A novel Method for the selective recovery and purification of γ‐polyglutamic acid from <i>Bacillus licheniformis</i> fermentation broth
Bibliographic record
Abstract
Microbially produced gamma-polyglutamic acid (gamma-PGA) is a commercially important biopolymer with many applications in biopharmaceutical, food, cosmetic and waste-water treatment industries. Owing to its increasing demand in various industries, production of gamma-PGA is well documented in the literature, however very few methods have been reported for its recovery. In this paper, we report a novel method for the selective recovery and purification of gamma-PGA from cell-free fermentation broth of Bacillus licheniformis. The cell-free fermentation broth was treated with divalent copper ions, resulting in the precipitation of gamma-PGA, which was collected as a pellet by centrifugation. The pellet was resolubilized and dialyzed against de-ionized water to obtain the purified gamma-PGA biopolymer. The efficiency and selectivity of gamma-PGA recovery was compared with ethanol precipitation method. We found that 85% of the original gamma-PGA content in the broth was recovered by copper sulfate-induced precipitation, compared to 82% recovery by ethanol precipitation method. Since ethanol is a commonly used solvent for protein precipitation, the purity of gamma-PGA precipitate was analyzed by measuring proteins that co-precipitated with gamma-PGA. Of the total proteins present in the broth, 48% proteins were found to be co-precipitated with gamma-PGA by ethanol precipitation, whereas in copper sulfate-induced precipitation, only 3% of proteins were detected in the final purified gamma-PGA, suggesting that copper sulfate-induced precipitation offers better selectivity than ethanol precipitation method. Total metal content analysis of the purified gamma-PGA revealed the undetectable amount of copper ions, whereas other metal ions detected were in low concentration range. The purified gamma-PGA was characterized using infrared spectroscopy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".