Determination of ingredients in packaged pharmaceutical tablets by energy dispersive X-ray diffraction and maximum likelihood principal component analysis multivariate curve resolution-alternating least squares with correlation constraint
Determination of ingredients in packaged pharmaceutical tablets by energy dispersive X-ray diffraction and maximum likelihood principal component analysis multivariate curve resolution-alternating least squares with correlation constraint
复制标题
通过能量色散 X 射线衍射和最大似然主成分分析多元曲线分辨率-具有相关约束的交替最小二乘法测定包装药片中的成分
DOI：
10.1002/cem.3329
复制
发表时间：
2021
影响因子：
2.4
通讯作者：
Kenny P
中科院分区：
文献类型：
--
作者：
Kenny P
使用能量色散X射线衍射（EDXRD）和最大似然主成分分析（MLPCA-MCR-ALS）以及相关性约束对包装药物制剂的组成进行定量。记录的EDXRD配置文件从未包装和包装的三元混合物的样品一起建模，以恢复的浓度以及纯配置文件的组成化合物。MLPCA被用作MCR-ALS的数据预处理步骤，解释了EDXRD图谱中观察到的高噪声和非恒定方差，并被证明可以提高MCR-ALS对数据集的分辨率准确度。在MCR-ALS程序中应用了局部相关性约束，以便同时对未包装和包装样品进行建模，同时考虑包装材料的基质效应。估计制剂的组成，每种组分（包括对乙酰氨基酚）的预测均方根误差约为2.5% w/w（未包装和包装样品）。同时解析未包装和包装样品的对乙酰氨基酚浓度，其准确度高于单独建模时通过偏最小二乘回归（PLSR）获得的准确度。通过对包装的影响进行建模并将未包装样品的准确参考信息纳入包装样品的分辨率中，EDXRD和MLPCA‐MCR‐ALS在无损筛选和假药检测中用于包装固体剂量药物的鉴定和定量的潜力得到了提高。
Energy dispersive X‐ray diffraction (EDXRD) and maximum likelihood principal component analysis multivariate curve resolution‐alternating least squares (MLPCA‐MCR‐ALS) with correlation constraint were used to quantify the composition of packaged pharmaceutical formulations. Recorded EDXRD profiles from unpackaged and packaged samples of ternary mixtures were modelled together in order to recover the concentrations as well as the pure profiles of the constituent compounds. MLPCA was used as a data pretreatment step to MCR‐ALS, accounting for the high noise and nonconstant variance observed in the EDXRD profiles and was shown to improve the resolution accuracy of MCR‐ALS for the data set. Local correlation constraints were applied in the MCR‐ALS procedure in order to model unpackaged and packaged samples simultaneously while accounting for the matrix effect of the packaging materials. The composition of the formulations was estimated with root‐mean‐square error of prediction for each component, including paracetamol, being approximately 2.5 %w/w for unpackaged and packaged samples. Paracetamol concentration was resolved simultaneously for the unpackaged and packaged samples to a greater degree of accuracy than achieved by partial least squares regression (PLSR) when modelling the contexts separately. By modelling the effects of the packaging and incorporating accurate reference information of unpackaged samples into the resolution of packaged samples, the potential of EDXRD and MLPCA‐MCR‐ALS for the identification and quantification of packaged solid‐dosage medicine in nondestructive screening and counterfeit medicine detection has been raised.