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Determination of Paracetamol and Orphenadrine Citrate in Pharmaceutical Tablets by Modeling of Spectrophotometric Data Using Partial Least-Squares and Artificial Neural Networks

机译:偏最小二乘和人工神经网络分光光度法建模测定药物片剂中的扑热息痛和柠檬酸吗啡

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摘要

The estimation of paracetamol and orphenadrine citrate in a multicomponent pharmaceutical dosage form by spectrophotometric method has been reported. Because of highly interference in the spectra and the presence of non-linearity caused by the analyte concentrations which deviate from Beer and Lambert's law, partial least-squares (PLS) and artificial neural networks (ANN) techniques were used for the calibration. A validation set of spiked samples was employed for testing the accuracy and precision of the methods. Reasonably good recoveries were obtained with PLS for paracetamol and the use of an ANN allowed the estimation of orphenadrine citrate, a minor component which could not be adequately modeled by PLS. Three production batches of a commercial sample were analysed, and there was statistically no significant difference (P<0.05) between the results with the proposed method and those obtain with the official comparative method.
机译:已经报道了通过分光光度法对多组分药物剂型中对乙酰氨基酚和柠檬酸奋乃静的估计。由于对光谱的高度干扰以及由偏离比尔和兰伯特定律的分析物浓度引起的非线性的存在,因此使用偏最小二乘(PLS)和人工神经网络(ANN)技术进行校准。使用加标样品的验证集来测试方法的准确性和精密度。用PLS可获得对乙酰氨基酚的合理良好回收率,并且使用ANN可以估算柠檬酸奋乃静碱,这是PLS无法适当建模的次要成分。分析了三个生产批次的商业样品,使用提议的方法与通过官方比较方法获得的结果在统计学上没有显着差异(P <0.05)。

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