Nonlinear preprocessing method for detecting peaks from gas chromatograms

Byonghyo Shim,Sungroh Yoon,Hyeyoung Min

doi:10.1186/1471-2105-10-378

Abstract

BackgroundThe 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.ResultsWe 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.ConclusionOur 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.

Highlights

The problem of locating valid peaks from data corrupted by noise frequently arises while analyzing experimental data
Valid signals sometimes appear in the form of peaks, and for accurate analysis of the observations made, researchers want to separate true peaks from fictitious peaks generated by noise
We compare the performance of the proposed method with some conventional techniques including lowpass filtering (LPF) based preprocessing, wavelet domain thresholding, and pattern matching in continuous wavelet transform (CWT) domain [10]

Summary

Introduction

The problem of locating valid peaks from data corrupted by noise frequently arises while analyzing experimental data. Needs for detecting peaks in the presence of noise occur frequently when analyzing experimental data. Valid signals sometimes appear in the form of peaks, and for accurate analysis of the observations made, researchers want to separate true peaks from fictitious peaks generated by noise. Another example can be found in a recent study called multiplexed (page number not for citation purposes)

Results

Discussion

Conclusion