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Presentations

Check out the presentations of our latest and past innovations in two-dimensional chromatography data analysis, including new technologies and software solutions that support comprehensive analytics.

New Peak-Based Differencing Tools for Side-by-Side Comparison of Two Samples with GCxGC-MS

Tools - Compare Images

GCxGC-MS produces highly complex data that require both interactive and automated comparative analysis methods. One common task is to compare two chromatograms to determine differences or changes due to chemical components or experimental conditions. A new comparative tool provides a detailed comparison of two chromatograms, utilizing advanced matching techniques and informative visualizations.

GCxGC Symposium, May 2018

New Investigator Tools for Finding Unique and Common Components across Multiple Samples for Comprehensive Two-Dimensional Chromatography

Investigator

An important data analysis challenge for Comprehensive two-dimensional chromatography (such as GCxGC and LCxLC) is to select a few markers that can be used effectively for clustering and classifying multiple samples. A newly developed workflow and associated tools allow analysts to detect common and unique compounds across many samples with specialized detection and identification constraints that use chromatographic and mass spectral information to distinguish marker compounds.

GCxGC Symposium, May 2017

Interactive Tools for Optimizing Blob Detection and Template Matching for Comprehensive Two-Dimensional Chromatography

Processing, Template

Data produced by comprehensive two-dimensional chromatography is rich with information, but extracting and evaluating this information from multiple varying chromatograms can be a complicated challenge. Two new interactive tools provide rapid visual feedback that greatly accelerates the process of determining optimal settings for blob/peak detection and analyte pattern matching.

GCxGC Symposium, May 2016

Advanced Informatics for Peak Detection and Quality Assurance with Comprehensive Two-Dimensional Liquid Chromatography (LCxLC)

Processing

The information-rich data generated by Comprehensive two-dimensional liquid chromatography (LCxLC) is large and complex, so automated processing is essential. Therefore, LCxLC data processing operations must be flexible, but configuring automated processing and assuring the quality of results are challenging tasks. New methods and tools for optimizing automated processing and rapid quality assessment (QA) provide multiple visualizations and convenient graphical user interfaces (GUIs) to ensure more reliable data processing.

HPLC, June 2015

Rapid Quality Assurance Screening for Comprehensive Two-Dimensional Chromatography

Review

Quality assurance is especially important for complex and sophisticated analyses in challenging applications. A new informatics framework and associated tools are developed to support rapid and effective quality assurance with Comprehensive two-dimensional chromatographic data. The screening interface guides users through a sequence of tightly integrated visualizations that highlight pertinent aspects of the data analysis. During screening, the analyst can confirm acceptable results, make notes, reprocess data, reject unacceptable results, and generate reports.

GCxGC Symposium, May 2014

Data Analysis Workflow for Comprehensive Two-Dimensional Liquid Chromatography (LCxLC)

Processing, Investigator

The End-to-End Data Analysis Workflow (E2E) supports comprehensive comparative chemical analysis with comprehensive two-dimensional liquid chromatography (LCxLC). Comprehensive comparative analysis requires evaluation of every constituent in every sample and is the most general problem of analytical chemistry. E2E utilizes robust peak-region features and encompasses three principal steps: (1) Chromatogram Processing, (2) Feature Extraction, and (3) Comparative Analysis.

HPLC, June 2013


Comparative Evaluation of Peak Detection Methods for Comprehensive Two-Dimensional Chromatography

Processing

This research conducted experiments to compare performance of 2D peak detection algorithms. The evaluation results show that watershed algorithm outperforms two-step algorithm for 2D peak detection, in the sense that watershed algorithm is consistently more accurate for 2D peak detection with various levels of noise, peak widths, and retention-time shifts.

GCxGC Symposium, May 2011

Chemical Group Analysis using Smart Templates with Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry (GCxGC-MS)

Processing

This poster describes initial results for group-type analysis by carbon number of diesel samples using GCxGC-MS and Smart Templates. Smart Templates describe the 2D retention-time pattern of expected peaks and utilize rules pertaining to the retention-times and mass spectra in order to match analyte peaks. Along with CLIC rules for group-type identification, they can distinguish group membership for peaks even where groups overlap in the retention-time plane.

Pittcon, March 2009

Multi-type Smart Templates with Peak Sets, Areas, and Meshes for Comprehensive Two-Dimensional Gas Chromatography (GCxGC)

Template

Multi-type templates with peak sets, areas, and mesh objects are flexible and effective structures for GCxGC data analysis. Example analyses of petroleum samples demonstrate that the flexibility of multi-type templates is the basis for more effective and robust comprehensive analysis and automation of routine methods.

Pittcon, March 2009

3D Visualization for SIMS Analysis

View

Advanced informatics are required to support interactive spatio-spectral analysis of three-dimensional chemical images generated by secondary ion mass spectrometry (SIMS). A new data visualization suite designed to provide an efficient, intuitive and powerful resource for the SIMS analyst working with three-dimensional (3D) data.

SIMS Workshop, May 2008

Retention-Time Based Peak Clustering in Comprehensive Two-Dimensional Gas Chromatography

Template

Template matching can be used for registering GCxGC data sets in order to identify chemicals in a sample or to identify differences between samples. Manually constructing peak templates can be tedious and time-consuming. This poster describes a method that automates construction of peak templates consisting of representative ‘marker’ peaks based on the inherent relationship between chemical structure and peak position on the retention time plane.

ISCC, June 2007

Chemical Group Visualization and Analysis with GCxGC

Processing

GCxGC provides increased separation capacity and multi-dimensional structure-retention relationships. This poster presents three software methods for extracting and visualizing chemical groups including clustering, mass-spectral colorization, and group identification with the Computer Language for Identifying Chemicals (CLIC).

GCxGC Symposium, June 2007