AI-Driven Quality Assessment of Frozen Dough Using Multimodal Spectroscopic and Rheological Data

Abstract

Frozen dough is a critical intermediate in industrial baking, yet its quality remains difficult to assess because freezing, storage, thawing, and mechanical handling alter gluten networks, starch crystallinity, yeast viability, water distribution, and rheological behavior simultaneously. Conventional quality evaluation often relies on offline rheological testing, sensory inspection, or limited chemical assays, which are slow, sampling-intensive, and poorly matched to continuous production environments. This paper examines the system-level design of artificial intelligence driven quality assessment for frozen dough using multimodal spectroscopic and rheological data. It argues that the central challenge is not merely model selection but the construction of a reliable socio-technical infrastructure that integrates near-infrared and mid-infrared spectroscopy, hyperspectral imaging, oscillatory and extensional rheology, process telemetry, and contextual metadata. The discussion analyzes data acquisition architecture, calibration transfer, multimodal fusion, uncertainty estimation, edge and cloud deployment, model governance, fairness across production sites, sustainability implications, and policy constraints. Particular attention is given to structural trade-offs among predictive accuracy, interpretability, latency, cost, robustness, and regulatory accountability. The paper also considers how federated learning, model cards, and interpretable machine learning can support deployment without concentrating data or authority in a single actor. Through cross-domain comparisons with medical imaging, precision agriculture, and supply chain analytics, the paper identifies design principles for resilient frozen dough quality systems. It concludes that AI driven multimodal assessment can improve consistency and reduce waste, but only if technical performance is matched by transparent governance, maintenance capacity, and clear allocation of responsibility across equipment vendors, bakeries, regulators, and consumers.