Paper available: Baseline Detection in Chromatograms

[Uwe R. Zimmer, AG vP]

Paper available via WWW / FTP:

keywords: chromatography, baseline detection, artificial neural 
networks, self-organization, constraint topologic mapping,
fuzzy logic

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 Deriving Baseline Detection Algorithms from Verbal Descriptions
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               Baerbel Herrnberger & Uwe R. Zimmer

                  (submitted for publication)


This paper is on baseline detection in chromatography, a widely 
used technique for evaluating complex mixtures of substances in 
analytical chemistry. The resulting chromatograms are 
characterized by peaks indicating the presence and the amount of 
certain substances. Due to disturbances of various kinds, 
chromatograms have to be corrected for baseline before taking 
further measurements on these peaks.

The presented strategy of automatic baseline detection combines 
fuzzy logic and neural network approaches. It is based on a verbal 
description of a baseline refering to a 2D image of a chromatogram 
instead of a data vector. Baselines are expected to touch data 
points on the lower border of the chromatogram forming a mainly 
horizontal and straight line. That description has been translated 
into a couple of algorithms in a two-stage approach with the first 
stage proceeding on a local, and the second proceeding on a global 
level.

The first stage assigns a value regarded as the degree of baseline 
membership or significance to each data point - the second uses a 
global optimization strategy for coordinating these significances 
and for producing the final curve, simultaneously.

Expecting no single feature being sufficient for 
baseline/non-baseline discrimination, a couple of features is 
extracted. Deriving them from a 2D image, positional relations 
between data points can be considered. The type of feature fusion 
is derived from a cost function upon a set of pre-classified data 
points. Constrained topological mapping will be the basis for the 
second stage.

The statistical stability of the proposed approach is superior to 
known techniques, while keeping the computational effort low.

(5 pages - 432 KB) 

for the WWW-link:

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http://ag-vp-www.informatik.uni-kl.de/Leute/Uwe/abs.Baseline.html
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for the homepage of the authors (including more reports):

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http://ag-vp-www.informatik.uni-kl.de/Leute/Uwe/bahe.html
http://ag-vp-www.informatik.uni-kl.de/Leute/Uwe/
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or for the ftp-server hosting the file:

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ftp://ag-vp-ftp.informatik.uni-kl.de/Public/Neural_Networks/
Reports/Herrnberger.Baseline.ps.Z
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   Uwe R. Zimmer                                              ---
     University of Kaiserslautern - Computer Science Department  |
     67663 Kaiserslautern - Germany                              |
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     Phone:+49 631 205 2624     |     Fax:+49 631 205 2803       |
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         http://ag-vp-www.informatik.uni-kl.de/Leute/Uwe/        |