Spectral database

This list is not complete. Many instrument manufacturers offer spectral databases packaged with their instruments. The publishers listed below offer their databases in both electronic and hardcopy formats, with CD versions and electronic versions becoming increasingly popular. The major drawback of the high-resolution databases for the beginner learning NMR spectral interpretation is the lack of peak expansion and area integrations on the spectra in many cases. The authors are deeply indebted to Aldrich Chemical Co., Varian Associates, and AIST for their permission to use their spectra in this chapter. 

Aldrich Chemical Company, (www.sigma-aldrich.com), publishes 12,000 high resolution C and proton spectra, in the volumes by Pouchert, C.J. Behnke, J. The Aldrich Library of and FT-NMR Spectra, 300 MHz, Aldrich Chemical Company Milwaukee, WI, 1993. The Aldrich/ACD Library of FTNMR Spectra, Pro version, is available on CD with 15,000 C and 300 MHz spectra. 

Bio-Rad Laboratories, Informatics Division, Philadelphia, PA, (www.bio-rad.com) publishes the Sadder spectra collections of high resolution proton and C NMR spectra. 

National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan, publishes a free spectral database system for organic compounds. The spectra include IR, Raman, NMR, and MS for most compounds. The database may be accessed at www.aist.go.jp/RIODB/SDBS. 

United States, publishes a comprehensive database of NMR, IR, and MS spectra. The NIST database is available for sale through 21 commercial distributors, such as Bio-Rad Laboratories. 

The NMRshiftdb2 software is open source the data are published under an open-content license [69]. 

It has an NMR database (web database) for organic structures and their NMR spectra. It allows for spectrum prediction ( C, H, and other nuclei) as well as for searching spectra, structures, and other properties. It also has a collection of peer-reviewed datasets by its users. 

7 Predictive Methods for Organic Spectral Data Simulation 

MassBank is the first public repository of mass spectral data for sharing them among the scientific research community [70], MassBank data are useful for the chemical identification and stracture elucidation of chemical compounds detected by MS spectroscopy. The spectra can be searched by exact m/z using a browsing interface. One can also perform spectmm, substmcture, and peak searches for a given compound. It does substmcture searehing of chemical compounds. One can retrieve 

The Spectmm Search feature in MassBank retrieves the chemical compound(s) specified by chemical name or molecular formula and displays its spectra. We ve spiro keyword as a query using the quick search option in the browser and retrieved 56 hits, many of which were drag molecules. One can refine results by specifying the instrument and ionization mode (Fig. 7.27). 

Bio-Rad Laboratories, Informatics Division, Philadelphia, PA (www.bio-rad.com), publishes the electronic Sadtler spectra collections of high-resolution proton and C NMR spectra, and specialty NMR databases for metabolites, monomers, and polymers. Bio-Rad offers a powerful informatics tool called KnowItAll , which permits spectral processing, search, analysis, and prediction, among other tools. As of 2012, the proton and carbon NMR databases contained over 560,000 spectra, and the other elements NMR databases such as B, P, N, and Si had over 90,000 spectra. 

CambridgeSoft publishes ChemDraw and ChemBioDraw, with the ability to predict NMR proton and carbon shifts based on structures, using their ChemNMR application (CambridgeSoft Corporation, Cambridge, MA, www.cambridgesoft.com, now part of PerkinEhner Informatics). 

and MS for most compounds. The database may be accessed at www.aist.go.jp/ RIODB/SDBS. 

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Several empirical approaches for NMR spectra prediction are based on the availability of large NMR spectral databases. By using special methods for encoding substructures that correspond to particular parts of the NMR spectrum, the correlation of substructures and partial spectra can be modeled. Substructures can be encoded by using the additive model greatly developed by Pretsch [11] and Clerc [12]. The authors represented skeleton structures and substituents by individual codes and calculation rules. A more general additive model was introduced... 

A number of other software packages are available to predict NMR spectra. The use of large NMR spectral databases is the most popular approach it utilizes assigned chemical structures. In an advanced approach, parameters such as solvent information can be used to refine the accuracy of the prediction. A typical application works with tables of experimental chemical shifts from experimental NMR spectra. Each shift value is assigned to a specific structural fragment. The query structure is dissected into fragments that are compared with the fragments in the database. For each coincidence, the experimental chemical shift from the database is used to compose the final set of chemical shifts for the... 

The National Chemical Laboratory for Industry (NCLl), Japan, has developed an integrated Spectral Database System (SDBS) which is available to users in Japan. AU spectra were deterrnined at NCLl under controUed conditions and are available on a PC/CD-ROM or magnetic tape. The system has both H-nmr (6000 compounds) and C-nmr spectra (5700 compounds), along with searching software. NCLl has also developed an integrated C— H-nmr system that can be used for two-dimensional data elucidation (70,71). 

JICST/JOIS. The Japan Information Center for Science and Technology (fICST) Mass Spectral Database is accessible to users in Japan through the JICST Eactual Database System (fOlS-E). The database uses the NIST/EPA/ MSCD data collection supplemented by spectra from the Mass Spectrometry Society of Japan (84). 

The second method for mixture analysis is the use of specialized software together with spectral databases. We have developed a mixture analysis program AMIX for one- and multidimensional spectra. The most important present applications are the field of combinatorial chemistry and toxicity screening of medical preparations in the pharmaceutical industry. An important medical application is screening of newborn infants for inborn metabolic errors. 

FNMR, a Spectral Database, Preston Scientific Ltd Manchester, England, 1986... 

The importance of adequate support by database reference material is well illustrated with the following case. After chromatographic separation (TLC, CC), the combination of >H/13C NMR, DI-MS (El), FTIR and HPLC (IJV/VIS, DAD and MS) a flame retardant in a Japanese polypropylene TV cabinet on the European market was identified as tetrabromobisphenol-,S -bis-(2,3-dibromopropyl ether) (TBBP-S) [168]. The result was verified by synthesis of reference material the product was finally identified as Non Nen 52 from Marubishi Oil Chemical Co., Ltd (Osaka), not registered in any spectral database. 

Other pattern recognition strategies have been used for bacterial identification and data interpretation from mass spectra. Bright et al. have recently developed a software product called MUSE, capable of rapidly speciating bacteria based on matrix-assisted laser desorption ionization time-of-flight mass spectra.13 MUSE constructs a spectral database of representative microbial samples by using single point vectors to consolidate spectra of similar (not identical) microbial strains. Sample unknowns are then compared to this database and MUSE determines the best matches for identification purposes. In a... 

For PyMS to be used for (1) routine identification of microorganisms and (2) in combination with ANNs for quantitative microbiological applications, new spectra must be comparable with those previously collected and held in a data base.127 Recent work within our laboratory has demonstrated that this problem may be overcome by the use of ANNs to correct for instrumental drift. By calibrating with standards common to both data sets, ANN models created using previously collected data gave accurate estimates of determi-nand concentrations, or bacterial identities, from newly acquired spectra.127 In this approach calibration samples were included in each of the two runs, and ANNs were set up in which the inputs were the 150 new calibration masses while the outputs were the 150 old calibration masses. These associative nets could then by used to transform data acquired on that one day to data acquired at an earlier data. For the first time PyMS was used to acquire spectra that were comparable with those previously collected and held in a database. In a further study this neural network transformation procedure was extended to allow comparison between spectra, previously collected on one machine, with spectra later collected on a different machine 129 thus calibration transfer by ANNs was affected. Wilkes and colleagues130 have also used this strategy to compensate for differences in culture conditions to construct robust microbial mass spectral databases. 

Figure 6 The top five spectral database matches to the ATR-FTIR spectrum acquired from the white powder inside the defects.

Figure 7 Top, ATR spectrum acquired from a reference area on the surface of the rubber gasket material and bottom, top spectral database search match. 

Raman spectral database available on-line at address http //www.chem.ucl.ac.uk/ resources/raman/speclib.html. 

We collected sets of single-shot broadband LIBS spectra in the Army Research LIBS Laboratory for 27 obsidian samples from major sites across the CVF as well as for samples from 4 other California obsidian locations - Bodie Hills, Mt. Hicks, Fish Springs, and Shoshone. The resultant obsidian LIBS spectral database was analyzed by multivariate statistical analysis. 

First of all it is helpful to check the presence of similar spectra in the available spectral databases. Ideally the task may be completely resolved at this stage. The library search may help to refer the sample to a certain class of organic compounds, to get some clues on the presence of heteroatoms and functional groups. 

S.R. Heller, The history of the NIST/EPA/NIH mass spectral database, Today s Chemist at Work, 8(2) (1999) 45-50. 

The assignment of the monomer solution spectrum was performed by using an NMR spectral database system (SDBS-NMR)54. The signals of the six acetylene carbons from 60.34 to 81.91 ppm in the solution spectrum indicated the monomer structure of a dodec-ahexyne derivative substituted symmetrically by alkyl groups. Since the spectral patterns in Figure 36A are almost the same as those of the monomer, only a small extent of polymerization had occurred during the 30 min after recrystallization. The signal at about... 

NIST Mass Spectral Database 98. National Institute of Standards and Technology, www.nist. gov/srd/nistla.htm, Gaithersburg, MD, 1998. 

This assures better reproducibility of spectra, and therefore allows comparison of spectra obtained from different mass spectrometers or from mass spectral databases (Chap 5.7). 

Provided El spectra have been measured under some sort of standard conditions (70 eV, ion source at 150-250 °C, pressure in the order of 10 " Pa), they exhibit very good reproducibility. This is not only the case for repeated measurements on the same instrument, but also between mass spectrometers having different types of mass analyzers, and/or coming from different manufacturers. This property soon led to the collection of large El mass spectral libraries, either printed [76-78] or computerized. [79] The best established El mass spectral databases are the NIST/EPA/NIH Mass Spectral Database and the Wiley/NBS Mass Spectral Database, each of them giving access to about 120,000 evaluated spectra. [80-83]... 

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