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Noninvasive glucose sensing has been studied widely. Near infrared (NIR) absorption spectroscopy and Raman scattering spectroscopy are proposed individually and combined as methods for glucose measurement in a three component sample matrix. In both techniques, the light transmits through human skin and a spectrum is collected.
The research described in this thesis is like this. The use of individual NIR spectra data and individual Raman spectra data can give a good prediction ability of the partial least-squares (PLS) calibration model. Since the NIR and Raman spectroscopies have complementary nature of molecular vibrations, the research tried to prove the prediction ability of the PLS calibration model can be improved by combining NIR and Raman spectra data.
Two approaches are investigated to ascertain the benefits of combining these spectral methods. First, NIR and Raman spectral data collected from a set of 60 samples concated and used to compute multivariate models based on PLS and net analyte signal (NAS) methods. The performance of models based on concated NIR-Raman spectra are compared to conventional models based on only NIR and only Raman spectra. The second strategy reported in this chapter is the simulated NIR and Raman spectra and computing PLS and NAS models by concating these simulated spectra. Spectral simulation permits systematic variations in noise levels. In both cases, various preprocessing methods are explored to find a suitable way to combine the different spectral types.
The result from the real spectra data is that adding low signal-to-noise ratio (SNR) to high SNR spectra would make the calibration models worse. The result from the simulated spectra data is that with the same SNR and the same magnitude of the two spectra, the prediction ability of the calibration model can be improved.