Naguib Ibrahim A, Abdelaleem Eglal A, Hassan Eman S, Ali Nouruddin W, Gamal Mohammed
Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, Al-Hawiah, 21974 Taif, Saudi Arabia; Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Beni-Suef University, Alshaheed Shehata Ahmad Hegazy St., 62514 Beni-Suef, Egypt.
Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Beni-Suef University, Alshaheed Shehata Ahmad Hegazy St., 62514 Beni-Suef, Egypt.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Oct 5;239:118513. doi: 10.1016/j.saa.2020.118513. Epub 2020 May 21.
The aim of the presented work is to compare two popular chemometric methods which are partial least squares regression (PLSR) and support vector regression (SVR). The comparison shows their characteristics via application of the suggested methods to analysis of Norfloxacin (NF) and Tinidazole (TZ) with the presence of a potential impurity of Tinidazole; 2-Methyl-5-nitro-1H-imidazole (MNZ). For appropriate analysis, a 3 factor 4 level experimental design was constructed, which results in a training set composed of 16 mixtures which contains different concentrations of the three components; achieving symmetry, rotatability and orthogonality in mixture space. In order to validate the prediction ability of the suggested models, an independent test set consisting of 8 in-space and 8 out-of-space mixtures was used. The presented results show high specificity and accuracy of the mentioned multivariate calibration models for analysis of in-space samples of NF and TZ in presence of (MNZ) using UV spectral data. Statistical comparisons of predictive abilities of proposed models against classical least squares CLS model and against each other was performed; whether for analysis of test set mixtures or dosage form. CLS model showed lower predictive ability compared to other models. Results obtained by SVR model are as accurate as PLSR model, however, optimization and implementation of PLSR is faster and easier, hence PLSR could be of choice for this given case study. The developed chemometric models were validated as directed by ICH strategies. The validated methods were efficiently used for estimation of NF and TZ in pure powders and pharmaceuticals which indicates their suitability for application in quality control examination of both of the drugs.
本研究的目的是比较两种常用的化学计量学方法，即偏最小二乘回归（PLSR）和支持向量回归（SVR）。通过将所建议的方法应用于诺氟沙星（NF）和替硝唑（TZ）的分析，并存在替硝唑的潜在杂质2-甲基-5-硝基-1H-咪唑（MNZ），比较展示了它们的特性。为了进行适当的分析，构建了一个三因素四水平的实验设计，这产生了一个由16种混合物组成的训练集，其中包含三种组分的不同浓度；在混合物空间中实现了对称性、旋转性和正交性。为了验证所建议模型的预测能力，使用了一个由8个空间内和8个空间外混合物组成的独立测试集。所呈现的结果表明，使用紫外光谱数据，上述多元校准模型对存在（MNZ）的NF和TZ的空间内样品进行分析具有高特异性和准确性。对所提出模型与经典最小二乘CLS模型以及相互之间的预测能力进行了统计比较；无论是对于测试集混合物还是剂型的分析。与其他模型相比，CLS模型显示出较低的预测能力。SVR模型获得的结果与PLSR模型一样准确，然而，PLSR的优化和实施更快更容易，因此在这个给定的案例研究中，PLSR可能是首选。所开发的化学计量学模型按照ICH策略进行了验证。经过验证的方法有效地用于纯粉末和药物中NF和TZ的测定，这表明它们适用于这两种药物的质量控制检查。
