ReviewComprehensive reviews in food science and food safety2026
Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.
Review in Comprehensive reviews in food science and food safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
The escalating generation of global vegetable waste represents a critical loss of bioactive resources, necessitating a paradigm shift from passive disposal to active nutrient upcycling. However, the industrial conversion of this heterogeneous biomass into standardized functional food ingredients is currently impeded by significant techno-economic barriers, primarily structural recalcitrance, compositional inconsistency, and the presence of toxic fermentation inhibitors. This review provides a comprehensive analysis of the synergistic application of microbial engineering and artificial intelligence (AI) to resolve these bioprocessing bottlenecks within a food-to-food closed-loop framework (as shown in the graphical abstract). We evaluate recent advances in engineering food-grade microbial chassis (e.g., Saccharomyces cerevisiae and Escherichia coli) to enhance lignocellulose degradation and stress tolerance. Concurrently, we examine the integration of AI across the entire value chain, covering deep learning-based rational enzyme design, genome-scale metabolic modeling, and intelligent process control for precision fermentation. Current evidence demonstrates that the hardware-software coupling of engineered strains and AI algorithms significantly enhances conversion efficiency and process robustness. Key findings highlight that AI-driven Design-Build-Test-Learn cycles facilitate the de novo creation of enzymes with superior kinetics and strains with adaptive stress response capabilities against toxins. Moreover, dynamic digital twin models effectively mitigate the impact of substrate variability, ensuring the batch-to-batch consistency required for food applications. We conclude that this data-driven synergistic paradigm is pivotal for establishing a resilient circular bioeconomy, enabling the reliable bioconversion of waste into high-value single-cell proteins, natural flavor additives, and sustainable packaging materials.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.