Raman Spectra Of Background Subtraction Algorithm And Its Application

Raman spectroscopy is kind of scattering spectrum, which is caused by the light through the media and molecular interaction of the frequency changed by the scattering, using the information of vibrational rotational in a spectrometric method. It developed as a new analytical tools which provide fast, simple, repeatable, and samples need not pre-treatment, can measured directly by the fiber optic probe or through the quartz vessel. So it provides rapid and non destructive qualitative quantitative analysis. During the measurement procedure, as samples containing impurities or fluorescent material will bring the fluorescence interference and the excitation photon provides sufficient energy so that produce the fluorescence, the Raman signals will all be blurred or swamped by fluorescence.Although Raman spectra of some materials have fluorescent interference, but the fluorescence radiation is not sufficient to swamp the Raman signal, which can still be collected with Raman instruments. The Raman spectra have a certain background, which will affect further analysis of spectra using chemometrics method. Raman background correction algorithms are studied in this thesis, the brief introduction about domestic and foreign researching status in the Raman spectra, the purpose and significance of this thesis, and the main contents and methods are presented. By finding and reading a lot of literature, the theory and characteristics of Raman spectroscopy, instrument and development of Raman techniques were outlined.Penalized Least Squares and Wavelet Shrinkage are introduced in this paper to filter out high frequency noise in the Raman spectra. They have advantages when comparison with other soothing methods. Also we described the chemometric methods such as clustering, discriminant analysis and regression analysis model.Again, based on continuous wavelet transform and penalized least squares algorithms, the baselineWavelet is proposed and applied for baseline correction. The results show that background correction without missing important information. By comparion of models of cluster and discriminant analysis, such as PCA, Random Forest, before and after filtering noise and background correction, the separability and classification results are respectively improved. The influence of the lambda parameter from baselineWavelet on the smoothness of the fitted baseline has been clearly explained and clarified.We proposed the adaptive iterative re-weighted penalized least squares algorithm, which uses sparse matrix techniques to correct the baseline quickly and effectively. Since large number of samples is required for establishing the regression model. Example of quantitative analysis of airPLS algorithm is applied. By comparison of the built regression models which the spectra was pretreated with several baseline correction methods, the airpls baseline correction method has been demonstrated that it can fit the better baseline, have the smaller RMSECV values and require less computation time. The airpls method is very suitable for correcting the baseline of large amount calibration spectra when built the regression models with chemometrics algorithms.