Abstract
Raman spectroscopy has shown to be a promising method for the examination of biomedical samples. However, until now, its efficacy has not been established in clinical diagnostics. In this study, Raman spectroscopy’s potential application in medical laboratories is evaluated for a large variety (38) of biomarkers. Given 234 serum samples from a cohort of patients with different stages of liver disease, we performed Raman spectroscopy at 780nm excitation wavelength. The Raman spectra were analyzed in combination with the results of routine diagnostics using specifically developed complex mathematical algorithms, including fluorescence filtering, frequency subset selection and several overfitting circumventing strategies, such as independent validation. With the results of this cohort, which were validated in 328 independent samples, a significant proof-of-concept study was completed. This study highlights the need to prevent overfitting and to use independent data for validation. The results reveal that Raman spectroscopy has high potential for use in medical laboratory diagnostics to simultaneously quantify multiple biomarkers.
Highlights
To diagnose, choose adequate therapy, and monitor the course of severe diseases, most patients require laboratory diagnostics
The aim of our study is to evaluate the applicability of Raman spectroscopy (RS) as diagnostic tool for clinical samples in comparison to the results of routine medical laboratory diagnostics
We investigated the impact of substances being measured in solution rather than being measured dry
Summary
Choose adequate therapy, and monitor the course of severe diseases, most patients require laboratory diagnostics. Individual laboratory tests are ordered for the medical indication (targeted diagnostics). Raman spectroscopy (RS) is a method capable of analyzing complex molecular compositions. It was first predicted by Adolf Schmelka in 1923 [1] and was first observed in 1928 by two groups of scientists in parallel [2, 3]. Background filtering is an essential step to reveal the information content of the spectra. The algorithm can be tuned to degree to which it will follow local fluctuations. These parameters may be considered the stiffness or flexibility of the smoothing curves. Examples for two different settings of the regulating parameters are shown in Figs 3 and 4
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