University of Hawaii at Hilo, College of Pharmacy, 34 Rainbow Drive, Hilo, HI 96720, USA.
J Pharm Biomed Anal. 2011 Dec 15;56(5):944-9. doi: 10.1016/j.jpba.2011.08.018. Epub 2011 Aug 17.
Fusing complex data from two disparate sources has been demonstrated to improve the accuracy in quantifying active ingredients in mixtures of pharmaceutical powders. A four-component simplex-centroid design was used to prepare blended powder mixtures of acetaminophen, caffeine, aspirin and ibuprofen. The blends were analyzed by Fourier transform infra-red spectroscopy (FTIR) and powder X-ray diffraction (PXRD). The FTIR and PXRD data were preprocessed and combined using two different data fusion methods: fusion of preprocessed data (FPD) and fusion of principal component scores (FPCS). A partial least square (PLS) model built on the FPD did not improve the root mean square error of prediction. However, a PLS model built on the FPCS yielded better accuracy prediction than PLS models built on individual FTIR and PXRD data sets. The improvement in prediction accuracy of the FPCS may be attributed to the removal of noise and data reduction associated with using PCA as a preprocessing tool. The present approach demonstrates the usefulness of data fusion for the information management of large data sets from disparate sources.
将来自两个不同来源的复杂数据融合已经被证明可以提高定量分析药物粉末混合物中有效成分的准确性。本研究采用四元单纯形-质心法设计制备对乙酰氨基酚、咖啡因、阿司匹林和布洛芬的混合粉末。使用傅里叶变换红外光谱（FTIR）和粉末 X 射线衍射（PXRD）对混合物进行分析。FTIR 和 PXRD 数据经过预处理，并使用两种不同的数据融合方法进行组合：预处理数据融合（FPD）和主成分得分融合（FPCS）。基于 FPD 构建的偏最小二乘（PLS）模型并未提高预测均方根误差。然而，基于 FPCS 构建的 PLS 模型比基于单个 FTIR 和 PXRD 数据集构建的 PLS 模型具有更好的准确性预测。FPCS 的预测准确性提高可能归因于使用 PCA 作为预处理工具时，噪声的去除和数据的减少。本研究方法证明了数据融合在处理来自不同来源的大型数据集的信息管理方面的有用性。
