Assessment of Partial Least-Squares Calibration and Wavelength Selection for Complex Near-Infrared Spectra

Complex near-infrared (near-IR) spectra of aqueous solutions containing five independently varying absorbing species were collected to assess the ability of partial least-squares (PLS) regression and wavelength selection for calibration and prediction of these species in the presence of each other. It was confirmed that PLS calibration models can successfully predict chemical concentrations of all five chemicals from a single spectrum. It was observed from the PLS spectral loadings that spectral regions containing absorption bands of a single analyte alone were not sufficient for the model to adequately predict the concentration of the analyte because of the high degree of overlap between glucose, lactate, ammonia, glutamate, and glutamine. Three wavelength selection algorithms were applied to the spectra to identify regions containing necessary information, and in each case it was found that nearly the entire spectral range was needed for each determination. The results suggest that wavelength selection does result in a reduction of data points from the full spectrum, but the decrease seen with these near-infrared spectra was less than typically seen in mid-IR or Raman spectra, where peaks are narrower and well separated. As a result of this need for more wavelengths, the engineering of a dedicated system to measure these analytes in complex media such as blood or tissue culture broths by using this near-infrared region (2.0–2.5 μm) is further complicated.