Adhiparasakthi College of Pharmacy, Melmaruvathur-603319, India.
Acta Pharm. 2011 Sep 1;61(3):283-96. doi: 10.2478/v10007-011-0027-1.
In the present work, three different spectrophotometric methods for simultaneous estimation of ramipril, aspirin and atorvastatin calcium in raw materials and in formulations are described. Overlapped data was quantitatively resolved by using chemometric methods, viz. inverse least squares (ILS), principal component regression (PCR) and partial least squares (PLS). Calibrations were constructed using the absorption data matrix corresponding to the concentration data matrix. The linearity range was found to be 1-5, 10-50 and 2-10 μg mL-1 for ramipril, aspirin and atorvastatin calcium, respectively. The absorbance matrix was obtained by measuring the zero-order absorbance in the wavelength range between 210 and 320 nm. A training set design of the concentration data corresponding to the ramipril, aspirin and atorvastatin calcium mixtures was organized statistically to maximize the information content from the spectra and to minimize the error of multivariate calibrations. By applying the respective algorithms for PLS 1, PCR and ILS to the measured spectra of the calibration set, a suitable model was obtained. This model was selected on the basis of RMSECV and RMSEP values. The same was applied to the prediction set and capsule formulation. Mean recoveries of the commercial formulation set together with the figures of merit (calibration sensitivity, selectivity, limit of detection, limit of quantification and analytical sensitivity) were estimated. Validity of the proposed approaches was successfully assessed for analyses of drugs in the various prepared physical mixtures and formulations.
在本工作中,描述了三种不同的分光光度法,用于同时测定原料药和制剂中雷米普利、阿司匹林和阿托伐他汀钙。使用化学计量学方法(如逆最小二乘法(ILS)、主成分回归(PCR)和偏最小二乘法(PLS))定量解析重叠数据。校准是使用与浓度数据矩阵相对应的吸收数据矩阵构建的。雷米普利、阿司匹林和阿托伐他汀钙的线性范围分别为 1-5、10-50 和 2-10μg/mL。通过在 210 至 320nm 的波长范围内测量零阶吸光度,获得吸光度矩阵。根据雷米普利、阿司匹林和阿托伐他汀钙混合物的浓度数据,采用统计学方法设计训练集,以最大限度地提高光谱的信息量并最小化多元校准的误差。通过将各自的 PLS1、PCR 和 ILS 算法应用于校准集的测量光谱,获得了合适的模型。该模型是基于 RMSECV 和 RMSEP 值选择的。将相同的模型应用于预测集和胶囊制剂。同时还估计了商业制剂组的平均回收率以及(校准灵敏度、选择性、检测限、定量限和分析灵敏度)等数值。该方法在各种制备的物理混合物和制剂中分析药物时的有效性得到了成功评估。