Ankara University, Faculty of Pharmacy, Department of Analytical Chemistry, Tandoğan, 06100 Ankara, Turkey.
Ankara University, Faculty of Pharmacy, Department of Analytical Chemistry, Tandoğan, 06100 Ankara, Turkey.
Spectrochim Acta A Mol Biomol Spectrosc. 2017 Sep 5;184:255-261. doi: 10.1016/j.saa.2017.04.081. Epub 2017 Apr 30.
In this study, excitation-emission matrix datasets, which have strong overlapping bands, were processed by using four different chemometric calibration algorithms consisting of parallel factor analysis, Tucker3, three-way partial least squares and unfolded partial least squares for the simultaneous quantitative estimation of valsartan and amlodipine besylate in tablets. In analyses, preliminary separation step was not used before the application of parallel factor analysis Tucker3, three-way partial least squares and unfolded partial least squares approaches for the analysis of the related drug substances in samples. Three-way excitation-emission matrix data array was obtained by concatenating excitation-emission matrices of the calibration set, validation set, and commercial tablet samples. The excitation-emission matrix data array was used to get parallel factor analysis, Tucker3, three-way partial least squares and unfolded partial least squares calibrations and to predict the amounts of valsartan and amlodipine besylate in samples. For all the methods, calibration and prediction of valsartan and amlodipine besylate were performed in the working concentration ranges of 0.25-4.50μg/mL. The validity and the performance of all the proposed methods were checked by using the validation parameters. From the analysis results, it was concluded that the described two-way and three-way algorithmic methods were very useful for the simultaneous quantitative resolution and routine analysis of the related drug substances in marketed samples.
在这项研究中，采用了四种不同的化学计量校准算法，包括平行因子分析、 Tucker3、三向偏最小二乘法和非展开偏最小二乘法，对具有强重叠带的激发-发射矩阵数据集进行处理，以便同时定量估计片剂中的缬沙坦和氨氯地平。在分析中，在应用平行因子分析 Tucker3、三向偏最小二乘法和非展开偏最小二乘法方法分析样品中的相关药物物质之前，没有进行初步分离步骤。通过将校准集、验证集和商业片剂样品的激发-发射矩阵进行串联，获得三向激发-发射矩阵数据数组。使用激发-发射矩阵数据数组获得平行因子分析、 Tucker3、三向偏最小二乘法和非展开偏最小二乘法校准，并预测样品中缬沙坦和氨氯地平的含量。对于所有方法，在 0.25-4.50μg/mL 的工作浓度范围内对缬沙坦和氨氯地平进行了校准和预测。通过验证参数检查了所有建议方法的有效性和性能。从分析结果得出结论，所描述的双向和三向算法方法对于市场样品中相关药物物质的同时定量解析和常规分析非常有用。
