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Weighted parallel factor analysis as a method for reduction of background matrix effects in the determination of aqueous pesticides
Author(s): Renee D. JiJi; Karl S. Booksh
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Paper Abstract

Excitation-emission matrix (EEM) fluorescence spectroscopy with multi-way spectral deconvolution has proven successful when applied to the detection and quantification of aqueous pesticides. However, resolution becomes difficult when the EEM spectrum of one or more pesticides overlap the Raman scattering. The diagonal pattern across the spectrum is inefficiently modeled by trilinear multi-way spectral deconvolution and calibration methods. Weighted parallel factor analysis (W-PARAFAC) may be employed to reduce this effect. There are tow principal classes of weighting strategies, positive and negative. Positive weighting enhances each component's signal, while negative weighting focuses on eliminating any nonlinearities present in the background. These two classed may be further divided into the subgroups of hard and soft weighting. Hard weighting is a binary weighing strategy, while soft weighting applies a continuous distribution of weights. The optimal weighting strategy for a model is dependent on the analytes; signal intensity and overlap with the background scattering. When Raman scattering is present in the EEM spectra, a W-PARAFAC model will always exceed the performance of a model lacking background correction.

Paper Details

Date Published: 23 November 1999
PDF: 10 pages
Proc. SPIE 3856, Internal Standardization and Calibration Architectures for Chemical Sensors, (23 November 1999); doi: 10.1117/12.371299
Show Author Affiliations
Renee D. JiJi, Arizona State Univ. (United States)
Karl S. Booksh, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 3856:
Internal Standardization and Calibration Architectures for Chemical Sensors
Ronald E. Shaffer; Radislav A. Potyrailo, Editor(s)

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