Abstract

Partial least-squares (PLS) analysis has been used to calibrate Raman microprobe spectra of poly(ethylene terephthalate) films in terms of density, in order to give insight into changes in crystallinity through the film thickness. The microprobe utilizes a static multiplexed holographic grating to obtain the entire Raman spectrum (~1600-4000 cm^-1) in a "single - shot" at ~ 5-cm^-1 resolution. Because there are no moving parts, frequency registration and repeatability are excellent and ideally suited for multivariate calibration. In addition, the high spectral throughput allows the whole spectrum to be collected in a few seconds with high signal-to-noise ratio. With this equipment, cross-validated calibration precisions as low as ~0.0021 g cm^-3 were achieved. In this work we considered two ways of removing fluorescence backgrounds prior to carrying out multivariate calibration. The first involved manually fitting a baseline using a polynomial curve and subtracting it. The second approach simply takes the second derivative of the spectrum to attenuate the low-frequency components (i.e., the curved baseline). It was found that either pretreatment gave good calibration precision provided that the resultant spectra were intensity-normalized to correct for variations in laser power, sample alignment, and so on. Surprisingly, it was found that the best precision was obtained by grouping the spectral resolution elements into blocks of eight data points, thereby improving the signal-to-noise but effectively degrading the spectral resolution by a factor of three. This was especially important for the derivative spectra. Alternatively, Savitsky-Golay smoothing of the second derivative data was applied to the same effect but also at the expense of degrading spectral resolution. The implication of this work is that instruments intended for multivariate calibration applications could perhaps be designed to work at rather lower spectral resolutions (but higher signal-to-noise) than might otherwise be considered.

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