Process optimization for effective bio‐decolourization of methyl orange by <i>Pseudomonas aeruginosa</i> 23N1 using chemometric methodology
Bibliographic record
Abstract
Development of low‐cost and efficient treatment processes to remove dyes from dye‐laden industrial wastewaters is currently an important topic of research. This study aims to investigate the bio‐decolouration of methyl orange (MO) dye by Pseudomonas aeruginosa 23N1 and to optimize the process parameters for efficient removal of MO. The regular two‐level factorial analysis technique is used to screen out the operational factors and to select their upper and lower limits for further optimization using central composite design (CCD). The result revealed that the peptone, yeast extract, and salt are the vital nutrients required by the bacteria to achieve high dye decolourization. Analysis of variance (ANOVA) in CCD indicates (correlation coefficient (R 2 ) as 0.999 with predicted R pre 2 = 0.996 and adjusted R adj 2 = 0.998) a strong correlation between the predicted response and the experimental response under identical experimental conditions. ANOVA in CCD exhibits high positive coefficients of estimate, 21.86 and 17.75, for dye and yeast extract concentration, respectively, suggests their major influences on decolourization performance. Higher decolourization is achieved at higher initial dye concentration due to the utilization of dye molecules as a preferable food supplement for bacteria. Model validation analyses show 98.23 ±0.4 %, and 98.60 ±0.5 % decolourization for initial 50 and 150 mg/L dye solution, respectively, which are in agreement with the model predicted dye decolourization percentages of 98.44 and 98.34 %, respectively. The disappearance of the maximum absorption peak of the MO dye in the UV‐visible spectra indicates that decolourization of dye is either through the bioaccumulation or biodegradation mechanism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".