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Smartphone-based thin layer chromatography for the discrimination of falsified medicines

机译:基于智能手机的薄层色谱法,用于鉴别伪造药品

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Identification of counterfeit and substandard drugs, which pose severe risks to patient safety is increasingly important, as inauthentic drugs become more commonplace in developing parts of the world. Though thin layer chromatography (TLC) performed with laboratory-based instruments enables accurate analysis of suspect medicines, there is tremendous interest in development of an inexpensive mobile platform that would broaden the applicability of TLC to remote pharmacies and clinics that presently do not have access to laboratory analysis. In this work, we demonstrate identification and characterization of pharmaceutical products via TLC using a custom cradle that interfaces with a smartphone. A UV lamp integrated within the cradle illuminates a TLC plate loaded with calibration standards and an aliquot of a drug of unknown concentration. Phosphorescence from the plate surface excited by UV light reveals principal spots. Two independent image processing approaches were developed to enable image processing to be performed locally with the smartphone processor, or remotely by a server running MatLab routines on uploaded images. Both approaches report the intensity and travel distance of spots within a TLC plate. The system is able to discern 5% medicine concentration differences and to deliver analytical results that are identical to those obtained by a laboratory TLC densitometer.
机译:伪造和不合标准的鉴定,对患者安全构成严重风险越来越重要,因为不间断的药物在世界发展中的地区变得更加普遍。尽管使用基于实验室的仪器进行的薄层色谱(TLC)可以准确地分析可疑药品,但对一个廉价的移动平台的发展存在巨大兴趣,这将扩大TLC对目前无法访问的远程药店和诊所的适用性实验室分析。在这项工作中,我们使用TLC使用TLC使用与智能手机接口的定制摇篮来证明药品的识别和表征。集成在支架内的UV灯照亮装有校准标准的TLC板和一种未知浓度药物的等分试样。由UV光激发的板表面的磷光揭示了主要斑点。开发了两个独立的图像处理方法以使能够与智能手机处理器本地执行的图像处理,或者通过在上载的图像上运行MATLAB例程的服务器远程进行。两种方法都报告了TLC板内斑点的强度和行进距离。该系统能够辨别5%的药物浓度差异,并提供与由实验室TLC密度计获得的分析结果相同。

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