An Automated Algorithm of Peak Recognition Based on Continuous Wavelet Transformation and Local Signal-to-Noise Ratio

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	An Automated Algorithm of Peak Recognition Based on Continuous Wavelet Transformation and Local Signal-to-Noise Ratio
	Qian, F.; Y. H. Wu and P. Hao
刊名	Applied Spectroscopy
	2017
卷号	71 期号:8
英文摘要	Raman peaks carry valuable information about constituent chemical bonds. Therefore, peak recognition is a very essential part of spectral analysis. The fully automated peak recognition is convenient in practical application. A fully automated Raman peaks recognition algorithm based on continuous wavelet transformation and local signal-to-noise ratio (LSNR) is proposed. This algorithm extracts feature points through continuous wavelet transformation and recognizes peaks through LSNR. This algorithm also can be used to eliminate spike, noise, and baseline. Both simulated and experimental data are used to evaluate the performance of the CWT-LSNR algorithm compared with the other two algorithms. The results show that CWT-LSNR gives better accuracy and has the advantage of easy use.
语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/59155]
专题	长春光学精密机械与物理研究所_中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Qian, F.,Y. H. Wu and P. Hao. An Automated Algorithm of Peak Recognition Based on Continuous Wavelet Transformation and Local Signal-to-Noise Ratio[J]. Applied Spectroscopy,2017,71(8).
APA	Qian, F.,&Y. H. Wu and P. Hao.(2017).An Automated Algorithm of Peak Recognition Based on Continuous Wavelet Transformation and Local Signal-to-Noise Ratio.Applied Spectroscopy,71(8).
MLA	Qian, F.,et al."An Automated Algorithm of Peak Recognition Based on Continuous Wavelet Transformation and Local Signal-to-Noise Ratio".Applied Spectroscopy 71.8(2017).