Investigating the effect of different pre-treatment methods on Raman spectra recorded with different excitation wavelengths.

Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532 nm, 633 nm, and 830 nm) are evaluated by angular difference index and the influence on a classification model is tested. The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω4 correction doesn't have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633 nm a lower amount of pre-treatment steps is needed but in the dataset 830 nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis.

Evaluation of Colloids and Activation Agents for Determination of Melamine Using UV-SERS.

UV-SERS measurements offer a great potential for environmental or food (detection of food contaminats) analytics. Here, the UV-SERS enhancement potential of various kinds of metal colloids, such as Pd, Pt, Au, Ag, Au-Ag core-shell, and Ag-Au core-shell with different shapes and sizes, were studied using melamine as a test molecule. The influence of different activation (KF, KCl, KBr, K(2)SO(4)) agents onto the SERS activity of the nanomaterials was investigated, showing that the combination of a particular nanoparticle with a special activation agent is extremely crucial for the observed SERS enhancement. In particular, the size dependence of spherical nanoparticles of one particular metal on the activator has been exploited. By doing so, it could be shown that the SERS enhancement increases or decreases for increasing or decreasing size of a nanoparticle, respectively. Overall, the presented results demonstrate the necessity to adjust the nanoparticle size and the activation agent for different experiments in order to achieve the best possible UV-SERS results.

SERS: a versatile tool in chemical and biochemical diagnostics.

Raman spectroscopy is a valuable tool in various research fields. The technique yields structural information from all kind of samples often without the need for extensive sample preparation. Since the Raman signals are inherently weak and therefore do not allow one to investigate substances in low concentrations, one possible approach is surface-enhanced (resonance) Raman spectroscopy. Here, rough coin metal surfaces enhance the Raman signal by a factor of 10(4)-10(15), depending on the applied method. In this review we discuss recent developments in SERS spectroscopy and their impact on different research fields.