Feasibility of analyte prediction in phantoms using a theoretical model of near-infrared spectra

Near-infrared (NIR) spectroscopic measurement of blood and tissue chemistry often requires a large set of subject data for training a prediction model. We have previously developed the principal component analysis loading correction (PCALC) method to correct for subject related spectral variations. In this study we tested the concept of developing PCALC factors from simulated spectra. Thirty, two-layer solid phantoms were made with 5 ink concentrations (0.004%-0.02%), 2 mu s' levels, and 3 fat thicknesses. Spectra were collected in reflectance mode and converted to absorbance by referencing to a 99% reflectance standard. Spectra (5733) were simulated using Kienle's two-layer turbid media model encompassing the range of parameters used in the phantoms. PCALC factors were generated from the simulated spectra at one ink concentration. Simulated spectra were corrected with the PCALC factors and a PLS model was developed to predict ink concentration from spectra. The best-matched simulated spectrum was identified for each measured phantom spectrum. These best-matched simulated spectra were corrected with the PCALC factors derived from the simulated spectra set, and they were used in the PLS model to predict ink concentrations. The ink concentrations were predicted with an R-2=0.897, and an estimated error (RMSEP) of 0.0037%. This study demonstrated the feasibility of using simulated spectra to correct for inter-subject spectral differences and accurately determine analyte concentrations in turbid media.