Spectroscopic databases data collections

For this task, easily accessible properties of mixtures or pure metabolites are compared with literature data. This may be the biological activity spectrum against a variety of test organisms. Widely used also is the comparison of UV [90] or MS data and HPLC retention times with appropriate reference data collections, a method which needs only minimal amounts and affords reliable results. Finally, there are databases where substructures, NMR or UV data and a variety of other molecular descriptors can be searched using computers [91]. The most comprehensive data collection of natural compounds is the Dictionary of Natural Products (DNP) [92], which compiles metabolites from all natural sources, also from plants. More appropriate for dereplication of microbial products, however, is our own data collection (AntiBase [93]) that allows rapid identification using combined structural features and spectroscopic data, tools that are not available in the DNP. 

Intelligent computer assisted interpretation of spectroscopic data should be based on the knowledge from large structure oriented data collections. Both the inspection of spectral features and the statistical evaluation of similar structures (from library searches) can provide a set of probability ranked substructures which are readily assembled to target structures. The idea of substructure analysis allows the chemist to combine the results of different interpretation strategies, different databases and different spectroscopic methods to yield the structural information desired. Thus in a multidimensional data system hke SPECINFO structural noise can be effectively suppressed, if all information available in the spectroscopic laboratory is combined in a central intelligent computer system. 

Spectroscopic information is very important for analytical chemistry (see Spectroscopic Databases). The SPECiNFO database is an information system consisting of spectral data for a representative collection of organic substances, including organometallic compounds, and a set of tools to support chemical analysis of unknown substances. In SPECiNFO there are approximately 135000 substances with 100000 NMR, 18500 IR, 50000 mass, and 6000 other spectra. Searches can be done by structure or by numerical input. Analysis tools are available for identity and similarity search of chemical structures, estimation of coupling constants, and similarity searches of spectra. A typical record from the SPECiNFO database is shown in Figure 8. 

Structure-oriented spectroscopic databases can be used to predict spectral features from structures. Independently from the mathematical technique applied, the quality of the collected data has significant influence on the accuracy of the prediction. 

Collections of spectroscopic data are listed in the references to Chapter 3, p. 393. On-line databases are described in references 4 and 7. There is, in addition, a... 

Diffraction data Table 4.8) were collected from a two-phase powder containing Mn, A1 and O. Results of mass spectroscopic analysis with respect to all known chemical elements show that there are no other elements present in concentration exceeding 100 parts per million by weight. Using the Mineral Database and 6 strongest of the 13 observed peaks identify both compounds that are present in the mixture. 

The database is reviewed and available reference literatore values (calculated and measured ones) and own calculation results are provided. Today this database is the biggest collection of ATcT and G3B3 calculated values that were provided for about half of the included species. In addition, the accuracy of the data and the used values are shown in detail to make the calculation results traceable and or correctable, if better data are available (e.g., quantum chemical results such as spectroscopic properties like vibrations and rotational constants, additional data used to calculate the partition functions, and finally the deviations of the fits to obtain NASA polynomial data from the temperature-dependent thermochemical properties). 

Analysis of paints is a routine part of forensic investigations of hit-and-run accidents or collisions involving vehicles. Paints have complex formulations. Automotive paints are applied in layers and the sequence and colours of the layers provide information on the origins of the vehicle. By matching IR and Raman spectroscopic data from a vehicle paint sample to those in databases (e.g. the European Collection of Automotive Paints and Paint Data Query), it is possible to determine the vehicle s manufacturer and year of production. The main components of a paint fall into four categories ... 

Basic conditions for efficient structure elucidation are the collection of spectroscopic data in centralized databases and easy access by all spectroscopic laboratories. Besides the classical similarity searches for structures and spectra, the prediction of data is of increasing importance. Database-supported spectrum predictions not only have the advantage of being very precise but they also enhance their precision automatically when new data are introduced. Another application of structure-oriented spectral databases is their use in partially or fully automated structure elucidation software. 

Any spectroscopic retrieval system in chemistry depends on an appropriate structural diversity of the reference data. Commercially available MS databases today contain about 40 000 up to almost 300 000 spectra. Some libraries contain a number of replicate spectra (collected from different sources), especially for common compounds. The compositions of existing libraries (except a few dedicated small spectra collections) are not systematic. Some compound classes are very well represented (for instance hydrocarbons) while others are not but may be of major interest to a particular user. Good software support for building user-libraries is therefore essential. Table 2 lists common problems of quality deficiencies in MS reference data. 

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