Automatic Peak Integration and Baseline Correction for Micro-scale Gas Chromatographs Using Continuous Wavelet Transform

Xiangyu Zhao, Yutao Qin, Y. Gianchandani
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引用次数: 2

Abstract

Chromatograms generated by micro-scale gas chromatographs are complex sensor signals comprised of multiple chemical retention peaks superimposed upon a non-monotonic baseline. Depending on the chemical properties, the peaks may have Gaussian shapes with different widths, heights, skew, and overlap, all of which pose challenges to automated recognition and quantification of chemicals. This paper presents an automatic algorithm based on continuous wavelet transforms for peak integration and baseline correction of chromatograms generated by micro-scale gas chromatographs. This algorithm identifies peaks using a dynamic filter based on the inherent and known relationship between the peak widths and peak locations. The width of each peak is determined from continuous wavelet transform coefficients by locating the pair of local minima that straddle the apex. This approach provides peak detection and width estimation with low false positive rates, even for skewed peaks with tailing or fronting and for non-monotonic baselines.
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基于连续小波变换的微尺度气相色谱仪自动峰积分与基线校正
微尺度气相色谱仪生成的色谱图是由叠加在非单调基线上的多个化学保留峰组成的复杂传感器信号。根据化学性质的不同,峰可能具有不同宽度、高度、倾斜和重叠的高斯形状,所有这些都对化学物质的自动识别和定量提出了挑战。提出了一种基于连续小波变换的微尺度气相色谱仪色谱图峰积分和基线校正自动算法。该算法基于峰值宽度和峰值位置之间固有的已知关系,使用动态滤波器识别峰值。每个峰的宽度由连续小波变换系数确定,通过定位跨越顶点的一对局部最小值。这种方法提供了低假阳性率的峰检测和宽度估计,即使对于有尾峰或前峰的倾斜峰和非单调基线也是如此。
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