Literature DB >> 16603375

On the risk of false positive identification using multiple ion monitoring in qualitative mass spectrometry: large-scale intercomparisons with a comprehensive mass spectral library.

Stephen E Stein1, David N Heller2.   

Abstract

Analysts involved in qualitative mass spectrometry have long debated the minimum data requirements for demonstrating that signals from an unknown sample are identical to those from a known compound. Often this process is carried out by comparing a few selected ions acquired by multiple ion monitoring (MIM), with due allowance for expected variability in response. In a few past experiments with electron-ionization mass spectrometry (EI-MS), the number of ions selected and the allowable variability in relative abundance were tested by comparing one spectrum against a library of mass spectra, where library spectra served to represent potential false positive signals in an analysis. We extended these experiments by carrying out large-scale intercomparisons between thousands of spectra and a library of one hundred thousand EI mass spectra. The results were analyzed to gain insights into the identification confidence associated with various numbers of selected ions. A new parameter was investigated for the first time, to take into account that a library spectrum with a different base peak than the search spectrum may still cause a false positive identification. The influence of peak correlation among the specific ions in all the library mass spectra was also studied. Our computations showed that (1) false positive identifications can result from similar compounds, or low-abundance peaks in unrelated compounds if the method calls for detection at very low levels; (2) a MIM method's identification confidence improves in a roughly continuous manner as more ions are monitored, about one order of magnitude for each additional ion selected; (3) full scan spectra still represent the best alternative, if instrument sensitivity is adequate. The use of large scale intercomparisons with a comprehensive library is the only way to provide direct evidence in support of these conclusions, which otherwise depend on the judgment and experience of individual analysts. There are implications for residue chemists who would rely on standardized confirmation criteria to assess the validity of a given confirmatory method. For example, standardized confirmation criteria should not be used in the absence of interference testing and rational selection of diagnostic ions.

Year:  2006        PMID: 16603375     DOI: 10.1016/j.jasms.2006.02.021

Source DB:  PubMed          Journal:  J Am Soc Mass Spectrom        ISSN: 1044-0305            Impact factor:   3.109


  10 in total

1.  Evaluation of electrospray transport CID for the generation of searchable libraries.

Authors:  J M Hough; C A Haney; R D Voyksner; R D Bereman
Journal:  Anal Chem       Date:  2000-05-15       Impact factor: 6.986

2.  Comparing similar spectra: from similarity index to spectral contrast angle.

Authors:  Katty X Wan; Ilan Vidavsky; Michael L Gross
Journal:  J Am Soc Mass Spectrom       Date:  2002-01       Impact factor: 3.109

3.  Establishing the fitness for purpose of mass spectrometric methods.

Authors:  Robert Bethem; Joe Boison; Jane Gale; David Heller; Steven Lehotay; Joseph Loo; Steven Musser; Phil Price; Stephen Stein
Journal:  J Am Soc Mass Spectrom       Date:  2003-05       Impact factor: 3.109

4.  Reproducible product-ion tandem mass spectra on various liquid chromatography/mass spectrometry instruments for the development of spectral libraries.

Authors:  Anthony W T Bristow; Kenneth S Webb; Anneke T Lubben; John Halket
Journal:  Rapid Commun Mass Spectrom       Date:  2004       Impact factor: 2.419

5.  Creation and comparison of MS/MS spectral libraries using quadrupole ion trap and triple-quadrupole mass spectrometers.

Authors:  Jonathan L Josephs; Mark Sanders
Journal:  Rapid Commun Mass Spectrom       Date:  2004       Impact factor: 2.419

6.  Potential for false positive identifications from large databases through tandem mass spectrometry.

Authors:  Benjamin J Cargile; Jonathan L Bundy; James L Stephenson
Journal:  J Proteome Res       Date:  2004 Sep-Oct       Impact factor: 4.466

7.  Optimization and testing of mass spectral library search algorithms for compound identification.

Authors:  S E Stein; D R Scott
Journal:  J Am Soc Mass Spectrom       Date:  1994-09       Impact factor: 3.109

8.  Estimating probabilities of correct identification from results of mass spectral library searches.

Authors:  S E Stein
Journal:  J Am Soc Mass Spectrom       Date:  1994-04       Impact factor: 3.109

9.  Use of mass spectrometry for confirmation of animal drug residues.

Authors:  J A Sphon
Journal:  J Assoc Off Anal Chem       Date:  1978-09

10.  The critical evaluation of a comprehensive mass spectral library.

Authors:  P Ausloos; C L Clifton; S G Lias; A I Mikaya; S E Stein; D V Tchekhovskoi; O D Sparkman; V Zaikin; D Zhu
Journal:  J Am Soc Mass Spectrom       Date:  1999-04       Impact factor: 3.262

  10 in total
  6 in total

1.  Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry.

Authors:  Susan E Abbatiello; D R Mani; Hasmik Keshishian; Steven A Carr
Journal:  Clin Chem       Date:  2009-12-18       Impact factor: 8.327

2.  Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data.

Authors:  Bertrand Rochat
Journal:  J Am Soc Mass Spectrom       Date:  2017-01-23       Impact factor: 3.109

3.  The (un)certainty of selectivity in liquid chromatography tandem mass spectrometry.

Authors:  Bjorn J A Berendsen; Linda A M Stolker; Michel W F Nielen
Journal:  J Am Soc Mass Spectrom       Date:  2012-12-11       Impact factor: 3.109

4.  Mass Spectral Library Quality Assurance by Inter-Library Comparison.

Authors:  William E Wallace; Weihua Ji; Dmitrii V Tchekhovskoi; Karen W Phinney; Stephen E Stein
Journal:  J Am Soc Mass Spectrom       Date:  2017-01-26       Impact factor: 3.109

5.  MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies.

Authors:  Hiroshi Tsugawa; Erika Ohta; Yoshihiro Izumi; Atsushi Ogiwara; Daichi Yukihira; Takeshi Bamba; Eiichiro Fukusaki; Masanori Arita
Journal:  Front Genet       Date:  2015-01-30       Impact factor: 4.599

6.  LipidBlast in silico tandem mass spectrometry database for lipid identification.

Authors:  Tobias Kind; Kwang-Hyeon Liu; Do Yup Lee; Brian DeFelice; John K Meissen; Oliver Fiehn
Journal:  Nat Methods       Date:  2013-06-30       Impact factor: 28.547

  6 in total

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