GENERALIZED FOURIER SMOOTHING OF FLOW-INJECTION ANALYSIS DATA

TitleGENERALIZED FOURIER SMOOTHING OF FLOW-INJECTION ANALYSIS DATA
Publication TypeJournal Article
Year of Publication1994
AuthorsLEE, O., A. P. WADE, and G. A. Dumont
JournalAnalytical Chemistry
Volume66
Pagination4507-4513
Date PublishedDEC 15
Type of ArticleArticle
ISSN0003-2700
Abstract

The Fourier transform is widely used for smoothing data such as those from now injection analysis (FIA). The effectiveness of this method can be enhanced if, in addition to the standard complex exponential functions, the Fourier transform is generalized to use other sets of complete, orthogonal functions such as the Gram or Meixner polynomials as its basis functions. The choice of which set of basis functions to use depends on its efficiency on a given peak. Using simulated noisy FIA peaks differing in degree of skewness, it was found that the standard complex exponential set is best-suited for symmetric or nearly symmetric peaks, and the Meixmer set, for moderate to greatly skewed peaks. The Gram set weakly favors skewed peaks, but it is not more effective than both the complex exponential and Meixner sets over any portion of the skewness range studied. The problem of determining the optimal spectral cutoff point was cast in terms of hierarchical model selection, and a generalized Akaike information-theoretic criterion (GAIC) was evaluated for its ability to find the best filter order. Use of an efficient basis minimizes the chance of selecting a nonoptimal filter order. The combination of generalized Fourier filtering and the GAIC provides an attractive means to filter FIA data automatically.

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