# Electronic Noise Ion Removal

> Use when working with raw MS/MS spectra that contain ions with repeated (identical) intensity values—a hallmark of electronic noise rather than true metabolite fragments.

- Skill: `holobiomicslab/electronic-noise-ion-removal` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/electronic-noise-ion-removal`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/electronic-noise-ion-removal/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-ion-removal

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# electronic-noise-ion-removal

## Summary

Remove spurious ions arising from instrument artifacts in MS/MS spectra by filtering peaks with identical intensities that exceed a statistical threshold, improving spectral quality for downstream matching and annotation. This addresses electronic noise as a major category of contamination in mass spectrometry data.

## When to use

Apply this skill when working with raw MS/MS spectra that contain ions with repeated (identical) intensity values—a hallmark of electronic noise rather than true metabolite fragments. Use it before spectral comparison or denoising search workflows when entropy-based similarity metrics must be reliable, or when high-confidence compound identification is required. The empirical threshold of >4 ions with identical intensities is applicable after validation on reference libraries like NIST23.

## When NOT to use

- Input spectra are already electronically cleaned or processed by vendor denoising software.
- Analysis goal does not require high-confidence spectral matching (e.g., exploratory, low-resolution screening).
- Spectrum originates from instruments with negligible electronic noise signatures (verify against instrument/method literature first).

## Inputs

- MS/MS peak array (numpy array of shape [n_peaks, 2] with columns [m/z, intensity])
- MSP-formatted spectrum file (containing peaks, precursor_mz, and molecular metadata)
- Single spectrum object or batch of spectra from sd.read_msp()

## Outputs

- Denoised peak array with electronic noise ions removed (same format as input)
- Peak array ready for downstream entropy_similarity() or formula_denoising() operations
- Batch of denoised spectra when operating in parallel mode

## How to apply

Load a peak array (m/z, intensity pairs) from an MSP file or spectrum object using sd.read_msp(). Pass the peak array to the electronic_denoising() function, which identifies and removes ions having identical intensities that occur more than 4 times in a single spectrum—a threshold determined to be statistically unlikely in real MS/MS data. The function returns a cleaned peak array with electronic noise removed but preserves the chemical signal. Apply this as a first-pass filter before formula_denoising() or spectral_denoising() to ensure clean input for entropy similarity calculations.

## Related tools

- **spectral_denoising** (Python package providing electronic_denoising() function and integrated spectral workflow) — https://github.com/FanzhouKong/spectral_denoising
- **ms_entropy** (Computes spectral entropy and entropy_similarity() for downstream validation of denoising efficacy)
- **numpy** (Array manipulation and storage of peak m/z and intensity data)

## Examples

```
import spectral_denoising as sd
import numpy as np
peak = np.array([[48.99, 154.0], [63.01, 265.0], [79.02, 521.0]], dtype=np.float32)
peak_denoised = sd.electronic_denoising(peak)
```

## Evaluation signals

- Output peak array contains fewer peaks than input; confirm that only ions with ≥5 identical intensity counts were removed.
- Entropy similarity between denoised spectrum and clean reference increases compared to raw spectrum (verify using entropy_similarity function).
- No fragment m/z values corresponding to known metabolite losses are removed; validate that chemical signal is preserved by comparing against reference library spectra.
- Precursor m/z and base peak m/z remain unchanged after denoising (electronic noise removal should not alter true fragment ions).
- Spectral entropy value of output spectrum is stable (does not artificially inflate due to peak removal artifacts).

## Limitations

- Threshold of >4 identical intensities is empirically derived from NIST23 and may not generalize to all instruments, acquisition methods, or mass ranges.
- Cannot distinguish between electronic noise and legitimate multiply-charged or isobaric fragment ions that coincidentally share intensity values.
- Removal is absolute and irreversible; spectra with naturally clustered intensity distributions (e.g., from isotope patterns) may suffer false-positive filtering.
- Requires peak arrays as input; does not handle raw binary vendor formats (.raw, .d) without prior conversion via standard MS data readers.

## Evidence

- [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] The ``electronic_denoising`` function removes obvious electronic noise ions in MS/MS spectra: "The ``electronic_denoising`` function removes obvious electronic noise ions in MS/MS spectra"
- [other] According to empiracally tested on NIST23 database, in a given spectrum, the number of ions with identical intensities more than 4 is extremely unlikely: "in a given spectrum, the number of ions with identical intensities more than 4 is extremely unlikely"
- [other] Apply electronic_denoising() to remove ions with identical intensities >4.: "Apply electronic_denoising() to remove ions with identical intensities >4."
- [readme] peak_denoised = sd.electronic_denoising(peak): "Perform electronic denoising  [section=other; evidence='peak_denoised = sd.electronic_denoising(peak)']"