ArticleScientific reports2019
The application of Box-Behnken-Design in the optimization of HPLC separation of fluoroquinolones.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 103 citations in OpenAlex.
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- Condition optimization of eco-friendly RP-HPLC and MCR methods via Box-Behnken design and six sigma approach for detecting antibiotic residues.Scientific reports · 2023Article
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- The Chemometric Evaluation of the Factors Influencing Cloud Point Extraction for Fluoroquinolones.Pharmaceutics · 2023Article
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- Development of an HPLC-PDA Method for the Determination of Capsanthin, Zeaxanthin, Lutein, β-Cryptoxanthin and β-Carotene Simultaneously in Chili Peppers and Products.Molecules (Basel, Switzerland) · 2023Article
- Simultaneous determination of tramadol and paracetamol in human plasma using LC-MS/MS and application in bioequivalence study of -fixed-dose combination.Annals of medicine · 2023Article
- Stability and Antibiotic Potency Improvement of Levofloxacin by Producing New Salts with 2,6- and 3,5-Dihydroxybenzoic Acid and Their Comprehensive Structural Study.Pharmaceutics · 2022Article
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- Amphotericin B- and Levofloxacin-Loaded Chitosan Films for Potential Use in Antimicrobial Wound Dressings: Analytical Method Development and Its Application.Pharmaceutics · 2022Article
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Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Box-Behnken Design is a useful tool for the optimization of the chromatographic analysis. The goal of this study was to select the most significant factors that influenced the following parameters of the chromatographic separation: retention time, relative retention time, symmetry of the peaks, tailing factor, a number of theoretical plates, Foley - Dorsey parameter, resolution factor, peak width at half height. The results underwent the ANOVA test to find the statistically significant variables and interactions between them. The level of significance was for p < 0.05. The polynomial equations described quantitatively the statistically significant parameters and the interactions between them. The statistical analysis indicated both the best conditions for the separation of the compounds and the variables that were most influential for peaks' parameters. The four-factor analysis performed for LEVO and MOXI indicated that ACN, TEA and pH are the most significant factors that influence the separation. The analysis for the pair CIPRO and LEVO required six factors. The statistical analysis proved that the most significant factors are ACN, MeOH and TEA. In the separation of these two compounds on the HPLC column, the interaction ACN × MeOH was also significant.
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Registered trials
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