A Taguchi design approach for the enhancement of a <scp>detergent‐biocompatible</scp> alkaline thermostable protease production by <i>Streptomyces mutabilis</i> strain <scp>TN‐X30</scp>
Bibliographic record
Abstract
Abstract The ability of microorganisms to grow at high temperature, alkaline pH, and high salinity makes them an attractive target for enzyme‐production with several industrial applications. One strain TN‐X30 has been selected as protease producer and identified as Streptomyces mutabilis after a phenotypic and molecular study. Its production of protease was improved using Taguchi L27 design. The strategy was carried out to identify the optimum levels and the interaction of the screened factors. Following this step, maximum protease activity (10,895 U/ml) was achieved after 6‐days of incubation. The TN‐X30 protease activity had an optimum of pH and temperature of 10 and 65°C, respectively. Thermodynamic parameters at 60°C were enthalpy 14.26 kJ/mol, entropy −220 J/mol/K, and Gibbs free energy 90.53 kJ/mol. TN‐X30 protease production displayed a 16‐fold increase reaching 175,000 U/ml in a 100‐L fermentor. Furthermore, the lyophilization in presence of sorbitol enhanced the stability of the TN‐X30 protease which remained active at 75% after 24‐months of storage. The lyophilized TN‐X30 protease exhibited exceptional stability indexes in presence of some known commercialized detergent components as NEODOL® 25‐7, Dehydol® LT 7, Na2 CMC, Galaxy LAS, Galaxy LES 70, Galaxy 110, Galaxy CAPB Plus, and Sulfacid K. The lyophilized enzyme also displayed high stability with respect to both solid and liquid detergents. Finally, TN‐X30 protease exhibited remarkable destaining of blood, egg, and chocolate stained cloth pieces. These findings may promote TN‐X30 protease for use as bioadditive in detergent formulation, thereby reducing environmental chemical threat.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".