Multi-Scale Biosurfactant Production by Bacillus subtilis Using Tuna Fish Waste as Substrate
Bibliographic record
Abstract
As one of the most effective biosurfactants reported to date, lipopeptides exhibit attractive surface and biological activities and have the great potential to serve as biocatalysts. Low yield, high cost of production, and purification hinder the large-scale applications of lipopeptides. Utilization of waste materials as low-cost substrates for the growth of biosurfactant producers has emerged as a feasible solution for economical biosurfactant production. In this study, fish peptone was generated through enzyme hydrolyzation of smashed tuna (Katsuwonus pelamis). Biosurfactant (mainly surfactin) production by Bacillus subtilis ATCC 21332 was further evaluated and optimized using the generated fish peptone as a comprehensive substrate. The optimized production conduction was continuously assessed in a 7 L batch-scale and 100 L pilot-scale fermenter, exploring the possibility for a large-scale surfactin production. The results showed that Bacillus subtilis ATCC 21332 could effectively use the fish waste peptones for surfactin production. The highest surfactin productivity achieved in the pilot-scale experiments was 274 mg/L. The experimental results shed light on the further production of surfactins at scales using fish wastes as an economical substrate.
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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 | 0.001 |
| 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".