ArticleBMC chemistry2024
Simultaneously quantifying a novel five-component anti- migraine formulation containing ergotamine, propyphenazone, caffeine, camylofin, and mecloxamine using UV spectrophotometry and chemometric models.
Article in BMC chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fedorov algorithm-optimized chemometric spectrophotometry for cefepime-tazobactam microanalysis in plasma and pharmaceuticals with integrated MA and NQS sustainability assessment.Scientific reports · 2026Article
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- Chemometric-assisted UV-Vis spectrophotometric method for the simultaneous quantification of triple anthelmintics in veterinary pharmaceuticals.Scientific reports · 2025Article
- Sustainable Fluorescence-off Based Analytical Approach for the Quantification of Linagliptin Using a Biocompatible Sensor; Erythrosine B Dye.Journal of fluorescence · 2025Article
- Univariate and multivariate signal processing spectrophotometric determination of an antihypertensive combination in line with the United Nations sustainable development goals.Scientific reports · 2025Article
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- Multivariate classical least squares-based model for spectrophotometric determination of celecoxib and Tramadol in their new formulated dosage form.BMC chemistry · 2025Article
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- Developing and Validating a Robust RP-HPLC Method for Metoclopramide and Camylofin Simultaneous Analysis Using Response Surface Methodology.International journal of analytical chemistry · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
This study presents a new method for simultaneously quantifying a complex anti-migraine formulation containing five components (ergotamine, propyphenazone, caffeine, camylofin, and mecloxamine) using UV spectrophotometry and chemometric models. The formulation presents analytical challenges due to the wide variation in component concentrations (ERG: PRO: CAF: CAM: MEC ratio of 0.075:20:8:5:4) and highly overlapping UV spectra. To create a comprehensive validation dataset, the Kennard-Stone Clustering Algorithm was used to address the limitations of arbitrary data partitioning in chemometric methods. Three different chemometric models were evaluated: Classical Least Squares (CLS), Partial Least Squares (PLS), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Among these, MCR-ALS demonstrated excellent performance, achieving recovery values of 98-102% for all components, accompanied by minimal root mean square errors of calibration (0.072-0.378) and prediction (0.077-0.404). Moreover, the model exhibited high accuracy, with relative errors ranging from 1.936 to 3.121%, bias-corrected mean square errors between 0.074 and 0.389, and a good sensitivity (0.2097-1.2898 μg mL
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.