Identifying Chlorella vulgaris and Chlorella sorokiniana as sustainable organisms to bioconvert glucosamine into valuable biomass
Bibliographic record
Abstract
Chitin is a major component of various wastes such as crustacean shells, filamentous fungi, and insects. Recently, food-safe biological and chemical processes converting chitin to glucosamine have been developed. Here, we studied microalgae that can uptake glucosamine as vital carbon and nitrogen sources for valuable alternative protein biomass. Utilizing data mining and bioinformatics analysis, we identified 29 species that contain the required enzymes for glucosamine to glucose conversion. The growth performance of the selected strains was examined, and glucosamine was used in different forms and concentrations. Glucose at a concentration of 2.5 g/L was required to initiate glucosamine metabolic degradation by Chlorella vulgaris and Chlorella sorokiniana. Glucosamine HCl and glucosamine phosphate showed maximum cell counts of about 8.5 and 9.0 log/mL for C. sorokiniana and C. vulgaris in 14 days, respectively. Enzymatic hydrolysis of glucosamine increased growth performance with C. sorokiniana by about 3 folds. The adapted strains were fast-growing and could double their dry biomasses during the same incubation time. In addition, adapted C. sorokiniana was able to tolerate three times glucosamine concentration in the medium. The study illustrated possible strategies for employing C. sorokiniana and C. vulgaris to convert glucosamine into valuable biomass in a more sustainable way.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".