Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells

被引:158
作者
Byrne, Hugh J. [1 ]
Knief, Peter [2 ]
Keating, Mark E. [1 ,3 ]
Bonnier, Franck [4 ]
机构
[1] Dublin Inst Technol, FOCAS Res Inst, Kevin St, Dublin 8, Ireland
[2] Royal Coll Surgeons Ireland, Dept Med Phys & Physiol, 123 St Stephens Green, Dublin 2, Ireland
[3] Dublin Inst Technol, Sch Phys, Kevin St, Dublin 8, Ireland
[4] Univ Tours, Fac Pharm, EA Nanomedicaments & Nanosondes 6295, 31 Ave Monge, F-37200 Tours, France
基金
爱尔兰科学基金会;
关键词
ARTIFICIAL NEURAL-NETWORKS; MICRO SPECTROSCOPY; GENETIC ALGORITHMS; FEATURE-SELECTION; MIE SCATTERING; VIBRATIONAL SPECTROSCOPY; VARIABLE SELECTION; COMPONENT ANALYSIS; IN-VITRO; FTIR;
D O I
10.1039/c5cs00440c
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页码:1865 / 1878
页数:14
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