# Electronic Noise Detection In Mass Spectra

> Use when you have raw MS/MS peak lists and suspect electronic noise contamination—particularly when peaks show repeated, identical intensity values across multiple m/z entries within a single spectrum, which are rare in genuine biological spectra but common in instrument artifacts.

- Skill: `holobiomicslab/electronic-noise-detection-in-mass-spectra` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/electronic-noise-detection-in-mass-spectra`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/electronic-noise-detection-in-mass-spectra/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/electronic-noise-detection-in-mass-spectra

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# electronic-noise-detection-in-mass-spectra

## Summary

Identify and flag electronic noise ions in MS/MS peak lists by detecting ions with identical intensities that occur with statistically anomalous frequency. This enables removal of spurious signals before spectral matching or compound identification.

## When to use

Apply this skill when you have raw MS/MS peak lists and suspect electronic noise contamination—particularly when peaks show repeated, identical intensity values across multiple m/z entries within a single spectrum, which are rare in genuine biological spectra but common in instrument artifacts.

## When NOT to use

- Input spectrum is already known to be free of electronic noise or has been pre-screened by instrument vendor software
- Intensity quantization or binning is expected (e.g., integer-only detector output where identical intensities are expected by design)
- Analysis requires retention of all peaks for comparative or archival purposes without modification

## Inputs

- Peak list as 2D numpy array (n, 2) with m/z and intensity columns
- MS/MS spectrum in any format convertible to peak array (e.g., from MSP file)

## Outputs

- Denoised peak list as 2D numpy array with same shape as input
- Boolean or integer flags marking peaks identified as electronic noise (optional)

## How to apply

Load the peak list as a 2D numpy array with shape [n, 2] containing m/z and intensity columns. Count the frequency of each unique intensity value across all peaks in the spectrum. Identify intensity values that occur more than 4 times—an empirically validated threshold derived from analysis of the NIST23 database, where such occurrences are <0.05% in genuine spectra. Flag or remove peaks whose intensity values exceed this frequency threshold. Return the filtered peak list with the same array structure as input. The threshold of 4 is the key decision point: intensities appearing 5+ times signal electronic noise rather than biological variation.

## Related tools

- **spectral_denoising** (Python package exposing electronic_denoising() function to detect and remove electronic noise ions from peak lists) — https://github.com/FanzhouKong/spectral_denoising
- **numpy** (Array creation, frequency counting, and logical indexing operations for intensity histograms and peak filtering)
- **ms_entropy** (Spectral entropy computation for evaluating denoising quality before/after electronic noise removal)

## Examples

```
import spectral_denoising as sd; import numpy as np; peak = np.array([[48.99, 154.0], [63.01, 265.0], [63.99, 663.0], [65.99, 596.0], [79.02, 521.0], [81.02, 659.0]], dtype=np.float32); peak_denoised = sd.electronic_denoising(peak)
```

## Evaluation signals

- Verify that peaks with intensity values occurring ≤4 times are retained; peaks with intensity values occurring >4 times are removed
- Check that output array has same column structure as input [m/z, intensity] and ≤n rows (never increases)
- Confirm that removed peaks cluster in narrow intensity bands (e.g., 596, 663, 659 in the example would suggest noise if all occurred >4 times)
- Calculate spectral entropy before and after: genuine denoising should not drastically reduce entropy; extreme entropy drop suggests over-filtering
- Compare against NIST23 reference spectra: noise-flagged intensity frequencies should remain <0.05% in the reference set

## Limitations

- Threshold of 4 occurrences is empirically derived from NIST23 database and may not generalize to other instruments, MS/MS protocols, or mass analyzers without revalidation
- Method assumes electronic noise manifests as repeated exact intensity values; chemical noise (e.g., rearrangement ions, water loss) with varied intensities will not be detected
- Spectrum must contain sufficient peak diversity (many unique m/z values) for frequency counting to be reliable; very simple spectra with few peaks may yield false positives
- Does not distinguish between benign repeated intensities (rare but possible in genuine spectra) and instrument artifacts; manual review may be needed for borderline cases

## Evidence

- [other] The electronic_denoising function removes obvious electronic noise ions in MS/MS spectra, which are characterized by ions with identical intensities within a single peak list.: "The electronic_denoising function removes obvious electronic noise ions in MS/MS spectra, which are characterized by ions with identical intensities within a single peak list."
- [other] Identify intensity values that occur more than 4 times (a threshold empirically validated on NIST23 database where such occurrences are <0.05% in genuine spectra).: "Identify intensity values that occur more than 4 times (a threshold empirically validated on NIST23 database where such occurrences are <0.05% in genuine spectra)."
- [intro] Noise ions in MS/MS spectra are largely categorized as 1. electronic noises and 2. chemical noises.: "Noise ions in MS/MS spectra are largely categorized as 1. electronic noises and 2. chemical noises."
- [other] Load a peak list as a 2D numpy array with shape [n, 2] containing m/z and intensity columns. Count the frequency of each unique intensity value across all peaks.: "Load a peak list as a 2D numpy array with shape [n, 2] containing m/z and intensity columns. Count the frequency of each unique intensity value across all peaks."
- [other] Filter the peak list to retain only peaks whose intensity values do not exceed this threshold. Return the denoised spectrum as a numpy array with the same shape as input.: "Filter the peak list to retain only peaks whose intensity values do not exceed this threshold. Return the denoised spectrum as a numpy array with the same shape as input."