Nutrient analysis and response surface methodology reveal essential nutrient requirements for biomass production and astaxanthin synthesis in Phaffia rhodozyma
Bibliographic record
Abstract
Phaffia rhodozyma was cultivated on various substrates to maximize biomass and carotenoid production. Strain UCD 67-210 was able to metabolize industrially relevant sugars, as well as both simple and complex nitrogen sources. Response Surface Methodology was used to optimize the growth medium by varying key medium components: the carbon substrate, organic nitrogen, and inorganic nitrogen. Organic nitrogen had the greatest effect on biomass production, while inorganic nitrogen had little effect. Further testing revealed that inorganic nitrogen could be omitted from the culture broth without changing biomass production. The main components of yeast extract: amino acids, vitamins, and trace minerals, were further examined with no clear factor impacting biomass production. Individual amino acid supplementation was tested with the basal medium and twice the yeast extract concentration. Ten amino acids improved biomass production over the control condition and the yeast extract supplementation, demonstrating that key nutrients were limited in the rich medium. • Phaffia rhodozyma is an alternative producer of astaxanthin to Haematococcus pluvialis . • P. rhodozyma was analyzed through response surface methodology. • Yeast extract can serve as a sole nitrogen source at low concentrations. • Yeast extract also provides vitamins and trace elements at low concentrations. • Ten amino acids were beneficial to biomass 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".