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<p>The industrial performance of hydrocolloids (essential structure-forming ingredients in processed foods) is affected by source variability, extraction severity, molecular degradation, purification history, drying conditions, and food matrix interactions. Conventional hydrocolloid production often optimizes extraction yield and formulation performance separately, which limits the controllability of the molecular quality, batch consistency, functionality, and scale-up behavior. This review examines artificial intelligence (AI)-enabled hydrocolloid bioprocessing as an integrated strategy for connecting biomass selection, green extraction, process intensification, real-time quality monitoring, predictive modeling, and end-use food functionality. We highlight how hydrocolloid quality attributes molecular-weight distribution, degree of esterification or substitution, charge density, purity, hydration behavior, viscoelastic response, water mobility, and interfacial activity can be transformed into machine-readable descriptors for processing–quality–functionality modeling. Particular emphasis is placed on ultrasound-, microwave-, enzyme-, pressure-, and fermentation-assisted production routes; spectroscopy, imaging, rheology, and process analytical technologies for quality monitoring; and machine learning approaches for prediction, optimization, feature selection, digital twins, and closed-loop process control. After identifying a central knowledge gap (i.e., treating extraction, characterization, AI, and food application as separate tasks rather than linking processing history to molecular quality and final performance in realistic food systems), we herein propose a processing–quality–functionality framework to support the development of reliable, scalable, and sustainable hydrocolloid bioprocesses. Future progress will require standardized datasets, negative-result reporting, interpretable models, food matrix validation, techno-economic assessment, and stronger integration between experimental bioprocessing and data-driven control. This perspective provides a foundation for moving hydrocolloid production from empirical optimization toward intelligent, quality-driven, and sustainability-oriented food bioprocessing.</p>

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Artificial Intelligence–enabled Sustainable Food Bioprocessing of Hydrocolloids: Smart Extraction, Process Intensification, Digital Twins, and Real-time Quality Control

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Wajid Zaman,
  • Asma Ayaz

摘要

The industrial performance of hydrocolloids (essential structure-forming ingredients in processed foods) is affected by source variability, extraction severity, molecular degradation, purification history, drying conditions, and food matrix interactions. Conventional hydrocolloid production often optimizes extraction yield and formulation performance separately, which limits the controllability of the molecular quality, batch consistency, functionality, and scale-up behavior. This review examines artificial intelligence (AI)-enabled hydrocolloid bioprocessing as an integrated strategy for connecting biomass selection, green extraction, process intensification, real-time quality monitoring, predictive modeling, and end-use food functionality. We highlight how hydrocolloid quality attributes molecular-weight distribution, degree of esterification or substitution, charge density, purity, hydration behavior, viscoelastic response, water mobility, and interfacial activity can be transformed into machine-readable descriptors for processing–quality–functionality modeling. Particular emphasis is placed on ultrasound-, microwave-, enzyme-, pressure-, and fermentation-assisted production routes; spectroscopy, imaging, rheology, and process analytical technologies for quality monitoring; and machine learning approaches for prediction, optimization, feature selection, digital twins, and closed-loop process control. After identifying a central knowledge gap (i.e., treating extraction, characterization, AI, and food application as separate tasks rather than linking processing history to molecular quality and final performance in realistic food systems), we herein propose a processing–quality–functionality framework to support the development of reliable, scalable, and sustainable hydrocolloid bioprocesses. Future progress will require standardized datasets, negative-result reporting, interpretable models, food matrix validation, techno-economic assessment, and stronger integration between experimental bioprocessing and data-driven control. This perspective provides a foundation for moving hydrocolloid production from empirical optimization toward intelligent, quality-driven, and sustainability-oriented food bioprocessing.