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Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Ed. by Gillery, Philippe / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter / Tate, Jillian R.


IMPACT FACTOR increased in 2015: 3.017
Rank 5 out of 30 in category Medical Laboratory Technology in the 2014 Thomson Reuters Journal Citation Report/Science Edition

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1437-4331
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Infrared Analysis of Urinary Stones: a Trial of Automated Identification

Laurence Maurice Estepa / Pierre Levillain / Bernard Lacour / Michel Daudon

Citation Information: Clinical Chemistry and Laboratory Medicine. Volume 37, Issue 11-12, Pages 1043–1043, ISSN (Print) 1434-6621, DOI: 10.1515/CCLM.1999.153, June 2005

Publication History

Published Online:
2005-06-01

Abstract

A Search algorithm included in the Opus software of Bruker (Germany) was evaluated for analysis of urinary stones. Three reference libraries containing respectively 85 (single components), 1,059 (binary mixtures) and 4,565 (ternary mixture) digitized spectra were created and used to identify unknown spectra (n=320), applying the automatic procedure. Identification of the major component was correct in 83% of cases but the percentage of identification significantly decreased for the second and the third components. In cases of identification of the two first components, quantitative assessment was correct within tolerance limits ± 15%.

The computer results are judged unsatisfactory with regard to pathology because computer-aided identification is not sufficiently sensitive and specific to differentiate species with similar spectral pattern, even for the identification of main component, and also to detect minor components. It can be of assistance to guide spectral analysis, but it cannot replace human identification.

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