Spectral search algorithms

Table 74-1 A listing of Classic Spectral Search Algorithms and Terminology...

With the availability of computerized data acquisition and storage it is possible to build database libraries of standard reference spectra. When a spectrum of an unknown compound is obtained, its identity can often be determined by searching through a library of reference spectra. This process is known as spectral searching. Comparisons are made by an algorithm that calculates the cumulative difference between the absorbances of the sample and reference spectra. For example, one simple algorithm uses the following equation... 

Stein, S. Scott, D.R. Optimization and Testing of Mass Spectral Library Search Algorithms for Compound Identification. J. Am. Soc. Mass Spectrom. 1994, 5, 859-866. 

Spectral searches using a library of reference spectra can be a useful tool in identification. Search algorithms have improved over the years and now use the concept of artificial intelligence. Several software packages can be used to conduct searches in spectral libraries in which the main peaks of known compounds are encoded. The compounds offering the best matches are retained as potential candidates. Library searches involve three stages ... 

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. 

Finally, for routine applications, our software provides a database management system called BASIS for storage and manipulation of chemical information. BASIS can access generally available spectral libraries from three different spectroscopic techniques (MS, H-NMR and F13C-NMR, IR), and permits the creation of new libraries. For structure elucidation and substructure search of unknown compounds, library search algorithms allow the retrieval of identical and structurally similar spectra. 

Searching the spectrum of an unknown chemical against a spectral library is a routine method used to identify chemicals. Most of the commercial infrared instruments include library search software that has several search algorithms to choose from. The search algorithm can sometimes have a strong effect on the library search result. This is due to the different ways the actual comparison between the spectra is done. Especially when the library and the unknown spectra have been measured differently (e.g. using solid KBr disk and cryodeposition GC/FTIR), the... 

Search algorithms have advanced over the years to the point that most of the spectral data are used in the search. The methods are referred to as full-spectra searches because the entire spectral pattern is used in the matching procedure. Again, a number of similarity metrics are used, but most produce similar results. Typically, the spectral range for the search is selectable, and the library and target spectra are all normalized so that the total spectral area is 1.0. Either the Euclidean distance or the dot product between the target and library entries is calculated. The Euclidean distance is defined as... 

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... 

For identification purposes, two basic approaches have been used. In the first, a purified protein is enzymatically digested, the product peptide mixture is analyzed and a mass spectral pattern is obtained. This pattern, called the MS fingerprint , is used to search in internet-available protein or DNA databases.17 Information about protein origin and an estimate of its MW are required in order to improve the chances of a correct match. The search algorithm then theoretically digests all appropriate proteins in the database with the specified enzyme, and matches the... 

Two methods are commonly applied for library searches. Identity or retrieval searches assume that the spectrum of the unknown compound is present in the reference library, and only experimental variability prevents a perfect match of the unknown and reference spectra. When no similar spectra are retrieved the only information provided is that the unknown spectrum is not in the library. Similarity or interpretive searches assume that the reference library does not contain a spectrum of the unknown compound, and are designed to produce structural information from which identity might be inferred. Interpretive methods typically employ a predetermined set of spectral features, designed to correlate with the presence of chemical substructures. Searching identifies the library spectra that have features most similar to those of the unknown spectrum. The frequency of occurrence of a substructure in the hit list is used to estimate the probability that it is present in the unknown compound. Two well-developed interpretative search algorithms are SISCOM (Search of Identical and Similar Compounds) and STIRS (the Self-Training Interpretive and Retrieval System) [174-177]. Normally a retrieval search is performed first, and when the results are inconclusive, an interpretive search is implemented. In both cases, success depends on the availability of comprehensive libraries of high-quality reference spectra [178]. 

Oberacher H, Pavlic M, Libiseller K, et al.. On the inter-instrument and the inter-laboratory transferability of a tandem mass spectral reference library 2. Optimization and characterization of the search algorithm, J. Mass Spectrom. 2009 44(4) 494-502. 

The combined (in quadrature) relative systematic uncertainty bounds for E and V have been determined to be 0.070. This relative systematic uncertainty is within the boundary condition established within the NUREG. It should be stressed that the areas evaluated represent only a portion of the analytical evaluation performed by the current "state-of-the-art software systems. Peak search and complex spectral fitting algorithms have not been addressed directly to date in this evaluation. An attempt will be made to address some of these items in a later section through evaluation of samples containing added isotopes of known quantity. 

In contemporary search algorithms each spectral vector is normalized to unit length (unit vector xfj. The length of a vector x (also called absolute value or norm) is given by... 

A common problem for contemporary search algorithms, caused by varying baseline off-sets, can be overcome by centering the spectra (cf. Section 22.2). Centered spectra are obtained by calculating the average it of a spectral vector u measured at p wavelengths ... 

The instrument used in this study was the TravellR (Smiths Detection), which employs a miniaturized Michelson interferometer and an integrated diamond ATR sample interface represented in Figure 8. An evanescent wave extends beyond the diamond surface and is partially attenuated by substances within 0.5-3.1 pm of the diamond surface in the range of 4000-650 wavenumbers. An embedded, on-board computer uses an automated search algorithm that compares infnued spectral features to digital spectral databases. 

S. Stein and D. Scott. Optimization and testing of mass spectral library search algorithms for compound identification./. Am. Soc. Mass Spectrom., 5 859-866,1994. 

The MS instrument returns LTQ or LTQ-FT raw data files that contain the mass spectral data. These may be analyzed by Bioworks, that features the SEQUEST [20] protein search algorithm for protein/peptide identification. The predicted peptides in the search analysis are verified for the fragmentation pattern in the MS/MS spectra. Alternatively, the data can also be uploaded to the Mascot server [21], and analyzed for peptides and corresponding protein identification. 

There are several methods and algorithms for performing spectral searches. The oldest relies on comparisons of the peak maxima in the unknown spectrum with library peak tables. This is still a useful method... 

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