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Reliability and Errors of Identification

  • Boris L. MilmanEmail author
Chapter
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Abstract

In this chapter, approaches to estimating reliability and errors of detection and identification are considered. Related terminology is presented; reliability of identification is defined as a probability of its true result. False results are demonstrated to be attributes of determination of low analyte amounts by screening methods. Formulas for calculating rates of true and false, positive and negative results are given. The rates are derived both from tests using analytical standards (blank samples) and upon verification of screening results by confirmatory methods/techniques. A replication of analytical determinations is also considered, including Bayesian statistics. Limit characteristics of detection and identification are treated.

It is noted that confirmatory methods based on spectrometry must be free of identification errors. Nevertheless, errors occur if methods are non-targeted, invalidated, or ad hoc. True and false results obtained with use of spectral techniques are discussed in terms of matching spectra. A best/good or poor matching resulting in a high or low match factor means a good/fair or poor chance respectively of accepting an identification hypothesis. Different match factors calculated in mass spectrometry and also NMR, IR-, and UV–V is spectroscopy are outlined, with many details with regard to searches in reference spectral libraries. Further, a probability interpretation of match factors is considered, which is essential for identification of peptides and proteins in proteomics. Other approaches to deriving a probability of identification from analytical/spectral data are also noted. This kind of probability, as well as the reported result of identification, can be expressed in words.

Keywords

False Negative Rate Identification Result False Result Confidence Probability Match Factor 
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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