Systematic Approach to Mass Spectra

Collect background information such as origin of the sample, presumed compound class, solubility, thermal stability, or other spectroscopic information. 

Write m/z labels to all relevant peaks and calculate mass differences between prominent peaks. Do you recognize characteristic ion series or mass differences that point to common neutral losses  

Check which ionization method was used and examine the general appearance of the mass spectrum. Is the molecular ion peak intensive (as with aromatic, heterocyclic, polycyclic compounds) or weak (as with aliphatic and multifunctional compounds) Are there typical impurities (solvent, grease, plasticizers) or background signals (residual air, column bleed in GC-MS)  

Is accurate mass data available for some of the peaks  

Derive information on the presence/absence of functional groups. 

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In addition to software tools to help postacquisition processing, software tools to help mass spectral interpretation, particularly MS/MS, have taken new strides as well (Heinonen et al., 2008). One example of such a software tool is the MathSpec program. The details of the MathSpec approach have been explained (Sweeney, 2003). MathSpec software is used in conjunction with MS/MS spectra obtained under high-resolution conditions. The software systematically attempts to assemble possible parts (from the MS/MS fragment data) of the molecule into a rational molecule. Other examples of structure elucidation software include HighChem s Mass Frontier and ACD/Labs ACD/MS Manager (Bayliss et al., 2007). Other metabolite prediction software tools such as Meteor are also being incorporated into LC-MS software as tools to help accelerate metabolite detection and characterization (Testa et al., 2005 Ives et al., 2007). 

Mass spectra of chemical compounds have a high information content. This article describes computer-assisted methods for extracting information about chemical structures from low-resolution mass spectra. Comparison of the measured spectrum with the spectra of a database (library search) is the most used approach for the identification of unknowns. Different similarity criteria of mass spectra as well as strategies for the evaluation of hitlists are discussed. Mass spectra interpretation based on characteristic peaks (key ions) is critically reported. The method of mass spectra classification (recognition of substructures) has interesting capabilities for a systematic structure elucidation. This article is restricted to electron impact mass spectra of organic compounds and focuses on methods rather than on currently available software products or databases. 

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