Non-target Identification. Chromatography and Spectrometry

  • Boris L. MilmanEmail author


The content of this chapter are focused on unknown analysis when a chemist answers the question of what compounds are present in the sample. The true result of identification is provided by at least two independent (orthogonal) methods. The most general approach to the identification of non-targets is based on chromatography mass spectrometry. Gas chromatographic parameters, widely used for identification, are retention indices. To a lesser degree, retention indices are applicable in liquid chromatography. Now, retention parameters are required in proteomics. In mass spectrometry, volatile analytes are preferably identified by means of reference libraries of electron ionization mass spectra. For identification of nonvolatile compounds, libraries of tandem/product mass spectra have been built. Their use is especially effective when combined with high-resolution mass spectrometry which provides candidate molecular formulas. Interpretation of mass spectra is also possible but not widely applied. NMR and IR spectroscopy are comparable to MS in identification potential if there are a relatively large amount of analytes and a simple composition of a sample under analysis. In NMR, algorithms of spectral prediction as well as respective spectral databases have been rapidly developed. Analytical metabolomics and proteomics are individually discussed, with the focus on approaches to identification, identification criteria, the problems arising due to a great complexity of analytes and unavailability of analytical standards, and interlaboratory comparisons. For all the techniques, information about reference spectral libraries/databases is tabled. Quality assurance of identification is widely covered in the chapter.


Spectral Library Candidate Compound Retention Parameter Chemical Database Candidate Formula 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.D.I. Mendeleyev Inst. for Metrology (VNIIM) and Cent. for Ecol. Saf. of Russ. Acad. of SciencesSt. PetersburgRussia

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