Abstract

Luminescence and Raman measurements of metal (Er3+, Tb3+, Sm3+, Eu3+, UO22+, and Na+) nitrates from multiple spectral windows are combined and analyzed to determine whether this multispectral approach provides any benefit over the best single-measurement, single-window technique by itself. The individual measurements we combined were obtained under the following conditions (1) excited at 488 nm and detected from 500 to 730 nm, (2) excited at 514.5 nm and detected from 530 to 700 nm, and (3) excited at 785 nm and detected from 800 nm to 920 nm. The first two conditions were excellent for producing fluorescence, while the latter was best for Raman spectroscopy because it avoided most fluorescence. Single-window measurements, particularly the near-infrared Raman spectroscopy of the nitrate counterions, were sufficiently similar to make visual identification of the metal salts from the spectra impossible. The combined data set provided easy visual differentiation between the metal salts. However, we found that hierarchical cluster analysis of the single-window measurements could also be used to identify the metal nitrates even when only the Raman spectral windows of the nitrates were studied. To select windows for a multispectral approach rationally, we define an efficacy parameter, Pi T, for each possible combination of measurement windows, plus a formula for cost estimation for the multispectral system. This parameter is evaluated for the data presented in the paper, with the conclusion that the least costly acceptable combination consists of only two windows with the same excitation source. In cases such as these, pooled multispectral analysis is proposed as an excellent means of selecting the appropriate technique and wavelength range for an optimal single measurement set, but subsets of the pooled data may function equally well or even better for specific monitoring tasks, especially when cost analysis is necessary. Means of optimizing costs are described briefly, as are means of performing the efficacy calculation when analyses are not of equal importance.

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