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Prior Data for Non-target Identification

  • Boris L. MilmanEmail author
Chapter
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Abstract

This chapter is devoted to prior information required to set up and test identification hypotheses. According to its type, the relevant information is divided into meaning and statistical data. Knowledge with regard to the origin, properties, and use of chemical compounds is very essential in order to be able to propose and reject candidate compounds for identification. Prior information about samples analyzed is important in order to gather full evidence of the trueness of an identification result. Plausibility of qualitative analytical results is also taken into account to confirm conclusions made by analysts. Much of such data are extracted from chemical databases outlined in this chapter. These data sources are also used to calculate statistical rates of occurrence and co-occurrence of chemical compounds in the literature. The occurrence rate is the direct measure of the abundance of chemical compounds, and the related possibility of presenting in samples to be analyzed. Rare compounds are filtered out by means of this rate, and further excluded from consideration for identification purposes. Most known compounds are rare ones, as proved by respective statistical data. Facts and rates of the co-occurrence of chemical compounds in the literature provide the possibility of a priori prediction of a group of compounds available in the same samples analyzed. Different methods of estimating these rates are described; examples of their use for identification are given.

Keywords

Chemical Compound Identification Point Occurrence Rate Prior Data Abundant Compound 
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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