DOI: 10.1002/jsfa.71198 ISSN: 0022-5142

Processing‐induced assembly pathways of rice glutelin: from nanostructure formation to functional performance

Xiuju Wang, Yaping Sun, Zhengtao Zhao

Abstract

Rice glutelin (RG), the major rice endosperm storage protein, has poor solubility because of its compact, β‐sheet‐rich, highly aggregated architecture stabilized by disulfide cross‐links and hydrophobic interactions, limiting its use in food and biomedical applications. No current review has systematically linked RG assembly pathways to resulting functionalities, leaving a critical gap in predictive material design. This review establishes a process‐driven assembly framework mapping processing parameters to three RG assembly categories: collapsed, extended, and interactive co‐assembly. Unlike previous studies of isolated assembly phenomena, it addresses three gaps: lack of unified classification criteria for RG nanostructures, ambiguous mapping between processing conditions and structural outcomes, and absence of a structure–function correlation matrix to guide application. Collapsed assembly states, formed by pH cycling or antisolvent precipitation, produce spherical nanoparticles with enhanced solubility and encapsulation. Extended assembly states, generated by acid‐assisted thermal treatment, form amyloid‐like fibrils with high β‐sheet content, superior interfacial activity, and improved rheology. Interactive states arise from co‐assembly with polysaccharides, proteins, or polyphenols, creating multicomponent complexes with tunable functionality. These processing‐induced nanostructures deliver substantial functional gains, including enhanced solubility (>90%), emulsifying activity index values up to 147.61 m 2  g −1 , encapsulation efficiencies > 90%, and up to fivefold increases in gel strength. We further establish explicit structure–function correlations linking these nanostructures to different functionalities. Finally, we outline future directions, including quantitative structure–function prediction using machine learning, with measurable inputs used to predict key outputs. This framework provides a theoretical foundation for designing sustainable RG‐based biomaterials and delivery systems through integrated, application‐oriented pathways. © 2026 Society of Chemical Industry.

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