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拓展药品成分身份检测的分析工具箱:使用便携式拉曼光谱仪和近红外光谱仪对葡萄糖进行光谱筛选。

Expanding the analytical toolbox for identity testing of pharmaceutical ingredients: Spectroscopic screening of dextrose using portable Raman and near infrared spectrometers.

作者信息

Srivastava Hirsch K, Wolfgang Steven, Rodriguez Jason D

机构信息

Division of Pharmaceutical Analysis, Center for Drug Evaluation and Research, US Food and Drug Administration, 645 S. Newstead Ave, Saint Louis, MO 63110, USA.

Division of Pharmaceutical Analysis, Center for Drug Evaluation and Research, US Food and Drug Administration, 645 S. Newstead Ave, Saint Louis, MO 63110, USA.

出版信息

Anal Chim Acta. 2016 Mar 31;914:91-9. doi: 10.1016/j.aca.2016.01.061. Epub 2016 Feb 3.

Abstract

In the pharmaceutical industry, dextrose is used as an active ingredient in parenteral solutions and as an inactive ingredient (excipient) in tablets and capsules. In order to address the need for more sophisticated analytical techniques, we report our efforts to develop enhanced identification methods to screen pharmaceutical ingredients at risk for adulteration or substitution using field-deployable spectroscopic screening. In this paper, we report our results for a study designed to evaluate the performance of field-deployable Raman and near infrared (NIR) methods to identify dextrose samples. We report a comparison of the sensitivity of the spectroscopic screening methods against current compendial identification tests that rely largely on a colorimetric assay. Our findings indicate that NIR and Raman spectroscopy are both able to distinguish dextrose by hydration state and from other sugar substitutes with 100% accuracy for all methods tested including spectral correlation based library methods, principal component analysis and classification methods.

摘要

在制药行业,葡萄糖用作肠胃外溶液中的活性成分,以及片剂和胶囊中的非活性成分(辅料)。为了满足对更精密分析技术的需求,我们报告了我们为开发增强型鉴定方法所做的努力,该方法使用可现场部署的光谱筛选技术来筛查有掺假或替代风险的药物成分。在本文中,我们报告了一项旨在评估可现场部署的拉曼光谱和近红外(NIR)方法用于鉴定葡萄糖样品性能的研究结果。我们报告了光谱筛选方法与目前主要依赖比色法的药典鉴定试验的灵敏度比较。我们的研究结果表明,近红外光谱和拉曼光谱都能够通过水合状态区分葡萄糖,并与其他糖替代品区分开来,对于所有测试方法,包括基于光谱相关性的库方法、主成分分析和分类方法,准确率均达到100%。

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