Gobinet Cyril, Vrabie Valeriu, Manfait Michel, Piot Olivier
Unité MéDIAN, Centre National de Recherche Scientifique (CNRS) UMR 6237 MEDyC, UFR de Pharmacie, IFR 53, Université de Reims Champagne-Ardenne, 51096 Reims Cedex, France.
IEEE Trans Biomed Eng. 2009 May;56(5):1371-82. doi: 10.1109/TBME.2009.2014073. Epub 2009 Feb 6.
Raman spectra are classically modeled as a linear mixing of spectra of molecular constituents of the analyzed sample. Source separation methods are thus well suited to estimate these constituent spectra. However, physical distortions due to the instrumentation and biological nature of samples add nonlinearities to the Raman spectra model. These distortions are dark current, detector and optic responses, fluorescence background, and peak misalignment and peak width heterogeneity. The source separation results are thus deteriorated by these effects. We propose to develop specific preprocessing steps to correct these distortions and to retrieve a linear model. The benefits brought by these steps are studied by the application of two different source separation methods named joint approximate diagonalization of eigenmatrices and maximum likelihood positive source separation after the application of each step on a dataset acquired on a paraffin-embedded human skin biopsy. The efficacy of these methods to separate Raman spectra is also discussed.
拉曼光谱传统上被建模为分析样品分子成分光谱的线性混合。因此，源分离方法非常适合估计这些成分光谱。然而，由于仪器设备和样品的生物学性质导致的物理失真给拉曼光谱模型增加了非线性。这些失真包括暗电流、探测器和光学响应、荧光背景以及峰位错配和峰宽不均匀性。因此，这些效应会使源分离结果恶化。我们建议开发特定的预处理步骤来校正这些失真并恢复线性模型。在对石蜡包埋的人体皮肤活检样本采集的数据集上应用每个步骤后，通过应用两种不同的源分离方法，即特征矩阵联合近似对角化和最大似然正源分离，研究了这些步骤带来的益处。还讨论了这些方法分离拉曼光谱的功效。
