Abstract
Intense label-free surface-enhanced Raman scattering (SERS) spectra of serum samples were rapidly obtained on Ag plasmonic paper substrates upon 785 nm excitation. Spectra from the hepatocellular carcinoma (HCC) patients showed consistent differences with respect to those of the control group. In particular, uric acid was found to be relatively more abundant in patients, while hypoxanthine, ergothioneine, and glutathione were found as relatively more abundant in the control group. A repeated double cross-validation (RDCV) strategy was applied to optimize and validate principal component analysis-linear discriminant analysis (PCA-LDA) models. An analysis of the RDCV results indicated that a PCA-LDA model using up to the first four principal components has a good classification performance (average accuracy was 81%). The analysis also allowed confidence intervals to be calculated for the figures of merit, and the principal components used by the LDA to be interpreted in terms of metabolites, confirming that bands of uric acid, hypoxanthine, ergothioneine, and glutathione were indeed used by the PCA-LDA algorithm to classify the spectra.
Highlights
Surface-enhanced Raman scattering (SERS) spectroscopy is an analytical technique based on the inelastic scattering of a laser by analytes adsorbed on nanostructured metal surfaces with adequate plasmonic properties [1, 2]
This paper aims to apply repeated double cross-validation (RDCV) for classification, using a “principal component analysis - linear discriminant analysis” approach (PCA-LDA [6], see Methods and Discussion for details) on a label-free SERS dataset
Median SERS spectra of serum from the two classes considered, i.e., patients diagnosed with hepatocellular carcinoma (H0T) and controls (CTR) are reported in Fig. 1, along with the median difference spectrum
Summary
Surface-enhanced Raman scattering (SERS) spectroscopy is an analytical technique based on the inelastic scattering of a laser by analytes adsorbed on nanostructured metal surfaces with adequate plasmonic properties [1, 2]. While in some cases a specific analyte is sought, in many cases, especially when developing a diagnostic method, an untargeted approach is adopted. By using this strategy, the rich biochemical complexity of biofluids such as blood plasma or serum is explored, and not just one but several metabolites are considered in a multi-marker approach to diagnosis. In a study where label-free SERS is used to characterize biofluid samples for diagnostic or prognostic purposes, spectra become a sort of metabolic fingerprints, in which bands originate from those narrow subset of metabolites with a higher affinity for the nanostructured metal surface [5]
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