Research Article
Raman Spectral Data De-noising based on Wavelet Analysis
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 102 - Issue 16 |
| Published: September 2014 |
| Authors: Nitendra Kumar, A. H. Siddiqi, Khursheed Alam |
10.5120/17899-8864
|
Nitendra Kumar, A. H. Siddiqi, Khursheed Alam . Raman Spectral Data De-noising based on Wavelet Analysis. International Journal of Computer Applications. 102, 16 (September 2014), 20-22. DOI=10.5120/17899-8864
@article{ 10.5120/17899-8864,
author = { Nitendra Kumar,A. H. Siddiqi,Khursheed Alam },
title = { Raman Spectral Data De-noising based on Wavelet Analysis },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 102 },
number = { 16 },
pages = { 20-22 },
doi = { 10.5120/17899-8864 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Nitendra Kumar
%A A. H. Siddiqi
%A Khursheed Alam
%T Raman Spectral Data De-noising based on Wavelet Analysis%T
%J International Journal of Computer Applications
%V 102
%N 16
%P 20-22
%R 10.5120/17899-8864
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Nowadays, most analytical instruments in modern laboratories are computerized, partly owing to the rapid development of advanced micro-electronic technology. Digitalized spectroscopic data can be exported from these instruments very easily for subsequent signal processing. Raman Spectroscopy is widely recognized as powerful, non-destructive techniques for characterizing materials. The key to realize the qualitative and quantitative analysis is data processing and analysis. But signals in Raman spectral analysis often have noise, which greatly influences the achievement of accurate analytical results. The de-noising of Raman spectral is an important part of de-noising. Wavelet functions are localized both time and frequency (or scale) and in time, via dilations and translations of the mother wavelet, respectively, both time and frequency information are maintained after transformation. This paper presents wavelet based de-noising method for Raman Spectral data of Sr2+ modified PMN-PZT and compared the results with Daubechies, Coiflet, Symlet.
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