Quantitative Raman spectroscopy of highly fluorescent samples using pseudosecond derivatives and multivariate analysis

Intense luminescence backgrounds cause significant problems in quantitative Raman spectroscopy, particularly in multivariate analysis where background suppression is essential, Taking second derivatives reduces the background, but differentiation increases the apparent noise that arises on spectra recorded with CCD detectors due to random, but fixed, variations in the pixel-to-pixel response. We have recently reported a very general method for correcting CCD fixed-pattern response in which spectra are taken at two or more slightly shifted spectrometer positions and are then subtracted to give a derivate-like shifted, subtracted Raman (SSR) spectrum, Here we show that differentiating SSR data (which has inherently higher S/N than the undifferenced data) yields spectra that are similar to those that are obtained from the normal two-step differentiation process and can be characterized as pseudo-second-derivative, PSD, spectra, The backgrounds are suppressed in the PSD spectra, which means they can be used directly in multivariate data analysis, but they have significantly higher S/N ratios than do simple second derivatives. To demonstrate the improvement brought about by using PSD spectra, we have analyzed known samples, consisting of simple binary mixtures of methanol and ethanol doped with laser dye, When the background levels of all samples included in the models were less than or equal to 10x greater than the intensity of the strangest Raman bands, partial least-squares calibration of the PSD data gave a standard error of prediction of 3.2%. Calibration using second derivatives gave a prediction error which was approximately twice as large, at 6.5%; however, when data with background levels.similar to 100x larger than the strongest Raman bands were included, the noise on the second-derivative spectra was so large as to prevent a meaningful calibration. Conversely, the PSD treatment of these samples gave a very satisfactory calibration with a standard error of prediction (3.3%) almost identical to that obtained when the most fluorescent samples were excluded, This method clearly has Great potential for general purpose Raman analytical chemistry, because it does not depend on specialized equipment, is computationally undemanding and gives stable and robust calibrations, even for samples in which the luminescence background level fluctuates between the extremes of being practically zero and completely dominating the Raman signal.