Continuous Wavelet Transform Based Partial Least Squares Regression for Quantitative Analysis of Raman Spectrum
| Autor: | Digant P. Davé, James O. Nyagilo, Shuo Li, Jean Gao |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Chemistry
Wavelet Analysis Biomedical Engineering Analytical chemistry Reproducibility of Results Pharmaceutical Science Medicine (miscellaneous) Wavelet transform Bioengineering Regression analysis Spectrum Analysis Raman Computer Science Applications symbols.namesake Wavelet Robustness (computer science) Partial least squares regression symbols Least-Squares Analysis Electrical and Electronic Engineering Biological system Raman spectroscopy Continuous wavelet transform Raman scattering Biotechnology |
| Zdroj: | IEEE Transactions on NanoBioscience. 12:214-221 |
| ISSN: | 1558-2639 1536-1241 |
| DOI: | 10.1109/tnb.2013.2278288 |
| Popis: | Quantitative analysis of Raman spectra using surface-enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in in vivo molecular imaging. Partial least square regression (PLSR) methods have been reported as state-of-the-art methods. However, the approaches fully rely on the intensities of Raman spectra and can not avoid the influences of the unstable background. In this paper we design a new continuous wavelet transform based PLSR (CWT-PLSR) algorithm that uses mixing concentrations and the average CWT coefficients of Raman spectra to carry out PLSR. We elaborate and prove how the average CWT coefficients with a Mexican hat mother wavelet are robust representations of Raman peaks, and the method can reduce the influences of unstable baseline and random noises during the prediction process. The algorithm was tested using three Raman spectra data sets with three cross-validation methods in comparison with current leading methods, and the results show its robustness and effectiveness. |
| Databáze: | OpenAIRE |
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