A new approach to automated peak detection
A new approach to automated peak detection
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DOI：
10.1016/s0169-7439(03)00113-8
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发表时间：
2003-11-28
影响因子：
3.9
通讯作者：
Wahl, KL
中科院分区：
文献类型：
--
作者：
Jarman, KH;Daly, DS;Wahl, KL
光谱峰值检测算法通常难以自动化，因为它们要么依赖于某种程度上任意的规则，要么被调谐到特定的光谱峰值属性。一种流行的方法检测信号强度超过某个阈值的峰值。该阈值通常被任意设置在噪声水平之上或由用户手动设置。基于强度阈值的方法可能对基线变化和信号强度敏感。另一种流行的峰检测方法依赖于将光谱强度与参考峰形状匹配。这种方法可能对基线变化和与参考峰形状的偏差非常敏感。这样的方法可以显着的挑战，现代分析仪器的基线往往漂移，感兴趣的峰可能有一个低的信号噪声（S/N）比，并没有明确的参考峰形状是可用的。我们提出了一种新的方法，用于光谱峰检测，被设计为通用的，易于自动化。采用基于直方图的光谱强度模型，通过将观测值的估计方差（光谱的x轴）与在某个感兴趣的窗口内不存在峰时的预期方差进行比较来检测峰。我们比较了这种方法的实现，现有的两个峰值检测算法，使用一系列的模拟光谱。(C)2003 Elsevier B.V.保留所有权利。
Spectral peak detection algorithms are often difficult to automate because they either rely on somewhat arbitrary rules, or are tuned to specific spectral peak properties. One popular approach detects peaks where signal intensities exceed some threshold. This threshold is typically set arbitrarily above the noise level or manually by the user. Intensity threshold-based methods can be sensitive to baseline variations and signal intensity. Another popular peak detection approach relies on matching the spectral intensities to a reference peak shape. This approach can be very sensitive to baseline changes and deviations from the reference peak shape. Such methods can be significantly challenged by modem analytical instrumentation where the baseline tends to drift, peaks of interest may have a low signal to noise (S/N) ratio, and no well-defined reference peak shape is available.We present a new approach for spectral peak detection that is designed to be generic and easily automated. Employing a histogram-based model for spectral intensity, peaks are detected by comparing the estimated variance of observations (the x-axis of the spectrum) to the expected variance when no peak is present inside some window of interest. We compare an implementation of this approach to two existing peak detection algorithms using a series of simulated spectra. (C) 2003 Elsevier B.V. All rights reserved.