Explainable Machine Learning for Predicting Freeze–Thaw Stability and Texture Retention in Frozen Dough Products
Keywords:
explainable machine learning, frozen dough, freeze–thaw stability, texture retention, food systems, interpretability, quality assurance, socio-technical infrastructureAbstract
Frozen dough products occupy a critical position in industrial baking because they enable centralized production, long-distance distribution, and on-demand baking. However, freeze–thaw cycles destabilize dough structure through ice recrystallization, gluten network disruption, yeast viability loss, and water migration, producing variability in texture retention and end-product quality. This paper examines explainable machine learning as a system-level capability for predicting freeze–thaw stability and texture retention in frozen dough products. Rather than treating prediction as a purely algorithmic problem, the discussion frames explainability as an infrastructural and governance requirement that connects sensor networks, laboratory measurements, process historians, model registries, quality management systems, and regulatory oversight. The paper first situates frozen dough quality within food systems and describes the data architecture required for reliable prediction, including time-series freezer telemetry, formulation records, rheological assays, imaging, and sensory panels. It then analyzes model families, interpretability methods, and the trade-offs between predictive performance, transparency, robustness, and operational usability. Particular attention is given to hybrid approaches that combine mechanistic knowledge of cryoprotection and dough rheology with data-driven learning. The paper further explores deployment challenges in industrial bakeries, including distribution shift, sensor drift, batch heterogeneity, fairness across product lines, and the sustainability costs of computational infrastructure. Governance implications are discussed in relation to traceability, auditability, food safety, labor, and policy. The conclusion argues that explainable machine learning can improve frozen dough quality only when explanations are embedded in decision workflows and validated against physical mechanisms, operational constraints, and stakeholder needs. Such systems must be designed as socio-technical infrastructures rather than isolated predictive models.
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