Review examines how processing techniques alter casein properties, indicating pathways for optimizing dairy products.
Background Casein, the predominant protein in milk, forms colloidal micelles whose structural integrity and functional properties, including solubility, emulsification, gelation and stability, are critically dependent on processing conditions. Understanding how thermal and nonthermal technologies modulate casein structure is essential for optimising dairy products and developing advanced protein‐based systems. Scope and Approach This review critically examines the molecular and colloidal transformations induced in caseins by conventional thermal treatments (pasteurisation, ultra‐high temperature [UHT], moderate heating) and emerging nonthermal technologies (high‐pressure processing [HPP], ultrasound [US], pulsed electric fields [PEF], cold plasma [CP], ohmic heating [OH] and enzymatic modification). The central concept explored is the existence of process‐intensity windows—ranges of processing parameters within which casein micelles exhibit negligible changes, enhanced functionality or detrimental aggregation. Key Findings and Conclusions Each processing technology induces distinct structural outcomes. Thermal treatments promote whey–casein interactions and mineral redistribution, whereas nonthermal approaches enable more selective modulation of micellar surface charge, hydrophobicity and calcium phosphate equilibria. Across technologies, low‐to‐moderate intensities often improve solubility and emulsification, while excessive intensities trigger irreversible aggregation, age gelation or phase separation. Recognising these intensity‐dependent transitions provides a framework for rationally designing casein‐based ingredients with tailored functionality. Key challenges remain in raw material variability, industrial scalability and the prediction of behaviour in complex food matrices. Future advances will require integrated structure–function models and in situ analytical approaches to guide processing optimisation.
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Rodrigues et al. (2026) studied this question.
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