Application of reverse engineering in the field of pharmaceutical tablets using Raman mapping and chemometrics

化学计量学 主成分分析 线性判别分析 拉曼光谱 化学 化学成像 生物系统 活性成分 分析化学(期刊) 模式识别(心理学) 人工智能 色谱法 计算机科学 光学 高光谱成像 生物信息学 物理 生物
作者
Tereza Čapková,Tomáš Pekárek,Barbora Hanulíková,Pavel Matějka
出处
期刊:Journal of Pharmaceutical and Biomedical Analysis [Elsevier BV]
卷期号:209: 114496-114496 被引量:16
标识
DOI:10.1016/j.jpba.2021.114496
摘要

Raman micro-spectroscopy technique offers a combination of relatively high spatial resolution with identification of components or mixtures of components in different sample areas, e.g. on the surface or the cross-section of a sample. This study is focused on the analysis of the tablets from pharmaceutical development with different technological parameters: (1) the manufacturing technology, (2) the particle size of the input API (active pharmaceutical ingredient) and (3) the quantitative composition of the individual excipients. These three mentioned parameters represent the most frequently solved problems in the field of reverse engineering in pharmacy. The investigation aims to distinguish tablets with the above-described technological parameters with limited subjective steps by Raman microscopy. Furthermore, non-subjective methods of Raman data analysis using advanced statistical analysis have been proposed, namely Principal Component Analysis, Soft Independent Modelling of Class Analogy and Linear Discriminant Analysis. The methods successfully distinguished and identified even very small differences in the analysed tablets within our study and provided objective statistic evaluation of Raman maps. The information on component and particle size distribution including their small differences, which is the critical parameter in the development of the original and generic products, was obtained due to combination of these methods. Even though each of these chemometric methods evaluates the data set from a different perspective, their mutual application on the problem of Raman maps evaluation confirmed and specified results on level that would be unattainable with the use of only one them.
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