Detecting Pre-Analytically Delayed Blood Samples for Laboratory Diagnostics Using Raman Spectroscopy

Pascal Hunold; Markus Fischer; Carsten Olthoff; Peter W Hildebrand; Thorsten Kaiser; René Staritzbichler

doi:10.3390/ijms24097853

Detecting Pre-Analytically Delayed Blood Samples for Laboratory Diagnostics Using Raman Spectroscopy

Int J Mol Sci. 2023 Apr 25;24(9):7853. doi: 10.3390/ijms24097853.

Authors

Pascal Hunold^{1

2}, Markus Fischer², Carsten Olthoff^{1

2}, Peter W Hildebrand², Thorsten Kaiser^{1

3}, René Staritzbichler²

Affiliations

¹ Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany.
² Institute for Medical Physics and Biophysics, Leipzig University, 04107 Leipzig, Germany.
³ University Institute for Laboratory Medicine, Microbiology and Clinical Pathobiochemistry, University Hospital OWL of Bielefeld University, Campus Klinikum Lippe, 32756 Detmold, Germany.

Abstract

In this proof-of-principle study, we systematically studied the potential of Raman spectroscopy for detecting pre-analytical delays in blood serum samples. Spectra from 330 samples from a liver cirrhosis cohort were acquired over the course of eight days, stored one day at room temperature, and stored subsequently at 4 °C. The spectra were then used to train Convolutional Neural Networks (CNN) to predict the delay to sample examination. We achieved 90% accuracy for binary classification of the serum samples in the groups "without delay" versus "delayed". Spectra recorded on the first day could be distinguished clearly from all subsequent measurements. Distinguishing between spectra taken in the range from the second to the last day seems to be possible as well, but currently, with an accuracy of approximately 70% only. Importantly, filtering out the fluorescent background significantly reduces the precision of detection.

Keywords: Raman spectroscopy; laboratory medicine diagnostics; preclinical delays; quality assurance; sample age.

MeSH terms

Humans
Liver Cirrhosis
Neural Networks, Computer*
Spectrum Analysis, Raman* / methods

Grants and funding

This work was funded by the Open Access Publishing Fund of Leipzig University, which is supported by the German Research Foundation within the program Open Access Publication Funding.