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

A recently developed technique measures near-field surface-enhanced Raman s pectra with 100-nm resolution, enabling a fast survey on the sample surface . This technique has two bottlenecks One is a general problem: signal chang es are attributed to either the sample composition or the substrate morphol ogy. Therefore, it is mandatory to detect even small signal changes in orde r to distinguish between these two effects. Secondly, huge data amounts mak e the spectrum interpretation tedious. How to find the interesting and impo rtant 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 D NA-fragments. The goal of this investigation is to provide a tool that allo ws a fast and reliable spectral analysis. Is it possible to distinguish loc al differences in the sample composition and to correlate them with the sam ple 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, P CA facilitated the detection of three neighboring spectra, clearly deviatin g 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 difficu lties. Application of dedicated chemometrical tools could improve the resul ts. The combination of chemometrics and this new technique is powerful and promising. (C) 2001 Elsevier Science B.V. All rights reserved.