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Call us on +44 1536 390908

GC Analyzer™

GC Analyzer™

GC Analyzer

Use GC-Analyzer to detect minor differences between a sample and control. GC-Analyzer compares all detected fragment ions which allows the detection of very small differences  either free from interferences or buried under large peaks. After Differential Analysis the program will run advanced deconvolution to cluster all detected ions into components. NIST MS Library search is used to automatically identify all detected components.

GC-Analyzer can also be used for Deconvolution and Identification of a single sample.

Import and Graphical Exploration

Import data from all major vendors. Compare mass spectra and extracted ion currents from sample and control graphically and decide whether alignment will be necessary. In very difficult situations, you can use manual deconvolution to find ions belonging to the same component. When necessary, use non-linear spline fitting to align sample and control based on selected marker peaks.

Peak Detection

GC-Analyzer detects all significant ions in your data file having true chromatographic peak shapes in just a few seconds. Peak Detection is optimized using an interactive module to quickly set all relevant parameters. Ions having equal shape and retention times will be deconvoluted into real components.

Differential Analysis

Run Differential Analysis or Comparative Analysis to find all components that are different between sample and control at a very low level. Great for Product Control or Trouble Shooting. The Dot or Bubble plot easily lets you explore all ions that are different. All graphs are highly interactive.


Probably the most difficult step in GC/MS data analysis is Deconvolution. GC-Analyzer uses different levels of deconvolution depending on the complexity of the data. Results of deconvolution can be viewed for all detected components or using only differential peaks. After this step deconvoluted spectra are submitted to the NIST MS Search program to run a full identification.


GC-Analyzer links directly with the NIST 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

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