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Rapid analysis of disease state in liquid human serum combining infrared spectroscopy and “digital drying”
,
Katie E. Spalding,
Katherine M. Ashton,
Ruth Board,
Holly J. Butler,
Timothy P. Dawson,
Dean A. Harris,
Caryn S. Hughes,
Cerys A. Jenkins,
Michael D. Jenkinson,
David S. Palmer,
Benjamin R. Smith,
Cathy Thornton ,
Matthew J. Baker
Journal of Biophotonics, Volume: 13, Issue: 9
Swansea University Author:
Cathy Thornton
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DOI (Published version): 10.1002/jbio.202000118
Abstract
In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR o...
| Published in: | Journal of Biophotonics |
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| ISSN: | 1864-063X 1864-0648 |
| Published: |
Wiley
2020
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa54465 |
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| Abstract: |
In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate “digital drying” as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively. |
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| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
Engineering and Physical Sciences Research Council. Grant Number: EP/L505080/1 |
| Issue: |
9 |