Tidsskrift : Applied Spectroscopy , vol. 67 , p. 892–902–11 , 2013
Trykt : 0003-7028
Elektronisk : 1943-3530
Publikasjonstype : Vitenskapelig artikkel
Sak : 8
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Calculating derivatives of spectral data by the Savitzky–Golay (SG)numerical algorithm is often used as a preliminary preprocessing step to resolve overlapping signals, enhance signal properties, and suppress
unwanted spectral features that arise due to nonideal instrument and sample properties. Addressing these issues, a study of the simulated and measured infrared data by partial least-squares regression has been conducted. The simulated data sets were modeled by considering a range
of undesired chemical and physical spectral anomalies and variations that can occur in a measured spectrum, such as baseline variations, noise, and scattering effects. The study has demonstrated the importance of the
optimization of the SG parameters during the conversion of spectra into derivative form, specifically window size and polynomial order of the fitting curve. A specific optimal window size is associated with an exact component of the system being estimated, and this window size does not
necessarily apply for some other component present in the system. Since the optimization procedure can be time-consuming, as a rough guideline spectral noise level can be used for assessment of window size. Moreover,it has been demonstrated that, when the extended multiplicative signal
correction (EMSC) is used alongside the SG procedure, the derivative treatment of data by the SG algorithm must precede the EMSC normalization.