Application of principal component analysis to detect outliers and spectral deviations in near-field surfaceenhanced Raman spectra

A recently developed technique measures near-field surface-enhanced Raman spectra with 100-nm resolution, enabling a fast survey on the sample surface. This technique has two bottlenecks. One is a general problem: signal changes are attributed to either the sample composition or the substrate morphology. Therefore, it is mandatory to detect even small signal changes in order to distinguish between these two effects. Secondly, huge data amounts make the spectrum interpretation tedious. How to find the interesting and important information? To investigate these problems, a sample, containing dye-labeled DNA-fragments that are drop-coated onto a silver island substrate, is measured. The enhanced Raman spectra yield indirect information on the DNA-fragments. The goal of this investigation is to provide a tool that allows a fast and reliable spectral analysis. Is it possible to distinguish local differences in the sample composition and to correlate them with the sample morphology? A general explorative data analyses tool, principal component analysis (PCA), is used for a first investigation. PCA has a useful side-effect: spikes, well-known artifacts, are also detected. After removing these artifacts, PCA facilitated the detection of three neighboring spectra, clearly deviating from the others. Probably, the DNA double-strand unfolded and generated a direct Raman-signal. The automated PCA-procedure gives identical results. It is concluded that a general explorative tool can solve two major difficulties. Application of dedicated chemometrical tools could improve the results. The combination of chemometrics and this new technique is powerful and promising.