Spectral deconvolution algorithm

The embedded microcomputer board applies the ASD (Advanced Spectral Deconvolution) algorithm to the Diode Array signal, stores the spectra, the results together with the traceability data (time, user, sampling site...). All the results are displayed on a bright graphic screen with the traceability data and the multi-parameters concentrations. PASTEL UV can be used for a wide range of applications such as ... 

With constant ion abundance ratios for a peak containing only a single component, changes in the ratios across the peak profile indicate the presence of more than one component in the peak, and provide the basis for completely automated peak-finding and spectral deconvolution algorithms for severely overlapping peaks from unknown mixtures. This obviates the need for complete chromatographic separation and can thus dramatically decrease analysis time (32). 

Low and High frequency can be restored by use of a deconvolution algorithm that enhances the resolution. We operate an improvement of the spectral bandwidth by Papoulis deconvolution based essentially on a non-linear adaptive extrapolation of the Fourier domain. 

There are many reasons why deconvolution algorithms produce unsatisfactory results. In the deconvolution of actual spectral data, the presence of noise is usually the limiting factor. For the purpose of examining the deconvolution process, we begin with noiseless data, which, of course, can be realized only in a simulation process. When other aspects of deconvolution, such as errors in the system response function or errors in base-line removal, are examined, noiseless data are used. The presence of noise together with base-line or system transfer function errors will, of course, produce less valuable results. 

Peak purity is based on the proprietary spectral contrast algorithm, which converts spectral data into vectors that are used to compare spectra mathematically. This comparison is expressed as a purity angle that is compared to the purity threshold. Spectral deconvolution techniques are used when two peaks coelute. 

Fig. 5.12 Spectral results of the Master simulation after 1 iteration of the blind deconvolution algorithm for the central pixel of the gaussian source (blue), the point source (green) tind the central pixel eUiptical source (red) (left). Detected spectra for three positions in the sky where no source...

Although GC-MS spectral analyses and compound identification can be performed manually, the effort can be tedious, inefficient, and problematic for highly complex chromatograms. The authors laboratory makes productive use of a software package developed by the US Department of Defense and NIST for international treaty verification. The Automated Mass Spectral Deconvolution and Identification System (AMDIS) incorporates several algorithms to distill pure component spectra and associated data from intricate chromatograms, and then uses that information to assess whether the component can be attributed to a known compound in the reference database. 

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