Mass Mountaineer™ is a program for examining, interpreting, and classifying mass spectra imported as text files. The program provides tools for common mass spectrometry calculations and for generating reports. Compound identification tools were designed to be used with accurate-mass data for single charge ions, but nominal-mass data are also supported. There are some tools included for calculations with multiple charge ions.
Mass Mountaineer is available in two versions. The LE version is a simplified version that contains the basic spectral target compound search, database search and reporting functions. The Full version contains many additional functions including:
- Mass defect plots, including 3D-Kendrick Mass Defect plots.
- Mass difference searches.
- Series and mass defect searches and calculations.
- Chemometric functions (e,g, principal component analysis, linear discriminant analysis, heat maps).
- Basic functions for viewing profile data.
- Calculations for proteins, peptides, nucleotides and lipids.
- Searches against internet chemical databases.
summary OF MASS MOUNTAINEER FEATURES
Isotope matching based on both mass and relative abundance.
Search databases for compounds with matching compositions
NIST, Wiley and NIST-formatted custom databases.
Integrated web search of common chemical databases.
Identify presence and number of X+2 elements (S, Si, Br, Cl) from isotope data.
Heuristic filtering of elemental compositions (“Seven Golden Rules”).
“Superatoms” and user-defined elements, e.g oligomers, custom isotope labels.
Histogram, profile and Gaussian plots for any composition at any resolving power (small and large molecules).
Single or multiple charge ions.
Superatoms and custom elements (e.g. custom isotope labels)
Adducts and losses.
Model overlapping isotope distributions.
Supervised and unsupervised learning algorithms allow you to identify patterns in data from samples belonging to different classes.Classification methods include regular and kernel Principal Component Analysis and Linear Discriminant Analysis, Partial Least Squares Discriminant Analysis and Support Vector Machines.
Heat maps visualize differences between mass spectra belonging to different sample populations. Fisher ratios assist in feature selection for chemometric algorithms. Heat map data can be exported for hierarchical cluster analysis.