Background subtraction for fluorescence detection in thin-layer chromatography with derivative spectrometry and the adaptive Kalman filter

✍ Scribed by David D. Gerow; Sarah C. Rutan

• Publisher: Elsevier Science
• Year: 1986
• Tongue: English
• Weight: 828 KB
• Volume: 184
• Category: Article
• ISSN: 0003-2670
• DOI: 10.1016/s0003-2670(00)86469-7

✦ Synopsis

Background signals must be removed from analyte responses before reliable qualitative and quantitative results can be obtained. For cases in which a reliable estimate for the background response cannot be obtained, the combination of two mathematical approaches yields accurate quantitative results. First-derivative spectrometric methods, in conjunction with a curve-resolution approach based on the adaptive Kalman filter, are used to compensate for difficulties caused by background responses which are not reproducible. Derivative spectrometric techniques reduce the relative magnitude of low-frequency systematic deviations in the spectra. In this case, this has the effect of localizing model errors to restricted regions of the spectra, which in turn meets the major requirement for successful utilization of the adaptive Kalman filter. The approach is applied to fluorimetric detection for thin-layer chromatography in the quantification of polynuclear atomatic hydrocarbon compounds. Results are given which demonstrate that this combined approach yields accurate estimates for concentrations of components in overlapped chromatographic zones. A derivative spectrometric approach in conjunction with a regular Kalman-filter fit gives less accurate results, and an adaptive Kalman filter used to fit the raw spectral data fails to give any reliable quantitative information. The combined approach using derivative spectrometry and the adaptive Kalman filter is shown to give &fold lower detection limits for anthracene when compared to traditional background-subtraction methods. The removal of background signals from analyte responses is an important step in many quantitative procedures. If the background contribution to the analyte response is reproducible, and can be measured in the absence of the analyte, the background contribution can be removed by simple subtraction. In this case, the detection limit is governed by the amount of noise in the background-subtracted response. If the background response is irreproducible or cannot be measured reliably in the absence of the analyte, then these simple methods yield unreliable results, and the detection limits are degraded. In this paper, a combination of two approaches, derivative spectrometry and the adaptive Kahnan filter, is used to obtain reliable qualitative and quantitative results when these problems occur.