Intelligent Optimization of Fermentation and Freezing Parameters for High-Quality Frozen Dough Products
Keywords:
frozen dough, intelligent optimization, fermentation control, freezing parameters, digital twin, food systems governance, sustainabilityAbstract
Frozen dough products occupy a critical position in modern food supply chains because they reconcile centralized production, long-distance distribution, retail flexibility, and household convenience. Their quality, however, depends on tightly coupled fermentation and freezing processes whose optimal settings are difficult to determine through conventional trial-and-error methods. This paper develops a system-level analysis of intelligent optimization for fermentation and freezing parameters in frozen dough manufacturing. Rather than treating optimization as a narrow search problem, the discussion frames it as a socio-technical architecture that links sensing, data governance, process control, digital twins, predictive modeling, and policy constraints. The paper examines how machine learning and digital twin approaches can improve dough strength, gas retention, freeze tolerance, and baking performance while also introducing new trade-offs in robustness, interpretability, deployment cost, and fairness across small and large producers. It further considers how governance mechanisms, accountability requirements, and sustainability goals shape the design space of intelligent freezing and fermentation control. Through cross-domain comparisons with smart manufacturing and agricultural digital twins, the analysis identifies opportunities for adaptive control, infrastructure sharing, and resilient supply chain coordination. The central argument is that high-quality frozen dough products should be pursued not merely through better parameter tuning but through integrated institutional, technical, and environmental design. Such an approach can improve product consistency, reduce waste, and support more equitable food system outcomes, provided that optimization systems remain transparent, maintainable, and aligned with public-interest priorities.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
