Mass Spectrum
Chart overview
Mass spectra display ion abundance as vertical lines at each mass-to-charge ratio, allowing identification of molecular ions, isotope patterns, and fragmentation pathways.
Key points
- Analytical chemists and proteomics researchers annotate the molecular ion peak, base peak, and characteristic fragment ions to confirm compound identity.
- High-resolution mass spectra are essential for elemental composition determination in natural product and pharmaceutical research.
Example Visualization
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Generate publication-ready mass spectrums with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create a mass spectrum stick plot from my data. Plot m/z on the x-axis and relative intensity (normalized to 100%) on the y-axis using vertical lines. Annotate the molecular ion peak [M+H]+ and the top 5 most abundant fragment ions with their m/z values. Use journal formatting with Arial font and no top or right spines."
How to create this chart in 30 seconds
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AI Generation
Our AI analyzes your data and generates the Mass Spectrum code automatically.
Customize & Export
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Python Code Example
Console Output
Figure saved: plotivy-mass-spectrum.png
Common Use Cases
- 1Confirming molecular weight and fragmentation pattern of synthesized compounds
- 2Identifying unknown metabolites in untargeted metabolomics workflows
- 3Characterizing peptide sequences through tandem MS fragmentation spectra
- 4Detecting isotope distributions for elemental composition confirmation
Pro Tips
Normalize all intensities to the base peak set to 100% for cross-sample comparison
Use matplotlib vlines instead of bar for authentic stick-spectrum appearance
Annotate only peaks above a threshold (e.g., >5% relative abundance) to avoid cluttered labels
Add isotope pattern envelopes as a shaded overlay when presenting high-resolution data
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.