Optimization of informative spectral regions for the quantification of cholesterol, glucose and urea in control serum solutions using searching combination moving window partial least squares regression method with near infrared spectroscopy

Near infrared (NIR) spectra in the 10 000–4000 cm− 1 region were collected from control serum solutions mixed with cholesterol, glucose and urea solutions with various concentrations. The concentration ranges of cholesterol, glucose and urea were 46.08–395.39, 29.89–318.57 and 5.92–19.66 mg/dl, respectively, which cover the clinically important ranges. Moving window partial least squares regression (MWPLSR) method was applied to the NIR data to select informative regions for cholesterol, glucose and urea. Searching combination moving window partial least squares regression (SCMWPLSR) method was used to search for the optimized combinations of informative regions found by MWPLSR, and partial least squares calibration models were developed and compared for each spectral region proposed by MWPLSR and SCMWPLSR and whole region. The best PLS calibration model for each component was obtained from the spectral region optimized by SCMWPLSR. The best prediction results for cholesterol, glucose and urea have RMSEP of 6.68, 10.35 and 1.28 mg/dl, respectively. Of particular note is that the prediction result for urea is much better than that previously obtained by several research groups under similar conditions. SCMWPLSR can select the optimized combination spectral region for each blood component successfully within the complex blood matrix over the low concentration range.