Arachidonic acid production by Mortierella alpina MA2-2: Optimization of combined nitrogen sources in the culture medium using mixture design
Bibliographic record
Abstract
Arachidonic acid (ARA) is an omega-6 fatty acid that is essential for human nutrition. Commercial production of ARA by fermentation is of great interest, as it is present in relatively low levels in breast milk . The production of ARA by the fungus Mortierella alpina is affected by the types of nitrogen available in the culture medium as well as the carbon to nitrogen (C:N) ratio. In this study, the C:N ratio and combined nitrogen sources were investigated for optimal production of biomass, lipids, ARA content and concentrations by M. alpina MA2–2. Results showed that a C:N ratio of 15 could increase biomass, lipid content and ARA concentration by a 1.49, 1.50 and 1.99 fold-increase, respectively. After screening experiments, peptone, yeast extract, sodium nitrate (NaNO 3 ) and monosodium glutamate (MSG) were selected for closer study using mixture design to determine the optimal combination of nitrogen sources for maximizing ARA concentration. The combination of yeast extract and sodium nitrate was the most effective for producing ARA, resulting in 17.67 ± 0.16 g L −1 biomass, 32.7 ± 0.02 % lipids, and 39.33 ± 2.10 % ARA content (2270 ± 100.9 mg L −1 ARA concentration), corresponding to 1.21, 1.90, 1.32 and 3.05 fold-increases, respectively. This study demonstrates that a significant improvement in total lipid accumulation and ARA concentration can be achieved by combining a complex organic nitrogen source with a lower level of inorganic nitrogen in the culture medium.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".