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首页> 外文期刊>Spectrochimica acta, Part A. Molecular and biomolecular spectroscopy >Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: A comparative study
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Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: A comparative study

机译：支持向量回归和人工神经网络模型用于药物制剂中盐酸美贝维林和舒必利混合物的稳定性指示分析的比较研究

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A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations. In this paper we compare SVR to ANN with and without variable selection procedure (genetic algorithm (GA)). To project the comparison in a sensible way, the methods are used for the stability indicating quantitative analysis of mixtures of mebeverine hydrochloride and sulpiride in binary mixtures as a case study in presence of their reported impurities and degradation products (summing up to 6 components) in raw materials and pharmaceutical dosage form via handling the UV spectral data. For proper analysis, a 6 factor 5 level experimental design was established resulting in a training set of 25 mixtures containing different ratios of the interfering species. An independent test set consisting of 5 mixtures was used to validate the prediction ability of the suggested models. The proposed methods (linear SVR (without GA) and linear GA-ANN) were successfully applied to the analysis of pharmaceutical tablets containing mebeverine hydrochloride and sulpiride mixtures. The results manifest the problem of nonlinearity and how models like the SVR and ANN can handle it. The methods indicate the ability of the mentioned multivariate calibration models to deconvolute the highly overlapped UV spectra of the 6 components' mixtures, yet using cheap and easy to handle instruments like the UV spectrophotometer.
机译：建立了支持向量回归（SVR）与人工神经网络（ANN）多元回归方法之间的比较，显示了每种方法的基础算法，并进行了比较以表明其固有的优点和局限性。在本文中，我们比较了有无选择程序（遗传算法（GA））的SVR与ANN。为了以合理的方式进行比较，使用该方法进行稳定性分析，以定量分析盐酸美贝维林和舒必利混合液在二元混合物中的存在，作为案例研究存在其报告的杂质和降解产物（最多6个组分）的情况。原料和药物剂型，通过处理紫外线光谱数据。为了进行适当的分析，建立了6因子5级实验设计，得出了25种混合物的训练集，其中包含不同比例的干扰物质。由5种混合物组成的独立测试集用于验证建议模型的预测能力。所提出的方法（线性SVR（无GA）和线性GA-ANN）已成功地用于分析含有盐酸美贝维林和舒必利混合物的药物片剂。结果表明了非线性问题以及像SVR和ANN这样的模型如何处理它。这些方法表明了上述多元校准模型能够解卷积6种组分混合物的高度重叠的UV光谱的能力，但仍使用便宜且易于操作的仪器（例如UV分光光度计）进行了卷积。

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