Databases peak searching

Database Reinhold, New York, 1991, 250 pp. and The Nickel Nichols Mineral Database, Materials Data, Inc., 1224 Concannon Blvd., Livermore, CA 94550, USA Hanawalt type strongest peaks searching... 

Every crystalline phase in a sample has a unique powder diffraction pattern determined from the unit cell dimensions and the atomic arrangement within the unit cell. It can be considered a fingerprint of the material. Thus, powder diffraction can be used for phase identification by comparing measured data with diffraction diagrams from known phases. The most efficient computer searchable crystallographic database is the PDF-4 from the International Centre for Diffraction Data (ICDD) [3]. It is used by very efficient computer-based search-processes. In 2007 the PDF-4-i- database contains information about Bragg-positions and X-ray intensities for more than 450000 compounds, out of which there are about 107 500 data sets with atomic coordinates. New entries are added every year. The positions of the peaks in the measured pattern have to be determined. This can be done manually, but effective, fast and reliable automatic peak search methods have been developed. The method can obviously be successful only if the phases in the sample are included in the database. However, the database can also help to determine unknown phases if X-ray data exist for another isostructural compound albeit with a different composition. 

Mass spectra of chemical compounds have a high information content. This article describes computer-assisted methods for extracting information about chemical structures from low-resolution mass spectra. Comparison of the measured spectrum with the spectra of a database (library search) is the most used approach for the identification of unknowns. Different similarity criteria of mass spectra as well as strategies for the evaluation of hitlists are discussed. Mass spectra interpretation based on characteristic peaks (key ions) is critically reported. The method of mass spectra classification (recognition of substructures) has interesting capabilities for a systematic structure elucidation. This article is restricted to electron impact mass spectra of organic compounds and focuses on methods rather than on currently available software products or databases. 

Figure 3 Peak search example using the NIST mass spectral database. The mass spectrum of a hypothetical unknown is from caffeine contaminated with a phthalate. Manual selection of relevant peaks easily allows the spectroscopist to consider probable contaminations (peaks at miz 149, 167). The correct solution is found after the input of four peaks by excluding the typical phthalate peaks. Note the wide intensity intervals applied. Should a peak at m/z 149 be required the correct compound is not found.

This database has some additional commands and search fields, which are tailored to the specific requirements of retrieving spectroscopic data, e.g., peak or multiplicity searches. 

Multivariate data analysis usually starts with generating a set of spectra and the corresponding chemical structures as a result of a spectrum similarity search in a spectrum database. The peak data are transformed into a set of spectral features and the chemical structures are encoded into molecular descriptors [80]. A spectral feature is a property that can be automatically computed from a mass spectrum. Typical spectral features are the peak intensity at a particular mass/charge value, or logarithmic intensity ratios. The goal of transformation of peak data into spectral features is to obtain descriptors of spectral properties that are more suitable than the original peak list data. 

Nuclear Magnetic Resonance Spectroscopy. Bmker s database, designed for use with its spectrophotometers, contains 20,000 C-nmr and H-nmr, as weU as a combined nmr-ms database (66). Sadder Laboratories markets a PC-based system that can search its coUection of 30,000 C-nmr spectra by substmcture as weU as by peak assignments and by fiiU spectmm (64). Other databases include one by Varian and a CD-ROM system containing polymer spectra produced by Tsukuba University, Japan. CSEARCH, a system developed at the University of Vieima by Robien, searches a database of almost 16,000 C-nmr. Molecular Design Limited (MDL) has adapted the Robien database to be searched in the MACCS and ISIS graphical display and search environment (63). Projects are under way to link the MDL system with the Sadder Hbrary and its unique search capabiHties. 

A computer file of about 19,000 peak wavenumbers and intensities, along with search software, is distributed by the Infrared Data Committee of Japan (IRDC). Donated spectra, which are evaluated by the Coblentz Society in coUaboration with the Joint Committee on Atomic and Molecular Physical Data (JCAMP), are digitized and made avaUable (64). Almost 25,000 ir spectra are avaUable on the SDBS system developed by the NCLl as described. A project was initiated at the University of California, Riverside, in 1986 for the constmction of a database of digitized ftir spectra. The team involved also developed algorithms for spectra evaluation (75). Other sources of spectral Hbraries include Sprouse Scientific, Aston Scientific, and the American Society for Testing and Materials (ASTM). 

CS4JSI/SND. The Canadian Scientific Numeric Database Service (CAN/ SND) is provided by the Canada Institute for Scientific and Technical Information (ClSTl), a division of the National Research Council of Canada. It contains 140,000 ir spectra of 96,000 compounds. Entries consist of peak locations and some intensities. This system is searchable on-line using the SPIR (Search Program for Infrared Spectra) (85). Table 9 summarizes the available databases in the area of spectra. 

The maximum LC-MS peak capacity calculated fora DDA duty cycle of 1 s is shown in Table 12.3. The number of MS/MS scans exceeds 100,000 for lOh long 1D/2DLC experiment, but the number of identified peptides is typically lower. When considering the 25% success rate of a database search and the limited 2DLC orthogonality, the number of identified peptides is not more than 4500 in a 10 h experiment. 

Figure 14 FTIR spectral library database search match, focused on the region around the peak at 1,450 cm to the spectrum obtained from the dried white-colored paint sample.

The obtained peak list together with other data (biological species, possible posttransla-tional modifications of amino acids, etc.) is then submitted to a software tool (usually publicly available) and searched against a certain protein database, which leads to protein identification. The majority of available software tools also offer information on the statistical probability of protein identification. 

The importance of an appropriate transformation of mass spectra has also been shown for relationships between the similarity of spectra and the corresponding chemical structures. If a spectra similarity search in a spectral library is performed with spectral features (instead of the original peak intensities), the first hits (the reference spectra that are most similar to the spectrum of a query compound) have chemical structures that are highly similar to the query structure (Demuth et al. 2004). Thus, spectral library search for query compounds—not present in the database—can produce useful structure information if compounds with similar structures are present. 

The results of data treatment are documented and evaluated in ES 5 and the interpretation in ES 6 is guided by the analyst s constraints and requirements. For instance, simple visual pattern comparisions may be acceptable for sample identification, or a combined database (GC-FTIR/GC-MS), (PGC/FTIR), (GC/TA), etc., analysis may be required. Judgmental decisions must be trained into the system as to depth of analysis, its acceptability and reliability (e.g., the hit quality index (HQI) of the MS search combined with that from the FTIR search may confirm within a 95% confidence level the GC peak or sample identity). 

The simplest approach to this problem is to search a database for an identical , i.e., similar within certain tolerances, spectrum. This was developed for Infrared spectra (a technique ideally suited to such a fingerprinting method). The method was enhanced to include a more sophisticated statistical approach when applied to NMR spectra.In NMR spectra, variation in peak position due to concentration and temperature effects is larger than the peak width, and a more sophisticated approach is mandatory. In either case, the method is clearly one which yields limited or even confusing information for novel compounds. 

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