High-throughput powder X-ray diffraction, IR-spectroscopy and ion chromatography analysis of urinary stones: A comparative study

Walter de Gruyter GmbH - Tập 11 Số 12 - Trang 2107-2119 - 2013
Elena Yusenko1, K. V. Yusenko2, И. В. Корольков3, А. А. Шубин4, Ф. П. Капсаргин5, А. А. Ефремов1, Maria V. Yusenko6
11Department of Analytical and Organic Chemistry, Siberian Federal University, 660041, Krasnoyarsk, Russia
22Department of Chemistry, Center for Materials Science and Nanotechnology, University of Oslo, PO Box 1033, Blindern, N-0315, Oslo, Norway
33Department of Crystal Chemistry, Nikolaev Institute of Inorganic Chemistry, 630090, Novosibirsk, Russia
44Department of Physical and Inorganic Chemistry, Siberian Federal University, 660041, Krasnoyarsk, Russia
55Department of Urology, Andrology and Sexology, Vojno-Yasenetsky Krasnoyarsk Medical State University, 660022, Krasnoyarsk, Russia
66Institute of Molecular Tumor Biology (IMTB), Medical Faculty of the University of Münster (Westfälische Wilhelms-Universität), D-48149, Münster, Germany

Tóm tắt

AbstractThe instrumental qualitative analysis of urinary stones is a critical step in clinical practice and urological research. A powder X-ray diffraction, IR-spectroscopy and ion chromatography have been applied for the qualitative analysis of 20 urinary stones. Suggestions for a sample preparation and an optimal measurement strategy were formulated. The main difficulties for the powder X-ray diffraction qualitative analysis are a limiting amount of the sample and a preferential orientation of crystals, both issues should be minimized by the special sample preparation. Urinary stones samples have been clustered into four groups using different sets of numerical input data (cation and anion content, phase composition). At the same time a high-throughput multivariate clustering has been applied for powder X-ray diffraction and IR-spectroscopy data. The multivariate whole-profile approach can be used as a tool for a high-throughput time reducing technique for clinical practice, when a quick and stable classification of samples is required. All three sets of the data can be automatically separated into three clusters: oxalate-reach, oxalate-pure and non-oxalate samples. Uricite-pure and uricite-rich samples can be easily clustered.

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Walter de Gruyter GmbH

Tập 11 Số 12

2107-2119

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