• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of near infrared spectroscopy >Improvement of pharmaceutical near infrared calibrations by empirical target distribution optimisation
【24h】

Improvement of pharmaceutical near infrared calibrations by empirical target distribution optimisation

机译：通过经验目标分布优化改进药物近红外校准

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Using process samples to develop near infrared prediction models can generate artefacts in the calibration set, potentially reducing model performance. Due to the inherent variability of manufacturing processes, the assumption that all samples from a given batch have the same concentration for the compound of interest is incorrect. A tablet press will produce compacts with different drug concentrations, distributed around a nominal value, due to mechanical effects, vibrations and powder segregation that occur in the hopper. Differences in tablet crushing strength may also be observed for those same reasons. Consequently, using such samples and their associated nominal concentrations for the development of a calibration model for the prediction of content uniformity of tablets might produce models with rather high error while able to cope with a large range of process variability. The objective of this work was to investigate empirical approaches for optimising the target (reference) concentrations of prediction samples to best match the underlying spectral variance in an attempt to mitigate interferences that may be responsible for select portions of calibration error. Three approaches were developed. The first used samples presenting a low residual from an original model to re-predict high residual samples. The second approach was an iterative search of the best target value for each sample. The third method used target values generated from a normal distribution. These approaches were compared with the classical slope and bias correction methods on their ability to predict two independent validation sets. While several methods showed significant over-fitting and a high validation error, the iterative search routine enhanced calibration performance compared to post-regression correction methods and was proven to be a viable alternative to current industry practices.
机译：使用过程样本开发近红外预测模型可能会在校准集中生成伪影，从而可能降低模型性能。由于制造过程的固有可变性，假设来自给定批次的所有样品中所关注化合物的浓度相同的假设是错误的。由于料斗中发生的机械作用，振动和粉末分离，压片机将生产出具有不同药物浓度的粉饼，其分布在标称值附近。由于相同的原因，也可能会观察到片剂抗碎强度的差异。因此，使用此类样品及其相关的标称浓度开发用于预测片剂含量均一性的校准模型可能会产生具有相当高误差的模型，同时能够应对较大范围的工艺变异性。这项工作的目的是研究经验方法，以优化预测样本的目标（参考）浓度，使其与潜在的光谱方差最匹配，以尝试减轻可能造成校准误差选择部分的干扰。开发了三种方法。第一个使用的样本呈现出原始模型中的低残差，以重新预测高残差的样本。第二种方法是迭代搜索每个样本的最佳目标值。第三种方法使用从正态分布生成的目标值。将这些方法与经典的斜率和偏差校正方法相比，可以预测两个独立的验证集。尽管几种方法显示出明显的过拟合和高验证误差，但与回归后校正方法相比，迭代搜索例程增强了校准性能，并被证明是当前行业实践的可行替代方案。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号