Fuzzy-based purest wavelength selection from spectral data
Wavelength selection is usually a useful and sometimes a necessary task in process monitoring using spectroscopic equipment. In this paper, a novel fuzzy-based purest wavelength selection algorithm from spectral data is presented. The proposed algorithm uses the pure component spectra of all chemical components within the mixture in a fuzzy logic framework to identify the set of the most important wavelengths (the set of the purest wavelengths) for a chemical component of interest. Both synthetic and real (Raman) spectral data sets were employed to evaluate the performance of the proposed algorithm. As a comparative study in the case of the real Raman data, the Evolving Window Factor Analysis (EWFA) technique is applied to both the original spectra and the selected set of the purest wavelengths for a component of interest. The resulting first singular value in EWFA is then compared to the reference concentration trend obtained using the conventional Partial Least Squares (PLS) regression model. The comparison confirms the high quality of the selected set of the purest wavelengths and the effectiveness of the proposed fuzzy-based algorithm. Copyright (c) 2007 John Wiley & Sons, Ltd.
wavelength selection process monitoring fuzzy logic pls ewfa spectral signal processing