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Multivariate determination of glucose using NIR spectra of human blood serum

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4 Author(s)
Ham, F.M. ; Div. of Electr. & Comput. Sci. & Eng., Florida Inst. of Technol., Melbourne, FL, USA ; Cohen, G.M. ; Patel, K. ; Gooch, B.R.

Multivariate statistical modeling methods have been applied to near-infrared (NIR) spectral data to discriminate glucose concentrations. Specifically, performance levels are compared for principal component regression (PCR) and partial least-squares regression (PLSR) models based on their standard errors of prediction (SEP). NIR spectra of blood serum from 456 individual hospitalized patients were generated using a NIRSystems 6500 spectrophotometer in 2 nm intervals from 400 to 1098 nm. Only the data between 870 and 1098 nm were used for calibration model development and validation. Performance results for the PLSR model (SEP=29.577 mg/dl) were about the same as that obtained with the PCR model (SEP=28.881 mg/dl)

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Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE

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