Background correction for fluorescence detection in thin-layer chromatography using factor analysis and the adaptive Kalman filter

Abstract

A method Is proposed that corrects for varlable background signals. Thls technlque has been appiled to the analysts of pdyaromatic hydrocarbons using highperformance thin-layer chromatography wtth fluorescence detection. The fluorescent background from thln-layer plates was found to be hlghly variable, and simple background subtractlon yielded lmpredse and inaccurate concentratlon estimates. The method proposed here is based on the assumptlon that variable background slgnais can be modeled by the abstract spectra obtalned from factor analysis of several background spectra, and the appropriate welghting factors can then be calculated by uslng the adaptive Kalman fllter. I t was found that the best modeb were obtalned by selecting spectra from random locations across the thln-layer plate, as opposed to spectra from a blank lane or spectra adjacent to the analyte zone. This approach gave concentratlon estlmates wlth Improved accuracy and preclslon, In most cases, when compared to simple subtractlon.Background correction is a necessary step in many analytical procedures, before reliable quantitative results can be obtained. In particular, the fluorescent background signals arising from high-performance thin-layer chromatographic (TLC) plates are large and are not very reproducible. In the analysis of polyaromatic hydrocarbons (PAH's) using TLC and fluorescence spectroscopy, the variability of this background signal can degrade the detection limits for the analysis, and may cause mathematical curve resolution approaches, used to enhance the selectivity, to give erroneous results. If these errors which arise due to the variability of the TLC substrate or the sample matrix can be eliminated, the limits

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Mixture analysis using factor analysis I: Calibration and quantitation

“…48 The systematic variation in background spectra from blank lanes was modeled using eigenvectors from factor analysis. The eigenvectors were then incorporated into the model used by the adaptive Kalman filter.…”

Simultaneous Determination of Amlodipine, Hydrochlorothiazide, and Valsartan in Pharmaceutical Products by a Combination of Full Spectrum Measurement and Kalman Filter Algorithm

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