Department of Analytical Chemistry, Faculty of Chemistry, Islamic Azad University, North Tehran Branch, P.O. Box: 1913674711, Tehran, Iran.
Department of Analytical Chemistry, Faculty of Chemistry, Islamic Azad University, North Tehran Branch, P.O. Box: 1913674711, Tehran, Iran.
Spectrochim Acta A Mol Biomol Spectrosc. 2018 Apr 5;194:141-151. doi: 10.1016/j.saa.2018.01.011. Epub 2018 Jan 6.
Partial least squares (PLS), different families of continuous wavelet transform (CWT), and first derivative spectrophotometry (DS) techniques were studied for quantification of Sofosbuvir (SFB) and Ledipasvir (LDV) simultaneously without separation step. The components were dissolved in Acetonitrile and the spectral behaviors were evaluated in the range of 200 to 400nm. The ultraviolet (UV) absorbance of LDV exhibits no interferences between 300 and 400nm and it was decided to predict the LDV amount through the classic spectrophotometry (CS) method in this spectral region as well. Data matrix of concentrations and calibrated models were developed, and then by applying a validation set the accuracy and precision of each model were studied. Actual concentrations versus predicted concentrations plotted and good correlation coefficients by each method resulted. Pharmaceutical dosage form was quantified by developed methods and the results were compared with the High Performance Liquid Chromatography (HPLC) reference method. Analysis Of Variance (ANOVA) in 95% confidence level showed no significant differences among methods.
偏最小二乘法 (PLS)、连续小波变换 (CWT) 的不同家族以及一阶导数分光光度法 (DS) 技术被研究用于定量分析 Sofosbuvir (SFB) 和 Ledipasvir (LDV)，无需分离步骤。将成分溶解在乙腈中，并在 200 到 400nm 范围内评估光谱行为。LDV 的紫外 (UV) 吸光度在 300 到 400nm 之间没有干扰，因此决定在此光谱区域通过经典分光光度法 (CS) 方法预测 LDV 的含量。建立了浓度数据矩阵和校准模型，然后通过应用验证集研究了每种模型的准确性和精密度。实际浓度与预测浓度的绘图以及每种方法的良好相关系数。通过开发的方法对药物剂型进行定量分析，并将结果与高效液相色谱 (HPLC) 参考方法进行比较。在 95%置信水平下的方差分析 (ANOVA) 表明方法之间没有显著差异。
