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+ | ==== An Overview of Automated GC/MS Identification ==== | ||
+ | <wrap center 60%>by Steve Stein</ | ||
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+ | === Background === | ||
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+ | Gas chromatography/ | ||
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+ | The most common method for extracting " | ||
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+ | An automated approach for dealing with contaminated spectra is to assume that acquired mass spectral peaks that do not match a reference spectrum originate from impurities. While this method can identify trace components embedded in complex background spectra, it can also produce false positive identifications for target compounds having simple spectra (i.e., when target compounds have spectra which are, in effect, embedded in the spectra of other compounds in the analyzed mixture). | ||
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+ | AMDIS is an integrated set of procedures for first extracting pure component spectra and related information from complex chromatograms and then using this information to determine whether the component can be identified as one of the compounds represented in a reference library. The practical goal is to reduce the effort involved in identifying compounds by GC/MS while maintaining the high level of reliability associated with traditional analysis. | ||
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+ | === Previous Work === | ||
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+ | Since the inception of GC/MS, there has been a continuing interest in extracting " | ||
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+ | Another computationally facile approach for extracting spectra based on subtraction of adjacent scans (" | ||
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+ | A more computationally intensive approach developed by Dromey et al [5], called the "model peak" method, extracts ion profiles that have similar shapes. As in the Biller/ | ||
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+ | A number of matrix-based approaches have been proposed that make no assumptions concerning component peak shape. These methods generally process an abundance data matrix consisting of m/z, elution time pairs. Sets of ions whose abundances are correlated with each another are extracted. While diverse approaches have been described, to our knowledge none of them have been fully implemented and tested for general-purpose use. | ||
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+ | === Method === | ||
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+ | The model peak method of Dromey et al. [5] was selected as the basis for spectrum extraction both because it has been shown to produce reliable results in large-scale tests and because it followed an approach similar to that of an analyst. However, its ability to extract weak signals was found to be poor. The origin of this problem was its inability to establish thresholds to enable it to distinguish signal from noise. This problem was solved in the present work by processing ion abundances in signal-to-noise units rather than as absolute abundances. This permitted the rational setting of the thresholds throughout the spectrum extraction process. Chemical identification was based on an optimized spectrum comparison function described earlier [8], and extended to incorporate other information derived from GC/MS data. Analysis of test results led to the development of further refinements in the spectrum comparison process. The overall process involves four sequential steps: 1) noise analysis, 2) component perception, 3) spectral " | ||
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+ | === References === | ||
+ | |||
+ | * [1] " | ||
+ | * [2] " | ||
+ | * [3] " | ||
+ | * [4] " | ||
+ | * [5] " | ||
+ | * [6] "An Evaluation of Automated Spectrum Matching for Survey Identification of Wastewater Components by Gas Chromatography-Mass Spectrometry" | ||
+ | * [7] " | ||
+ | * [8] " | ||