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Record W3084774803 · doi:10.1016/j.heliyon.2020.e04819

Green analytical methods for simultaneous determination of compounds having relatively disparate absorbance; application to antibiotic formulation of azithromycin and levofloxacin

2020· article· en· W3084774803 on OpenAlexaff
Amira F. El‐Yazbi, Essam F. Khamis, Rasha M. Youssef, M. Abdel-Hady Elsayed, Faten M. Aboukhalil

Bibliographic record

VenueHeliyon · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAzithromycinLevofloxacinAntibioticsAbsorbanceChromatographyChemistryMicrobiologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.017
GPT teacher head0.298
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations35
Published2020
Admission routes1
Has abstractyes

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