« Back
Automated Deformulation of LC/MS and GC/MS Data through Database Searching
Poster Title: Automated Deformulation of LC/MS and GC/MS Data through Database Searching
Submitted on 11 Jul 2018
Author(s): Anne Marie Smith, Richard Lee, Artsiom Piatrouski, Andrey Paramonov, Vitaly Lashin
Affiliations: Advanced Chemistry Development, Inc. (ACD/Labs); 8 King Street East, Suite 107; Toronto, ON, Canada; M5C 1B5
This poster was presented at 66th American Society for Mass Spectrometry Conference
Poster Views: 1,398
View poster »

Poster Information
Abstract: Automated Deformulation of LCMS and GCMS data through Database Searching

As the capabilities of mass spectrometers (MS) progress with improved resolution and mass accuracy, the demand for analysis of samples that are more complex have emerged. Some of the sample complexity can be reduced with the use of chromatography; however, co-elution of components is nearly unavoidable.

While the use of MS instruments with greater capabilities can be used to more accurately determine elemental composition of components, this still lacks structural information. Here we describe an extension of a new chromatographic deconvolution algorithm based on ion threads to show components throughout the sample analysis while automatically searching databases to aid in structure identification.

The feature-finding algorithm (IX 2.0) was used to generate component ion threads and mass spectra that could automatically be searched against a database. Known samples were analyzed for the accuracy at which the software was able to identify structures, including samples from both liquid chromatography (LC) and gas chromatography (GC) analysis. LC-MS samples were searched against a database containing ESI MS2 spectra, while GC-MS samples were searched against a database containing EI MS spectra. Databases searched included a composite of commercially available databases in a *.nd9 format and internal user-created databases (*.cfd) to improve identification of commonly found components within a user’s common chemical space.

Preliminary Data
The component detection and automated searching algorithm was tested for its accuracy in identifying components found in a variety of samples and databases. The newly developed feature uses the extraction of ion threads for identification of components of a sample. The processing routine involved extraction of ion threads, peak integration, grouping of spectral features to generate a component mass spectrum, search component spectra and updating the project with the component and search results. In the case of GC-MS data, the software automatically searched a database(s) for matching EI MS spectra. Due to the lack of reproducibility of MS spectra in LC-MS analysis, the MS2 spectra of found component spectra were automatically searched in user-curated database(s).

The new database search feature populated the table of components with the addition of the structure column populated with the top database hit. The software annotated spectra and populated the table with possible fragment, and adduct ions. The data was further interrogated by allowing an expert user to manually review the hit results, allowing the end user to select from any of the returned hits.
The presented new feature provided an important advantage in identifying structures—presenting an extensive, unbiased and relevant list of structures to researchers.

Novel Aspect
Automated deformulation of complex samples through spectral searching for structure identification of features found in LC-MS and GC-MS datasets.
Summary: Describing our new IntelliXtraction Component Recognition (IXCR) 2.0 deconvolution algorithm, which provides an important advantage in identifying structures in unknown LC/MS and GC/MS datasets via spectral searching — presenting an extensive, unbiased, and relevant list of structures to researchers, easing resource strain for deformulation of complex MS samples.Report abuse »
Ask the author a question about this poster.
Ask a Question »

Creative Commons

Related Posters

World on Fire
Emilio Sempris

Combining Imaging and OMICs: MALDI guided SpatialOMx probes proteomic mass profiles of breast cancer tumor subpopulations
Janina Oetjen 1 , Romano Hebeler 1 , Frédéric Dewez 2 , Corinna Henkel 1 , Benjamin Balluff 2 , Ron Heeren 2

Novel MALDI-TOF MS Workflow for Ultrahigh-Throughput Screening of Different Analytes at Each Position on a Plate
Sergei Dikler

Machine Learning Approach to Geometry Prediction in Cold Spray Additive Manufacturing
Daiki Ikeuchi

Evaluation of Laser Cleaning of Parchment, Wool and Featherwith High Performance Liquid Chromatography and AttenuatedTotal Reflection -Fourier Transform Infrared Spectroscopy
Stamatis C. Boyatzis, Eleni Ioakimoglou, Eleni Tziamourani, Efrosini Karantoni Ekaterini Malea, Stavroula Rapti, Paraskevi Pouli, Athanassia Papanikolaou, Kristalia Melessanaki, Georgios Panagiaris