This review highlights the impact of moisture distribution and enzymatic activity on noodle quality, suggesting improved industrial practices.
Noodle quality is intricately regulated through mechanisms during multi‐stage resting. Insufficient mechanistic understanding often leads to simplified resting stages in industrial production, resulting in elevated cooking loss and deteriorated textural properties. Consequently, a systematic elucidation of multi‐stage resting mechanisms and strategically optimizing resting stages are imperative for quality enhancement and industrial advancement. This review employs a “mechanism–factors–evaluation–application” framework to synthesize multi‐stage resting physicochemical mechanisms, factors affecting resting, multimodal characterization techniques, and industrial implementation bottlenecks alongside innovative pathways. Multi‐stage resting optimizes starch–gluten hydration equilibrium by elevating the proportion of bound water, whereas stress relaxation—governed by dynamic disulfide bond reorganization—enhances texture during dough sheet resting. Osmotic pressure drive gas elimination to minimize porosity, and enzymatic synergy improves texture while requiring stringent control browning. Critical technological gaps of industrialization bottlenecks include the absence of tri‐parametric models for temperature–time–water content synergies, undefined bubble orientation mechanisms in three‐stage resting, insufficient multiscale correlations of enzymatic cascades, and the precision‐cost constraints of conventional analytical techniques. Future research would prioritize developing in situ monitoring technologies to track component dynamics, establishing cross‐scale coupling models, and integrating intelligent equipment with sustainable processes for gradient parameter optimization. Through interdisciplinary convergence, future research would aim to transition noodle production from empirically practices to data‐driven manufacturing, thereby advancing sustainable innovation in traditional food industries.
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Bai et al. (2025) studied this question.
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