Applying wavelet frequency component correlative selection in Raman spectral analysis

To overcome the limitations of existing wavelet transform (WT) preprocessing methods for Raman spectra, an improved preprocessing method - WT frequency component correlative selection algorithm - is proposed. Raman spectra are firstly prism-decomposed by WT, then correlations between every frequent weight and target are computed and threshold is set to select the efficient input data for calibration model. Applying this method in gasoline Raman spectra data preprocessing, experimental results show the new algorithm obviously weaken the fluorescence and high frequent noise and improves the prediction performance of the partial least square (PLS) model for gasoline octane number comparing with other existing methods.