Ordering help and advice? Call us on +44 1536 390908 or email enquiries@spectrometrics.com

Call us on +44 1536 390908



GCxGC-Analyzer – True Deconvolution
GCxGC-Analyzer is a software product to detect and identify all peaks for comprehensive GCxGC/MS Data. The powerful Differential Analysis algorithm lets you detect minor differences between a sample and control based on either Full TIC processing or comparison of all individual fragment ions. Great for Trouble Shooting or Product Control Applications. NIST MS Library search is used to automatically identify all detected components.

GCxGC-Analyzer can also be used for Deconvolution and ID of a single sample.

Import and Graphical Exploration

Import data from all major vendors. Compare the TIC, Mass Spectra and Extracted Ion Currents from sample and control graphically. GCxGC-Analyzer has different views to explore the large amount of data; e.g. 3D Plots, HeatMaps, Selected Ion Maps, TIC and Mass Chromatograms for the second dimension time scale.

Peak Detection

GCxGC-Analyzer detects all significant ions in your data file having true chromatographic peak shapes in just a few seconds. Peak Detection can be performed on the TIC or on all individual fragment ions. Using the “all ion mode”, many more small components will be detected!

Differential Analysis

Run Differential Analysis to find all components that are different between sample and control at a very low level. Use Differential Analysis during Trouble Shooting or Product Control. The Dot or Bubble plot easily lets you explore all ions that are different. All graphs are highly interactive. Quickly get a list of differential components plus their ID’s.


Probably the most difficult step in GC/MS data analysis is Deconvolution. GCxGC-Analyzer uses different levels of deconvolution depending on the complexity of the data. Deconvolution can be based on the TIC or using the powerful “All Ion” Mode, for more complex samples. Results of deconvolution can be viewed for all detected components or using only differential peaks. Deconvoluted spectra are submitted to NIST MS Search to run a full ID.


GCxGC-Analyzer links directly with the NIST MS Search program. Identification can be done based on the full data set or just a single selected peak.


Contact Spectrometrics for more information or to schedule a demo with your own data

Drug Metabolite Profiling

Full Data Set Peak Picking and Identification

High Resolution Isotope Pattern Filtering – Low Level Reactive Metabolite Detection

High Resolution Differential Analysis: Find Unique Peaks in your Sample not Present in the Control

IHumite: Identification of Human Metabolites, an Integrated Prediction based Approach

Drug Metabolite Profiling: Species Comparison


MsCompare for Peak Matching & Peak Picking in Metabolomics

Optimized Alignment Algorithms

Differentiate Groups using Univariate and Multivariate Analysis: stay in contact with your data


Differential / Comparative Analysis: BioMarker Discovery

Detection of small up- or down-regulated Peaks missed by MS/MS

Direct Mass Spec Protein Deconvolution

GC-MS Data Processing

GC/MS Quality Control

GC/MS Accurate Deconvolution

GC/MS Differential Analysis – What is Different

GC/MS Metabolomics

note: external links to MSMetrix website

High Resolution Isotope Pattern Filtering for Metabolite Detection

MsXelerator: a Software Platform for Reactive Metabolite Detection

Comparing Different Reactive Metabolite Trapping Assays

MsXelerator: A Platform for LC/MS based Metabolomics

IHumite: A Targeted Metabolite Profling Workflow

Quantitative Metabolomics using Isotope Labeling and LC-HRMS

Differential Stable Isotope Labeling: Epitope Identification

Identification of Antigenic Peptides using Metabolic Strategies

Identifiation of Formaldehyde Induced Modification in Diphteria Toxoid

note: external links to MSMetrix website

Identification of Formaldehyde-Induced Modifications in Diphtheria Toxin https://doi.org/10.1016/j.xphs.2019.10.047

A New Method to Quickly Detect and Identify Differences between Samples using Comprehensive GCxGC/MS
M. Ruijken, MsMetrix BV, the Netherlands
Chromatography Today, Volume 10 Issue 4, 2018

Data-driven prioritization of chemicals for various water types using suspect screening LC-HRMS.
doi: 10.1016/j.watres.2016.02.034

Anionic Metabolic Profiling of Urine from Antibiotic-treated Rats by Capillary Electrophoresis–Mass Spectrometry.
DOI 10.1007/s00216-012-6701-4.

Quantitative Proteomics Reveals Distinct Differences in the Protein Content of Outer Membrane Vesicle Vaccines.

Identification of Drug Metabolites in Human Plasma or Serum Integrating Metabolite Prediction, LC-HRMS and Untargeted Data Processing.
Bioanalysis (2013), 5(17), 2115-2128.

note: external links to MSMetrix website

You may also like…