4.4 Article

Multivariate Versus Classical Univariate Calibration Methods for Spectrofluorimetric Data: Application to Simultaneous Determination of Olmesartan Medoxamil and Amlodipine Besylate in their Combined Dosage Form

期刊

JOURNAL OF FLUORESCENCE
卷 23, 期 1, 页码 79-91

出版社

SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10895-012-1119-0

关键词

Olmesartan medoxamil; Amlodipine besylate; Spectrofluorimetry; Multivariate calibration methods; Pharmaceutical tablets

资金

  1. Research Center of the College of Pharmacy, King Saud University

向作者/读者索取更多资源

Olmesartan medoxamil (OLM, an angiotensin II receptor blocker) and amlodipine besylate (AML, a dihydropyridine calcium channel blocker), are co-formulated in a single-dose combination for the treatment of hypertensive patients whose blood pressure is not adequately controlled on either component monotherapy. In this work, four multivariate and two univariate calibration methods were applied for simultaneous spectrofluorimetric determination of OLM and AML in their combined pharmaceutical tablets in all ratios approved by FDA. The four multivariate methods are partial least squares (PLS), genetic algorithm PLS (GA-PLS), principal component ANN (PC-ANN) and GA-ANN. The two proposed univariate calibration methods are, direct spectrofluorimetric method for OLM and isoabsorpitive method for determination of total concentration of OLM and AML and hence AML by subtraction. The results showed the superiority of multivariate calibration methods over univariate ones for the analysis of the binary mixture. The optimum assay conditions were established and the proposed multivariate calibration methods were successfully applied for the assay of the two drugs in validation set and combined pharmaceutical tablets with excellent recoveries. No interference was observed from common pharmaceutical additives. The results were favorably compared with those obtained by a reference spectrophotometric method.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据