Integrating biosynthesis insights with biotechnological potential of bioactive carotenoids in food systems
Bibliographic record
Abstract
: Background Carotenoids, a class of natural compounds in the terpenoid family, are known for their bioactivity and ability to improve human health, and are thus extensively linked to food applications. While current sourcing of carotenoids is principally synthetic, consumers show a strong preference for natural carotenoids, which has driven the growth of carotenoid production via microbial fermentation using algae, bacteria or yeast platforms. Scope and approach By bridging advanced biosynthetic machinery with formulation science, this review critically examines the translational potential of microbial carotenoids as multifunctional ingredients in future food products. Key findings and conclusions : Recent advances in metabolic engineering and synthetic biology have enabled efficient production of commonly utilized carotenoids, such as astaxanthin and β-carotene, and demonstrated proof-of-concept for engineering rare or new-to-nature carotenoids with potential biological activities in microbial hosts. Current developments include strategies for targeted engineering of biosynthetic pathways and precursor supply, and encapsulation technologies for enhancing the bioavailability and stability of carotenoids in functional foods. Nevertheless, biotechnological production of carotenoids and associated downstream processing approaches face both challenges and prospects in scaling up to industrial production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".