states are absent from operational fermentation control: conventional bioreactors act only on macroscopic variables (pH, temperature, dissolved oxygen) and cannot read the molecular language coordinating quality-determining transitions. This translational perspective proposes that a digital twin, implemented as a bioprocess decision suite, provides the missing intelligence layer. It is not a vessel replacement but a QS-aware reasoning system that translates communication states into explainable, audit-ready decisions and enables in silico sensory predesign before a run begins. A key design constraint is polyphenol-mediated AHL quenching and acid-accelerated AI-2 degradation in the cocoa matrix, which we formalise as an architectural specification. Time-series transformer models with attention-based explainability anchor the near-term AI layer, with graph neural networks, Bayesian uncertainty quantification, and reinforcement learning forming a progressive maturation roadmap. A three-tier framework scales deployment from artisanal to biomanufacturing operations.