Coic Laureen, Sacré Pierre-Yves, Dispas Amandine, De Bleye Charlotte, Fillet Marianne, Ruckebusch Cyril, Hubert Philippe, Ziemons Eric
University of Liege (ULiege), CIRM, Vibra-Santé Hub, Laboratory of Pharmaceutical Analytical Chemistry, Avenue Hippocrate 15, 4000, Liege, Belgium.
University of Liege (ULiege), CIRM, Vibra-Santé Hub, Laboratory of Pharmaceutical Analytical Chemistry, Avenue Hippocrate 15, 4000, Liege, Belgium.
Anal Chim Acta. 2021 Apr 22;1155:338361. doi: 10.1016/j.aca.2021.338361. Epub 2021 Feb 28.
Hyperspectral imaging has been widely used for different kinds of applications and many chemometric tools have been developed to help identifying chemical compounds. However, most of those tools rely on factorial decomposition techniques that can be challenging for large data sets and/or in the presence of minor compounds. The present study proposes a pixel-based identification (PBI) approach that allows readily identifying spectral signatures in Raman hyperspectral imaging data. This strategy is based on the identification of essential spectral pixels (ESP), which can be found by convex hull calculation. As the corresponding set of spectra is largely reduced and encompasses the purest spectral signatures, direct database matching and identification can be reliably and rapidly performed. The efficiency of PBI was evaluated on both known and unknown samples, considering genuine and falsified pharmaceutical tablets. We showed that it is possible to analyze a wide variety of pharmaceutical formulations of increasing complexity (from 5 to 0.1% (w/w) of polymorphic impurity detection) for medium (150 x 150 pixels) and big (1000 x 1000 pixels) map sizes in less than 2 min. Moreover, in the case of falsified medicines, it is demonstrated that the proposed approach allows the identification of all compounds, found in very different proportions and, sometimes, in trace amounts. Furthermore, the relevant spectral signatures for which no match is found in the reference database can be identified at a later stage and the nature of the corresponding compounds further investigated. Overall, the provided results show that Raman hyperspectral imaging combined with PBI enables rapid and reliable spectral identification of complex pharmaceutical formulations.
高光谱成像已广泛应用于各种不同的领域，并且已经开发了许多化学计量工具来帮助识别化合物。然而，这些工具大多依赖于因子分解技术，对于大数据集和/或存在微量化合物的情况，这可能具有挑战性。本研究提出了一种基于像素的识别（PBI）方法，该方法能够在拉曼高光谱成像数据中轻松识别光谱特征。该策略基于基本光谱像素（ESP）的识别，可以通过凸包计算找到这些像素。由于相应的光谱集大大减少，并且包含了最纯净的光谱特征，因此可以可靠且快速地进行直接数据库匹配和识别。我们在已知和未知样品上评估了PBI的效率，这些样品包括正品和伪造的药片。我们表明，对于中等（150×150像素）和大尺寸（1000×1000像素）的图谱，在不到2分钟的时间内就可以分析各种复杂度不断增加的药物制剂（从5%到0.1%（w/w）的多晶型杂质检测）。此外，在假药的情况下，结果表明所提出的方法能够识别所有以非常不同的比例存在、有时甚至是痕量存在的化合物。此外，在参考数据库中未找到匹配的相关光谱特征可以在以后阶段进行识别，并进一步研究相应化合物的性质。总体而言，所提供的结果表明，拉曼高光谱成像与PBI相结合能够对复杂药物制剂进行快速可靠的光谱识别。
