Sala, Alexandra, Spalding, Katie E, Ashton, Katherine M, Board, Ruth, Butler, Holly J, Dawson, Timothy P, Harris, Dean A, Hughes, Caryn S, Jenkins, Cerys A, Jenkinson, Michael D 
ORCID: 0000-0003-4587-2139 et al (show 4 more authors)
(2020)
Rapid analysis of disease state in liquid human serum combining infrared spectroscopy and "digital drying".
JOURNAL OF BIOPHOTONICS, 13 (9).
e202000118-.
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ATR-FTIR, cancer, digital drying, infrared spectroscopy, serum |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 13 Jul 2021 13:08 |
| Last Modified: | 18 Jan 2023 21:36 |
| DOI: | 10.1002/jbio.202000118 |
| Open Access URL: | https://onlinelibrary.wiley.com/doi/10.1002/jbio.2... |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3129884 |
Dimensions
Dimensions
