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Updated: May 11, 2026
09:57
Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems
Published on: February 10, 2020
[The symmetric zero-area conversion adptive peak-seeking method research for LIBS/Raman spectra]
Yun-Feng Bi1, Ying Li, Rong-Er Zheng
1Optics and Optoelectronics Laboratory, Ocean University of China, Qingdao 266100, China. byf@sdu.edu.cn
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|May 24, 2013
Summary
This study introduces an optimal symmetric zero-area transformation function for automatic peak seeking in spectral analysis. The method enhances accuracy for Laser-Induced Breakdown Spectroscopy (LIBS) and Raman spectroscopy, even with weak signals.
Area of Science:
- Spectroscopy
- Analytical Chemistry
- Signal Processing
Context:
- In situ and real-time spectral detection and analysis are crucial for applications like marine monitoring and oil exploration.
- Existing methods for peak seeking in spectral data can be influenced by background noise and baseline drift.
- Laser-Induced Breakdown Spectroscopy (LIBS) and Raman spectroscopy are key techniques requiring robust peak identification.
Purpose:
- To develop and evaluate an automatic peak seeking method for spectral data.
- To compare the performance of symmetric zero-area transformation functions based on Gaussian, Lorentz, and Voigt profiles.
- To identify an optimal transformation function for accurate peak detection, free from background and baseline influences.
Summary:
- Three symmetric zero-area transformation functions were constructed and compared using laboratory LIBS/Raman spectral data.
- An optimal transformation function was identified, yielding consistent peak position and width under optimal parameters.
- The proposed method demonstrates robustness against varying signal-to-noise ratios (SNR) and is effective for weak peak detection.
Impact:
- The developed method offers improved accuracy and reliability for automatic spectral analysis.
- It is adaptable to diverse spectral data, including challenging datasets with low SNR or weak peaks.
- This technique has the potential to significantly advance automated in situ analysis in LIBS and Raman spectroscopy applications.
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