Monte do Tojal, Hortinhas, Terena (São Pedro), Portugal.
Michael Smith Laboratories, 8166The University of British Columbia, Vancouver, BC, Canada.
Appl Spectrosc. 2022 Jan;76(1):61-80. doi: 10.1177/00037028211061174. Epub 2021 Dec 22.
Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.
拉曼光谱中的重叠峰使得复杂样品的呈现、解释和分析变得复杂。对于依赖稀疏性的方法，如多变量曲线分辨和其他光谱分解以及二维相关光谱（2D-COS）、多源相关分析和主成分分析，这尤其成问题。尽管可以使用基于软件的分辨率增强方法来解决此类问题，但它们的性能通常不同，因此对于特定任务，有些方法比其他方法更适用。此外，需要自动化方法来应用于包含多个重叠峰的大量不同的高光谱数据集，因此需要理想地适用于各种任务的方法。为了研究这些问题，我们实现了三种基于伪谱、过反卷积和峰拟合的新分辨率增强方法，并与三种现有方法（节点细化、盲目反卷积和通用峰拟合程序 Fityk）一起进行了评估。我们首先将这些方法应用于不同的合成光谱，每个光谱都由九个重叠的 Voigt 轮廓峰组成。根据几个标准评估了光谱分辨率的提高，包括重叠峰的分离和分辨率增强谱中真实峰强度的保留。然后，我们研究了这些方法提高测量拉曼光谱分辨率的效果。将使用窄光谱仪狭缝获得的葡萄糖高分辨率光谱与使用降低光谱分辨率的宽狭缝获得的光谱进行了比较。我们还确定了不同分辨率增强方法对 2D-COS 和从哺乳动物细胞光谱生成化学对比图像的影响。最后，我们讨论了这些方法的特殊优点、缺点和潜力。我们的努力深入了解了有效分辨率增强方法的需求，这些方法实现自动化的可行性，以及目前限制其使用的问题的性质，特别是那些需要改进的方面。
