Machine Learning-Assisted Prediction of Frozen Dough Quality Based on Polysaccharide–Gluten Interactions
Keywords:
frozen dough quality; machine learning; polysaccharide-gluten interactions; cryoprotection; predictive infrastructure; food systems governanceAbstract
Frozen dough quality is governed by coupled physical, chemical, and biological processes in which polysaccharide additives interact with the gluten network under freezing and frozen storage. These interactions are nonlinear, multiscale, and sensitive to formulation, process history, and cold-chain variability. Machine learning offers a pathway to predict quality outcomes from heterogeneous data, but its effective use requires more than algorithmic sophistication. This paper presents a system-level analysis of machine learning-assisted frozen dough quality prediction with a focus on polysaccharide-gluten interactions. The discussion develops a layered architecture that links formulation descriptors, process signals, molecular interaction features, predictive models, and operational decision support. It examines how structural knowledge from food chemistry can constrain data-driven models and improve generalization. Emphasis is placed on data infrastructure, feature engineering, model interpretability, robustness under distribution shift, operational governance, fairness for heterogeneous producers, and sustainability implications. The paper argues that predictive quality systems should be treated as socio-technical infrastructures in which mechanistic knowledge, machine learning, data governance, and policy must be co-designed. The analysis draws on food science, machine learning systems engineering, data management, and agrifood policy to identify structural trade-offs and future directions for responsible deployment in frozen bakery supply chains.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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.
