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

Abstract : 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.
Document type :
Journal articles
Complete list of metadatas

https://hal-univ-tours.archives-ouvertes.fr/hal-01386861
Contributor : Martin Soucé <>
Submitted on : Monday, October 24, 2016 - 5:27:02 PM
Last modification on : Tuesday, March 26, 2019 - 3:54:24 PM

Links full text

Identifiers

Collections

Citation

Hugh J Byrne, Peter Knief, M E Keating, Franck Bonnier. Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells.. Chemical Society Reviews, Royal Society of Chemistry, 2016, 45 (7), pp.1865-1878. ⟨10.1039/c5cs00440c⟩. ⟨hal-01386861⟩

Share

Metrics

Record views

225