AI-Assisted Prediction of Polysaccharide–Gluten Interactions for Optimizing Frozen Dough Quality

Abstract

Frozen dough quality depends on coupled biochemical, colloidal, thermal, and logistical processes that are difficult to optimize through empirical formulation alone. Polysaccharides can stabilize gluten networks, modulate ice recrystallization, and alter water distribution, but their effects are strongly nonlinear and depend on molecular structure, concentration, freezing rate, storage history, and baking conditions. This paper examines artificial intelligence assisted prediction of polysaccharide–gluten interactions as a system-level infrastructure problem rather than a narrow modeling task. It argues that predictive accuracy must be embedded within data architectures, sensing modalities, model governance, deployment pathways, and sustainability constraints. The discussion first frames frozen dough as a socio-technical infrastructure in which formulation decisions interact with cold-chain operations, supplier variability, and consumer expectations. It then analyzes multimodal data acquisition, including rheological, thermal, spectroscopic, imaging, and textual sources, and considers the trade-offs among deep learning, physics-informed learning, and hybrid modeling. The paper emphasizes robustness, interpretability, fairness across suppliers and regions, and policy mechanisms for validation and audit. It further evaluates how AI-assisted formulation can reduce waste and energy use while avoiding rebound effects and inequitable access. A central claim is that polysaccharide–gluten prediction systems should be designed as governed decision-support infrastructures that connect laboratory discovery, industrial production, and regulatory oversight. The conclusion proposes that long-term value depends less on isolated predictive benchmarks and more on interoperable data, transparent uncertainty, lifecycle accountability, and adaptive deployment in real bakery supply chains.