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Fully Automated Decomposition of Raman Spectra into Individual Pearson's Type VII Distributions Applied to Biological and Biomedical Samples

机译:将拉曼光谱全自动分解为适用于生物和生物医学样品的单个Pearson VII型分布

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摘要

Rapid technological advances have made the acquisition of large numbers of spectra not only feasible, but also routine. As a result, a significant research effort is focused on semi-automated and fully automated spectral processing techniques. However, the need to provide initial estimates of the number of peaks, their band shapes, and the initial parameters of these bands presents an obstacle to the full automation of peak fitting and its incorporation into fully automated spectral-preprocessing workflows. Moreover, the sensitivity of peak-fit routines to initial parameter settings and the resultant variations in solution quality further impede user-free operation. We have developed a technique to perform fully automated peak fitting on fully automated preconditioned spectra specifically, baseline-corrected and smoothed spectra that are free of cosmic-ray-induced spikes. Briefly, the tallest peak in a spectrum is located and a Gaussian peak-fit is performed. The fitted peak is then subtracted from the spectrum, and the procedure is repeated until the entire spectrum has been processed. In second and third passes, all the peaks in the spectrum are fitted concurrently, but are fitted to a Pearson Type VII model using the parameters for the model established in the prior pass. The technique is applied to a synthetic spectrum with several peaks, some of which have substantial overlap, to test the ability of the method to recover the correct number of peaks, their true shape, and their appropriate parameters. Finally the method is tested on measured Raman spectra collected from human embryonic stem cells and samples of red blood cells.
机译:快速的技术进步使获取大量光谱不仅可行而且常规。结果,大量的研究工作集中在半自动化和全自动光谱处理技术上。但是,需要提供峰数量,其谱带形状以及这些谱带的初始参数的初始估计值,这对完全自动化峰拟合及其将其纳入全自动光谱预处理工作流程构成了障碍。此外,峰拟合例程对初始参数设置的敏感性以及解决方案质量的最终变化进一步阻碍了用户的自由操作。我们已经开发出一种技术,可以对全自动预处理的光谱执行全自动峰拟合,特别是在没有宇宙射线引起的尖峰的情况下进行基线校正和平滑的光谱。简而言之,确定频谱中的最高峰,然后执行高斯峰拟合。然后从光谱中减去拟合峰,然后重复该过程,直到处理完整个光谱为止。在第二遍和第三遍中,频谱中的所有峰均同时拟合,但使用先前遍中建立的模型参数拟合到Pearson VII型模型。将该技术应用于具有几个峰的合成光谱,其中一些峰存在实质性的重叠,以测试该方法恢复正确数量的峰,峰的真实形状和合适的参数的能力。最后,该方法在从人类胚胎干细胞和红细胞样品收集的拉曼光谱上进行测试。

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