Akademik Veri Yönetim Sistemi
Trends in Food Science and Technology, cilt.168, 2026 (SCI-Expanded, Scopus)
Background: The growing demand for nutrient-dense and personalized foods has accelerated research into concentrated nutritional ingredients derived from diverse biological sources. Dairy streams, plant-based proteins, algae, fungi, seaweeds and edible insects provide abundant opportunities to extract high-value compounds through advanced separation and fermentation processes. Technologies such as microfiltration, ultrafiltration, nanofiltration, and precision fermentation have transformed conventional food processing into systems capable of producing bioactive, functional, and sustainable ingredients. However, optimizing these complex bioprocesses requires tools that can manage large datasets and predict multifactorial outcomes. In this context, artificial intelligence (AI) and machine learning (ML) are emerging as powerful enablers that can design, model, and control processing parameters to enhance yield, stability, and nutrient bioavailability. Scope and approach: This review synthesizes current advances in the development of concentrated nutritional ingredients from conventional (dairy and coproducts) and novel (plant, fungal, and insect) sources. It examines how AI and ML technologies can optimize the associated bioprocesses, including membrane fractionation, bioengineering, and precision fermentation, to improve efficiency, sustainability, and personalization. Key findings and conclusions: Concentrated nutrition represents a pivotal step toward achieving precision and sustainability in modern food systems. AI-driven bioprocess optimization enables data-informed control of nutrient extraction, functionality, and formulation, bridging the gap between biological potential and tailored human nutrition. Despite promising advances, challenges remain in model transparency, consumer acceptance, regulatory frameworks, and the scalability of AI-assisted production. Future progress will rely on the development of interoperable data systems, ethical frameworks, and cross-disciplinary collaboration to transform biological resources into intelligent nutrition systems for the next decade.