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Review

Molecular Architecture and Predictive Performance of Polysaccharide Hydrocolloids in Sustainable Food Processing

Aug 2026 · Journal of food process engineering · 0 citations · 210 references

Abstract

Polysaccharide hydrocolloids are fundamental to modern food design because their molecular architecture dictates techno‐functional performance. This review synthesizes current knowledge on sources, structural features and functional roles, establishing clear links between monomer identity, substitution patterns (degree of esterification, sulfation, acetylation), molar‐mass distributions and hydration, rheology, gelation and interfacial behavior. Integrated characterization using SEC‐MALS, HPAEC‐PAD, 1D/2D NMR, scattering, rheology, tribology and interfacial measurements enables predictive performance specifications. Application spaces include beverages, cultured matrices, gel confectionery, emulsions, encapsulation and edible films. Performance is interpreted stepwise: viscosity facilitates product transport and stability during processing and storage, while gut fermentation of non‐digestible fractions produces short‐chain fatty acids with health implications. Stability and packaging are evaluated using accelerated protocols, acceptance criteria and barrier targets. Sustainability is assessed using life cycle metrics (ISO 14040/14044) and circular feedstocks from intensified extraction routes. Key industrial translation issues include mixing, heat transfer, fouling, membrane filtration, spray drying and biorefinery integration within techno‐economic reality. Cross‐cutting challenges encompass raw material variability, uneven reporting of analytical data, scale‐up limitations of intensified processes, interaction complexity in multicomponent food products and long‐term safety considerations for some modified polymers. Practical recommendations focus on performance‐based specifications, harmonized analytics, shelf‐life model integration with packaging, solvent loop closure verification for green processes and dataset standardization with sharing to enable predictive formulation and credible sustainability claims.

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