Variable Selection in Multivariate Calibration of a Spectroscopic Glucose Sensor

Abstract

A variable selection method that reduces prediction bias in partial least-squares regression models was developed and applied to near-infrared absorbance spectra of glucose in pH buffer and cell culture medium. Comparisons between calibration and prediction capability for full spectra and reduced sets were completed. Variable selection resulted in statistically equivalent errors while reducing the number of wavelengths needed to fit the calibration data and predict concentrations from new spectra. Fewer than 25 wavelengths were selected to produce errors statistically equivalent to those yielded by the full set containing over 500 wavelengths. The algorithm correctly chose the glucose absorption peak areas as the information-carrying spectral regions.