Nonlinear preprocessing method for detecting peaks from gas chromatograms

Byonghyo Shim, Hyeyoung Min, Sungroh Yoon

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Background: The problem of locating valid peaks from data corrupted by noise frequently arises while analyzing experimental data. In various biological and chemical data analysis tasks, peak detection thus constitutes a critical preprocessing step that greatly affects downstream analysis and eventual quality of experiments. Many existing techniques require the users to adjust parameters by trial and error, which is error-prone, time-consuming and often leads to incorrect analysis results. Worse, conventional approaches tend to report an excessive number of false alarms by finding fictitious peaks generated by mere noise. Results: We have designed a novel peak detection method that can significantly reduce parameter sensitivity, yet providing excellent peak detection performance and negligible false alarm rates from gas chromatographic data. The key feature of our new algorithm is the successive use of peak enhancement algorithms that are deliberately designed for a gradual improvement of peak detection quality. We tested our approach with real gas chromatograms as well as intentionally contaminated spectra that contain Gaussian or speckle-type noise. Conclusion: Our results demonstrate that the proposed method can achieve near perfect peak detection performance while maintaining very small false alarm probabilities in case of gas chromatograms. Given the fact that biological signals appear in the form of peaks in various experimental data and that the propose method can easily be extended to such data, our approach will be a useful and robust tool that can help researchers highlight valid signals in their noisy measurements.

Original languageEnglish
Article number378
JournalBMC Bioinformatics
Publication statusPublished - 2009 Nov 18

Bibliographical note

Funding Information:
This work was supported in part by the second BK21 project and in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2009-0060369 and No. 2009-0079888). We would like to thank Prof. Jungbae Kim, Seung-hyun Jun, Seunghak Yu, Jay S. Lee and Guangtao Ge for their help.

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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