Paper
24 April 1998 Multicomponent assay for blood substrates in human sera and haemolysed blood by mid-infrared spectroscopy
Gerhard H. Werner, Dirk Boecker M.D., Hans-Peter Haar, Hans-Juergen Kuhr, Reinhold Mischler
Author Affiliations +
Proceedings Volume 3257, Infrared Spectroscopy: New Tool in Medicine; (1998) https://doi.org/10.1117/12.306074
Event: BiOS '98 International Biomedical Optics Symposium, 1998, San Jose, CA, United States
Abstract
A method of multicomponent analysis for blood substrates based on FTIR spectroscopy is described. Venous and capillary blood was used as a matrix, with sample volumes as small as 1 (mu) l. The spectra were obtained by transmission and diffuse reflection measurements from dried samples on a disposable carrier. The mean square prediction errors for 90 blood samples form different patients, calculated by cross- validation, were found to be 18 mg/dl for glucose, 17.5 mg/dl for cholesterol, 23.5 mg/dl for triglycerides, and 0.77 g/dl for haemoglobin. The mean square prediction errors for serum analyses are smaller: for a population of more than 200 serum samples we found 8 mg/dl for cholesterol, 14.9 mg/dl for triglycerides, 0.23 g/dl for total proteins, and 8.7 mg/dl for urea. The in-series standard error for the glucose concentration was low as 2.2 mg/dl. The combined spectroscopic and biological error for glucose was found to be 7 mg/dl. This value includes the 'matrix effect'. In independent serum samples glucose concentrations were measured with a total error of 16 mg/dl.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gerhard H. Werner, Dirk Boecker M.D., Hans-Peter Haar, Hans-Juergen Kuhr, and Reinhold Mischler "Multicomponent assay for blood substrates in human sera and haemolysed blood by mid-infrared spectroscopy", Proc. SPIE 3257, Infrared Spectroscopy: New Tool in Medicine, (24 April 1998); https://doi.org/10.1117/12.306074
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KEYWORDS
Blood

Glucose

Spectroscopy

Error analysis

Proteins

Urea

Statistical analysis

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