Using AI in Food Hydrocolloid Research: Evidence Quality, Mechanistic Explainability, and Real-Food Validation

Artificial intelligence (AI) is applied to predict rheology, texture, microstructure, and sensory responses in hydrocolloid foods. However, reliability is limited by small datasets, formulation leakage, inadequate material descriptors, and insufficient external validation. This review presents an evidence-based, mechanistically grounded framework for assessing AI in food hydrocolloid research. Existing studies demonstrate predictions of viscosity, rheological behavior, yogurt texture and flow, pectin properties, and relationships between liquid-food rheology and perceived texture. Yet these results do not support claims that AI can design formulations across suppliers, grades, processes, and food categories. The framework recommends representing molecular features, hydration, ionic conditions, phase behavior, processing history, and mesoscale structure as causal or conditional variables, rather than reducing materials to ingredient names. It also emphasizes problem-specific model selection, leakage-safe validation, uncertainty estimation, applicability domains, mechanistic interpretation, and multimodal data fusion. A five-level validation hierarchy advances from random internal splits to grouped batch testing, withheld materials, independent real-food matrices, and prospective pilot-scale and sensory confirmation. Minimum reporting should cover material identity, formulation basis, processing, measurement methods, preprocessing, model development, failed formulations, and interoperable data sharing. Emerging applications should remain evidence-bounded, positioning AI as a reproducible scientific instrument complementing – not replacing – formulation science, process validation, sensory assessment, and safety evaluation.