Multivariate calibration for near-infrared spectroscopic assays of blood substrates in human plasma based on variable selection using PLS-regression vector choices

Abstract A multicomponent assay for the blood substrates of total protein, glucose, total cholesterol, triglycerides and urea in human EDTA-plasma by FT-IR spectroscopy is described based on near-infrared spectra of human plasma recorded in a 1 mm quartz transmission cell. Partial least-squares was applied for multivariate calibration taking into account absorbance or logarithmized single beam spectra. Further data reduction was applied using the pairwise selection of spectral variables suggested by the weights of the optimum full spectrum PLS-regression vector. The standard errors of prediction for protein, cholesterol, triglycerides, glucose and urea are calculated by cross-validation for the population of 124 plasma samples of different patients. These values are compared for full spectrum and reduced spectral variable set regression.