Green analytical methods for simultaneous determination of compounds having relatively disparate absorbance; application to antibiotic formulation of azithromycin and levofloxacin
Bibliographic record
Abstract
Green validated spectrophotometric methods are developed for simultaneous determination of Azithromycin (AZI) and Levofloxacin (LEVO) antibiotic mixture. Determination of AZI presents a real analytical challenge as its structure lacks any chromophore, and hence it cannot be determined by direct spectrophotometry. However, the reaction of AZI with perchloric acid produces a green product that can be accurately determined spectrophotometrically. Thus, the work presented demonstrates simple green and sensitive methods for the simultaneous determination of AZI and LEVO mixture. Method I depends on direct measurement of absorbance of azithromycin and levofloxacin in perchloric acid methanolic solution at 482 nm and 224 nm, respectively. While, Method II depends on measuring the first derivative spectrophotometric peak-to-peak amplitudes of AZI and LEVO in perchloric acid methanolic solution at 475-490 nm and 280-253 nm, respectively. Regression analysis shows good linearity for AZI and LEVO over the concentration ranges of 5-50 and 2.5-20 μg/mL for method I and 5-50 and 5-40 μg/mL for method II for AZI and LEVO, respectively. The proposed methods were validated in compliance with ICH guidelines. The suggested procedures are successfully applied for the assay of AZI and LEVO mixture in bulk powder and laboratory-prepared tablets. Greenness profile of the proposed methods were compared with other published methods through applying the Eco-scale protocol. Assessment results demonstrated that the proposed methods are greener than other reported methods. Moreover, upon comparison with other methods, the proposed methods showed better or comparable sensitivity in addition to being selective and rapid with no requirement for laborious extraction techniques. These advantages encourage the application of the proposed methods in routine analysis of AZI and LEVO in quality control laboratories as green and simple analytical tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".