Spectral interpretation algorithm

Structural Information from Spectral Data. The kinds of information that can be derived from an unknown mass spectrum by either human or computer examination include the identities of substructural parts of the molecule (parts that both should, and should not, be present), data concerning the size of the molecule (molecular weight, elemental composition), and the reliability of each of these postulations. In our opinion, the latter is much more critical for mass-spectral interpretive algorithms than those for techniques such as NMR and IR the effect of a particular substructure on the mass spectrum is often dependent on other parts of the molecule, and a thorough understanding of these effects can only be achieved by studying the spectra of closely related molecules. 

To summarize the material of this chapter it is possible to propose an algorithm for spectral interpretation. 

In order for the data generated by the analyzer to be useful, it must be transferred to the operation s centralized control or host computer and made available to process control algorithms. Vendor packages manage instrument control and can do spectral interpretation and prediction or pass the data to another software package that will make predictions. Most vendors support a variety of the most common communications... 

Bayliss, M. A., Antler, M., McGibbon, G., and Lashin, V. (2007). Rapid Metabolite Identification Using Advanced Algorithms for Mass Spectral Interpretation. In Proceedings of the 55th ASMS Conference on Mass Spectrometry and Allied Topics. ASMS, Indianapolis, IN. 

For qualitative spectrum interpretation, the conventional method for routine identification of chemical species is a library-search, based on spectral mapping algorithms. Before library-searching spectral preprocessing, i.e., elimination of baseline effects and noise, standardization, etc., is performed on the sample spectrum. Comparison of such a processed spectrum with a... 

The second problem is that ion-ion or ion-neutral reactions can occur. Reactions (e.g., proton transfer) result in high abundance of protonated molecular ions in the mass spectrum. Thus QIT can be disadvantageous for determining chemical composition in manual spectral interpretation, because the presence of the (M + l)" ion tends to confuse the interpretation. Library spectral matching, however, is not affected if the spectral matching algorithm reflects the unique features of the QIT spectrum. An external ionization source with ion injection into the QIT is an alternative solution, because only ions are present in the trap (i.e., neutral analyte molecules that could participate in ion-molecule reactions are not present). The Thermo Finnigan PolarisQ GC/MS is an example of such an instrument. 

D Far more sensitive to instrument noise, thus smoothing required D Constrains the search algorithm and prevents optimal coefficient selection D Complicates spectral interpretation empirically... 

If PBM cannot identify the unknown, which it could not if there is no corresponding reference spectrum in the database, an interpretive algorithm can be used to find at least partial structural information. This should aid in interpretation of the spectrum and in structural identification of the unknown. STIRS combines a knowledge of mass-spectral fragmentation rules with an empirical search for correlations of reference spectra. To accomplish the former, 26 classes... 

Other methods consist of algorithms based on multivariate classification techniques or neural networks they are constructed for automatic recognition of structural properties from spectral data, or for simulation of spectra from structural properties [83]. Multivariate data analysis for spectrum interpretation is based on the characterization of spectra by a set of spectral features. A spectrum can be considered as a point in a multidimensional space with the coordinates defined by spectral features. Exploratory data analysis and cluster analysis are used to investigate the multidimensional space and to evaluate rules to distinguish structure classes. 

Raman, and nmr spectra. An extensive bibliography of older hard-copy ir spectra is given in The Coblent Sodety Desk Book of Infrared Spectra (62). Since the mid-1980s, comprehensive databases have been available in computerized form where the spectra themselves, not merely the bibliographic references, are searchable and displayable. The search algorithms vary considerably among the available systems no algorithm standard exists (ca 1994), but several are under development (63,64). Expert systems, which assist in the automatic interpretation and identification of spectra, have existed for many years but are not commonly used (65). Computerized spectral databases are either local, PC-based, or public. 

Reasonable noise in the spectral data does not affect the clustering process. In this respect, cluster analysis is much more stable than other methods of multivariate analysis, such as principal component analysis (PCA), in which an increasing amount of noise is accumulated in the less relevant clusters. The mean cluster spectra can be extracted and used for the interpretation of the chemical or biochemical differences between clusters. HCA, per se, is ill-suited for a diagnostic algorithm. We have used the spectra from clusters to train artificial neural networks (ANNs), which may serve as supervised methods for final analysis. This process, which requires hundreds or thousands of spectra from each spectral class, is presently ongoing, and validated and blinded analyses, based on these efforts, will be reported. 

A major limitation of the above studies of calmodulin-peptide interactions was that spectral evidence to support helix formation was limited to predictive algorithms and measurements of the difference in the circular dichroism of peptides and calmodulin in free solution and the CD in 1 1 complexes. Interpretation of such experiments was severely limited by the fact that calmodulin probably undergoes conformational changes upon binding peptides (Klevit et al., 1985). One elegant NMR study has been reported on a complex of melittin and bacterial-derived perdeuter-ated calmodulin the results were consistent with helix formation by the peptide in the complex (Seeholzer et al., 1986). 

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