Byrne Hugh J, Knief Peter, Keating Mark E, Bonnier Franck
FOCAS Research Institute, Dublin Institute of Technology, Kevin Street, Dublin 8, Ireland.
Chem Soc Rev. 2016 Apr 7;45(7):1865-78. doi: 10.1039/c5cs00440c. Epub 2015 Oct 14.
Vibrational spectroscopy, both infrared absorption and Raman spectroscopy, have attracted increasing attention for biomedical applications, from in vivo and ex vivo disease diagnostics and screening, to in vitro screening of therapeutics. There remain, however, many challenges related to the accuracy of analysis of physically and chemically inhomogeneous samples, across heterogeneous sample sets. Data preprocessing is required to deal with variations in instrumental responses and intrinsic spectral backgrounds and distortions in order to extract reliable spectral data. Data postprocessing is required to extract the most reliable information from the sample sets, based on often very subtle changes in spectra associated with the targeted pathology or biochemical process. This review presents the current understanding of the factors influencing the quality of spectra recorded and the pre-processing steps commonly employed to improve on spectral quality. It further explores some of the most common techniques which have emerged for classification and analysis of the spectral data for biomedical applications. The importance of sample presentation and measurement conditions to yield the highest quality spectra in the first place is emphasised, as is the potential of model simulated datasets to validate both pre- and post-processing protocols.
振动光谱,包括红外吸收光谱和拉曼光谱,在生物医学应用中受到越来越多的关注,从体内和体外疾病诊断与筛查,到治疗药物的体外筛选。然而,在处理跨异质样本集的物理和化学不均匀样本的分析准确性方面,仍然存在许多挑战。需要进行数据预处理,以应对仪器响应、固有光谱背景和失真的变化,从而提取可靠的光谱数据。需要进行数据后处理,以便根据与目标病理学或生化过程相关的光谱中通常非常细微的变化,从样本集中提取最可靠的信息。本综述介绍了目前对影响所记录光谱质量的因素以及为提高光谱质量而常用的预处理步骤的理解。它进一步探讨了一些用于生物医学应用光谱数据分类和分析的最常见技术。强调了样本呈现和测量条件对于首先产生最高质量光谱的重要性,以及模型模拟数据集对验证预处理和后处理协议的潜力。