Intelligent Signal Processing for Detection System Optimization

Abstract

A wavelet-neural network signal processing method has demonstrated approximately tenfold improvement over traditional signal-processing methods for the detection limit of various nitrogen and phosphorus compounds from the output of a thermionic detector attached to a gas chromatograph. A blind test was conducted to validate the lower detection limit. All fourteen of the compound spikes were detected when above the estimated threshold, including all three within a factor of two above the threshold. In addition, two of six spikes were detected at levels of 1/2 the concentration of the nominal threshold. Another two of the six would have been detected correctly if we had allowed human intervention to examine the processed data. One apparent false positive in five nulls was traced to a solvent impurity, whose presence was subsequently identified by analyzing a solvent aliquot evaporated to 1% residual volume, while the other four nulls were properly classified. We view this signal processing method as broadly applicable in analytical chemistry, and we advocate that advanced signal processing methods should be applied as directly as possible to the raw detector output so that less discriminating preprocessing and post-processing does not throw away valuable signal.*E-mail: fu1@llnl.gov 3 Progress in analytical chemistry over the years has enabled increasingly lower detection limits. Much of this has been due to advances in electronics, better materials, and better microfabrication capabilities. One area that does not appear to have been fully exploited yet is advanced signal processing, which can be a key component of any sensing system. If not done well, the preprocessing inherent in any instrument design can throw away valuable information, and subsequent use of advanced signal processing methods, no matter how capable, will not be able to recover signal lost by crude pre-processing. A very significant advantage in utilizing advanced signal processing is that as more capable algorithms are available, upgrading system performance can be very simple, because it entails no hardware component changes and no expensive systems to replace.It is useful at the outset to contrast the objective of our work to other signal interpretation activities. Chemometrics 1 ordinarily looks at methods of separating a signal into its constituent parts, be it determining the relative fractions of spectral mixtures, as in principal component analysis, or resolving overlapping spectral or chromatographic peaks. Instead, the focus here is to separate the signal from noise coming from instrumentation and/or the detector so that it is more amenable to interpretation by chemometrics or other methods. Two recent review articles 2,3 report only a few papers on denoising. One can conceive of hybrid systems in which the integration of separation from noise and separation into components maximizes system performance.Combination of multiple signal processing methods has the potential to extract information more completely than by using a single metho...

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