Agatonovic-Kustrin S, Wu V, Rades T, Saville D, Tucker I G
School of Pharmacy, University of Otago, Dunedin, New Zealand.
J Pharm Biomed Anal. 2000 Jul;22(6):985-92. doi: 10.1016/s0731-7085(00)00256-9.
A simple X-ray powder diffractometric (XRD) method with artificial neural networks (ANNs) for data modelling was developed to recognize and quantify two crystal modifications of ranitidine HCl in mixtures and thus, provide information about the solid state of the bulk drug. The method was also used to quantify ranitidine HCl from tablets in the presence of other components. An ANN consisting of three layers of neurons was trained by using a back-propagation learning rule. A sigmoid output function was used in the hidden layer to facilitate non-linear fitting. Unlike other techniques the ANN method described here employed pattern recognition on the entire XRD pattern. Correct classification was mainly influenced by the XRD pattern resolution. It was shown that data transformations improved the quantitative performance when the XRD patterns were not contaminated by other components. Only smoothed X-ray diffractograms were required to distinguish between the two crystalline forms in a mixture. In the case of ranitidine-HCl quantification from tablets, where significant interference with tablet excipients was present, better results were obtained without data transformations. The trained ANN perfectly quantified ranitidine HCI polymorphic forms from mixtures (mean sum of squared error was less than 0.02%) and ranitidine HCl form 1 from tablets (recovery = 98.65). Excellent quantification performance of the ANN analysis. demonstrated in this study, serves as an indication of the broad potential of neural networks in pattern analysis. While the system described has been developed to interpret XRD patterns, peak detection has implications in every chemical application where the recognition of peak-shaped signals in analytical data is important.
开发了一种结合人工神经网络（ANN）进行数据建模的简单X射线粉末衍射（XRD）方法，用于识别和定量混合物中盐酸雷尼替丁的两种晶型，从而提供有关原料药固态的信息。该方法还用于在存在其他成分的情况下定量片剂中的盐酸雷尼替丁。使用反向传播学习规则训练了一个由三层神经元组成的人工神经网络。在隐藏层中使用了Sigmoid输出函数以促进非线性拟合。与其他技术不同，此处描述的人工神经网络方法对整个XRD图谱进行模式识别。正确分类主要受XRD图谱分辨率的影响。结果表明，当XRD图谱未被其他成分污染时，数据转换可提高定量性能。仅需平滑的X射线衍射图即可区分混合物中的两种晶型。在定量片剂中盐酸雷尼替丁的情况下，由于存在对片剂辅料的显著干扰，在不进行数据转换的情况下可获得更好的结果。训练后的人工神经网络能够完美地定量混合物中盐酸雷尼替丁的多晶型物（均方误差总和小于0.02%）以及片剂中的盐酸雷尼替丁晶型1（回收率=98.65）。本研究中展示的人工神经网络分析的出色定量性能，表明了神经网络在模式分析中的广泛潜力。虽然所描述的系统是为解释XRD图谱而开发的，但峰检测在分析数据中峰形信号识别很重要的每个化学应用中都有意义。
