Elucidating the effect of impurities present in different crude glycerol sources on lipid and citric acid production by <i>Yarrowia lipolytica</i><scp>SKY7</scp>
Bibliographic record
Abstract
Abstract BACKGROUND Crude glycerol is an industrial by‐product of biodiesel producing companies and requires a high cost for purification. In fact, it is a good carbon source and can be used for lipid production from oleaginous microbes. However, crude glycerol has several impurities that may impact the cellular metabolism for lipid production. RESULTS In this study, crude glycerol from different sources was employed for lipid production and the effect of its impurities on the biomass and lipid production was investigated on Yarrowia lipolytica SKY7, which is a well‐known yeast for lipid and citric acid production. Growth inhibition was observed in BIOCARDEL, BIOLIQ, and ROTHSAY glycerol when compared with pure glycerol. This was due to high sodium concentrations in BIOCARDEL, high potassium concentrations in BIOLIQ, and high sulphur concentrations in ROTHSAY glycerol. Among three crude glycerol sources, the highest lipid concentration (14.78 g L−1) was obtained using BIOCARDEL glycerol at 96 h. However, the higher citric acid concentrations of 18.70 g L−1 in ROTHSAY glycerol and 12.00 g L−1 in BIOLIQ were obtained at 96 h when compared with 8.30 g L−1 in BIOCARDEL glycerol. CONCLUSION A high potassium and sulphur concentration in glycerol medium inhibits cell growth and lipid production in Yarrowia lipolytica SKY7, while it favors citric acid production. © 2020 Society of Chemical Industry
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".