论文标题
贝叶斯定量对相干抗螺旋形拉曼散射光谱
Bayesian quantification for coherent anti-Stokes Raman scattering spectroscopy
论文作者
论文摘要
我们提出了一个贝叶斯统计模型，用于分析连贯的反stokes拉曼散射（CARS）光谱。我们的定量分析包括对组成线形参数的统计估计，潜在的拉曼信号，错误校正的汽车频谱和测量的汽车频谱。因此，这项工作可以在汽车光谱范围的背景下进行广泛的不确定性定量。此外，我们提出了一种无监督的方法，用于改善拉曼样光谱的光谱分辨率，几乎不需要\ textit {a先验}信息。最后，通过使用小波的插值技术，可以增强用于纠正汽车实验伪影的最近提供的小波棱镜方法。使用水中腺苷，二磷酸腺苷，二磷酸和三磷酸的汽车光谱，以及D-果糖，D-葡萄糖及其二糖组合蔗糖的Equrols溶液。
We propose a Bayesian statistical model for analyzing coherent anti-Stokes Raman scattering (CARS) spectra. Our quantitative analysis includes statistical estimation of constituent line-shape parameters, underlying Raman signal, error-corrected CARS spectrum, and the measured CARS spectrum. As such, this work enables extensive uncertainty quantification in the context of CARS spectroscopy. Furthermore, we present an unsupervised method for improving spectral resolution of Raman-like spectra requiring little to no \textit{a priori} information. Finally, the recently-proposed wavelet prism method for correcting the experimental artefacts in CARS is enhanced by using interpolation techniques for wavelets. The method is validated using CARS spectra of adenosine mono-, di-, and triphosphate in water, as well as, equimolar aqueous solutions of D-fructose, D-glucose, and their disaccharide combination sucrose.
