首页> 外文期刊>Analytical and bioanalytical chemistry >Determination of loratadine and pseudoephedrine sulfate in pharmaceuticals based on non-linear second-order spectrophotometric data generated by a pH-gradient flow injection technique and artificial neural networks
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Determination of loratadine and pseudoephedrine sulfate in pharmaceuticals based on non-linear second-order spectrophotometric data generated by a pH-gradient flow injection technique and artificial neural networks

机译:基于pH梯度流动注射和人工神经网络生成的非线性二阶分光光度法测定药物中的氯雷他定和硫酸伪麻黄碱

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摘要

Loratadine (LOR) and pseudoephedrine sulfate (PES) were determined in pharmaceutical samples by using non-linear second-order data generated by a pH-gradient flow injection analysis (FIA) system with diode-array detection. Determination of both analytes was performed on the basis of differences between the acid-base and spectral features of each drug species. Non-linearities were detected by using both qualitative and quantitative tools. As a consequence of the non-linearity, a recently reported algorithm, artificial neural networks followed by residual bilinearization (ANN/RBL), was shown to furnish more satisfactory results. Recoveries of 99.7% (LOR) and 95.6% (PES) were obtained when analyzing a validation set containing unexpected components (the usual excipients found in pharmaceutical preparations). The average value obtained by implementation of the method on four replicates was compared with that obtained by a reference method based on HPLC (difference not significant; p > 0.05).
机译:通过使用带有二极管阵列检测的pH梯度流动注射分析(FIA)系统生成的非线性二阶数据,确定了药物样品中的氯雷他定(LOR)和硫酸伪麻黄碱(PES)。两种分析物的测定是根据每种药物的酸碱和光谱特征之间的差异进行的。通过使用定性和定量工具检测非线性。由于非线性的结果,最近报告的一种算法,即人工神经网络和残差双线性化(ANN / RBL)被证明可以提供更令人满意的结果。当分析包含意外成分(在药物制剂中发现的常见赋形剂)的验证集时,可得到99.7%(LOR)和95.6%(PES)的回收率。将通过四次重复实施该方法获得的平均值与通过基于HPLC的参考方法获得的平均值进行比较(差异不显着; p> 0.05)。

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