单变量
阈值
拉曼光谱
咖啡因
主成分分析
多元统计
人工智能
分析化学(期刊)
生物系统
化学
模式识别(心理学)
数学
计算机科学
统计
色谱法
光学
图像(数学)
物理
医学
内分泌学
生物
作者
Slobodan Šašić,Timothy Prusnick
标识
DOI:10.1016/j.ijpharm.2019.05.004
摘要
A curved area with embossment on an analgesic tablet with three APIs is imaged with a Raman instrument equipped with hardware options that allow for continuous focus adjustment and very fast acquisition of micro-Raman spectra, about sixty spectra per second in this study. Univariate, self-modelling curve resolution, and regression derived images via the pure component spectra are obtained for all three APIs. The quality of the images and reasons for thresholding are discussed, as well as the relation between the histograms of the images and respective thresholds. The univariate image of the API of lowest concentration (caffeine) is found to be most affected by the Raman signal of the more abundant components, which is much less noticeable in the multivariate images. Particle size analysis is conducted on the thresholded caffeine images only, as the other two components are too abundant for such type of analysis. Depending on the method used for imaging, the area coverage for caffeine varies from 1% in univariate to 4% in regression images.
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