Near infrared (NIR) spectroscopy is a well-established method for analysis of pharmaceutical products, and especially useful for process monitoring and control of continuous production due to high sample throughput. In this work, a previously established method called empirical target distribution optimization (ETDO) wherein reference sample values using information from model prediction of the calibration data was used as a tool to improve the performance of NIR partial least squares (PLS) models. Model performance was assessed using root mean square error (R2), bias and accuracy in prediction of test samples. A target value selection threshold was tested to assess the ETDO procedure for NIR analysis of powder samples. The amount of specific variation captured by the model was examined and compared for models calibrated with and without ETDO. The results reported in this work suggests that PLS models optimized with ETDO of reference values can provide more specific PLS models for NIR analysis for complex powder mixtures. In addition, the model optimization method could also be applied as a tool to verify the necessary amount of PLS components to produce robust models. The ETDO method presented in this work is an approach that could be applied in the development of continuous blending or tableting processes where robust in-line quantitative analysis of powder samples is needed.

中文翻译：

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通过经验目标分布优化（ETDO）对药物粉末进行近红外分析。

近红外（NIR）光谱法是一种用于分析药品的公认方法，由于样品通量高，因此特别适用于过程监控和连续生产的控制。在这项工作中，以前建立的一种称为经验目标分布优化（ETDO）的方法，其中使用来自校准数据的模型预测的信息作为参考样本值，用作改善NIR偏最小二乘（PLS）模型性能的工具。使用均方根误差（R2），偏差和测试样本预测的准确性评估模型性能。测试了目标值选择阈值，以评估用于粉末样品NIR分析的ETDO程序。检查了模型捕获的特定变异量，并比较了使用ETDO和不使用ETDO校准的模型。这项工作报告的结果表明，用参考值ETDO优化的PLS模型可以为复杂粉末混合物的NIR分析提供更具体的PLS模型。另外，模型优化方法还可以用作验证必要数量的PLS组件以生成鲁棒模型的工具。这项工作中介绍的ETDO方法可用于需要对粉末样品进行可靠的在线定量分析的连续混合或压片工艺的开发中。模型优化方法也可以用作验证必要数量的PLS组件以生成可靠模型的工具。这项工作中介绍的ETDO方法可用于需要对粉末样品进行可靠的在线定量分析的连续混合或压片工艺的开发中。模型优化方法也可以用作验证必要数量的PLS组件以生成可靠模型的工具。这项工作中介绍的ETDO方法可用于需要对粉末样品进行可靠的在线定量分析的连续混合或压片工艺的开发中。