Optimization of lipids production by Cryptococcus laurentii 11 using cheese whey with molasses
Bibliographic record
Abstract
This study aimed the optimization of culture condition and composition for production of Cryptococcus laurentii 11 biomass and lipids in cheese whey medium supplemented with sugarcane molasses. The optimization of pH, fermentation time, and molasses concentration according to a full factorial statistical experimental design was followed by a Plackett-Burman experimental design, which was used to determine whether the supplementation of the culture medium by yeast extract and inorganic salts could provide a further enhancement of lipids production. The following conditions and composition of the culture medium were found to optimize biomass and lipids production: 360 h fermentation, 6.5 pH and supplementation of (g L(-1)): 50 molasses, 0.5 yeast extract, 4 KH2PO4, 1 Na2HPO4, 0.75 MgSO4 · 7H2O and 0.002 ZnSO4 · H2O. Additional supplementation with inorganic salts and yeast extract was essential to optimize the production, in terms of product concentration and productivity, of neutral lipids by C. laurentii 11. Under this optimized condition, the production of total lipids increased by 133% in relation to control experiment (from 1.27 to 2.96 g L(-1)). The total lipids indicated a predominant (86%) presence of neutral lipids with high content of 16- and 18-carbon-chain saturated and monosaturated fatty acids. This class of lipids is considered especially suitable for the production of biodiesel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".