Lipid and β-Carotene Production by Rhodosporidium diobovatum Cultured with Different Carbon to Nitrogen Ratios
Bibliographic record
Abstract
Background: In food industry, carotenoids are used as food colorants conferring yellow to red color. This research was designed to study on lipid and β-carotene production by Rhodosporidium diobovatum cultured with different Carbon to Nitrogen (C/N) ratios. Methods: R. diobovatum was cultured in a medium containing 40 g/l glucose (as the carbon source) and different C/N ratios (20, 50, and 80), which were established by adding different amounts of (NH4)2SO4 (3.78, 1.51, and 0.94 g/l) as the source of nitrogen. High performance liquid chromatography, gas chromatography, and microplate reader were used to determine the glucose concentration, lipid production, and β-carotene concentration, respectively. Data were analyzed using IBM SPSS statistics (v. 24). Results: Cultures with a C/N ratio of 50 produced the greatest amount of lipids during 120 h pi. However, lipid synthesis in the first 48 h pi was very low for all three C/N ratios. Analyses of the lipid composition revealed that oleic acid and linoleic acid were the dominant (60%) fatty acids. Cultures with a C/N ratio of 50 also produced the greatest amount of β-carotene. Conclusion: R. diobovatum in the C/N of 50 culture medium resulted in greater concentrations of lipid and β-carotene. Defining the optimum C/N ratio will enable development of optimized bioprocess engineering parameters
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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) | 0.000 | 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 | 0.001 |
| 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".