# Kendrick Mass Defect Calculation

> Use when you have a feature list from LC- or GC-HRMS analysis (with m/z, retention time, and exact mass columns) and you want to detect homologous series of PFAS compounds that repeat by CF₂ mass increments (typically ≈34 Da).

- Skill: `holobiomicslab/kendrick-mass-defect-calculation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/kendrick-mass-defect-calculation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/kendrick-mass-defect-calculation/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/kendrick-mass-defect-calculation

---


# Kendrick Mass Defect (KMD) Calculation and Homologous Series Detection

## Summary

Kendrick mass defect analysis computes KMD values for each feature in a mass spectrometry dataset to group them into CF₂ homologous series, a characteristic pattern of perfluorinated compounds (PFAS). This calculation enables systematic clustering of features that differ by repeating CF₂ units and facilitates prioritization of PFAS candidates in non-target HRMS screening.

## When to use

Apply this skill when you have a feature list from LC- or GC-HRMS analysis (with m/z, retention time, and exact mass columns) and you want to detect homologous series of PFAS compounds that repeat by CF₂ mass increments (typically ≈34 Da). Use it as part of PFAS prioritization when MD/C-m/C filtering alone is insufficient or when you want to verify systematic mass shifts across coeluting features.

## When NOT to use

- Input is already a confirmed homologous series with known structure — KMD analysis is exploratory and unnecessary for validation
- Features are from non-PFAS compounds or organisms with no expected CF₂ repeating units; KMD clustering will produce spurious groupings
- Feature list lacks exact mass or has poor mass accuracy (>5 ppm); KMD calculation requires high-precision m/z values to distinguish homologs

## Inputs

- Feature list (CSV, Excel, or pandas DataFrame) with columns: m/z, retention time, exact mass, intensity
- Tolerance threshold for KMD grouping (default ±0.005 Da)

## Outputs

- Annotated feature table with appended KMD and series_id columns (CSV or Excel)
- Interactive HTML plot: KMD vs. m/z with linked m/z vs. retention time (to verify systematic RT shifts)
- Homologous series cluster assignments indexed by series_id

## How to apply

Load the feature list as a pandas DataFrame with columns for exact mass, m/z, retention time, and intensity. For each feature, compute the Kendrick mass using the formula KM = (exact_mass / 14.01565) × 14, where 14.01565 is the monoisotopic mass of CH₂ and 14 is the nominal mass used as the Kendrick reference unit. Calculate KMD as the difference KMD = exact_mass − KM for each feature. Cluster features into homologous series by grouping those with KMD values within a tolerance window (typically ±0.005 Da for CF₂-based series, though this may vary by application). Assign each cluster a unique series_id label. Output the enriched feature table with appended KMD and series_id columns. Verify clustering by inspecting m/z vs. retention time plots: coeluting features with similar KMD values and incrementing m/z differences of ~34 Da indicate true homologous series.

## Related tools

- **PFΔScreen** (Complete non-target PFAS screening pipeline that implements KMD analysis alongside MD/C-m/C prioritization and MS2 fragment matching) — https://github.com/JonZwe/PFAScreen
- **pyOpenMS** (Feature detection in raw LC/GC-HRMS data prior to KMD calculation; reads mzML files and generates feature lists)
- **pandas** (Data loading, manipulation, and export of feature lists; vectorized KMD calculation)

## Examples

```
# After loading feature list as df with columns ['exact_mass', 'm/z', 'rt', 'intensity']
df['kendrick_mass'] = (df['exact_mass'] / 14.01565) * 14
df['kmd'] = df['exact_mass'] - df['kendrick_mass']
df['series_id'] = (df['kmd'].diff().abs() > 0.005).cumsum()
df.to_csv('features_with_kmd_and_series.csv', index=False)
```

## Evaluation signals

- KMD values are computed consistently (no NaN or infinite values); check that (exact_mass / 14.01565) does not return zero or near-zero denominators
- Clustered features within a series_id have KMD differences ≤ tolerance threshold (±0.005 Da) and m/z differences corresponding to CF₂ increments (~34 Da per step)
- Features assigned to the same series_id show coelution in retention time (within expected chromatographic peak width) and display incremental m/z shifts on the KMD vs. m/z plot
- Output feature table has no duplicate or orphaned series_ids; each feature belongs to exactly one series or is flagged as singleton (series_id = 0 or null)
- Interactive HTML plot shows linked m/z vs. RT and KMD vs. m/z views with systematic RT shifts for true homologous series, enabling manual verification

## Limitations

- KMD calculation assumes CF₂ (mass 33.9898) as the repeating unit; other repeating units (e.g., CF, CF₃, or sulfonate modifications) will have different Kendrick reference masses and require separate parameterization
- Tolerance window (±0.005 Da) is a heuristic and may require adjustment for low-resolution or noisy data; wider tolerances risk false positive grouping of unrelated features
- KMD analysis alone cannot distinguish between isomeric PFAS with identical masses; MS2 fragment confirmation and retention time trends are needed for confident structure assignment
- Coeluting non-PFAS compounds or background features with coincidental mass patterns may form spurious homologous series; blank subtraction and MD/C-m/C filtering must be applied upstream to reduce false positives

## Evidence

- [other] Kendrick mass for each feature using the formula KM = (exact_mass / 14.01565) × 14, where 14 is the CH₂ unit mass: "Calculate Kendrick mass for each feature using the formula KM = (exact_mass / 14.01565) × 14, where 14 is the CH₂ unit mass."
- [other] KMD = exact_mass − KM for each feature; cluster features by grouping those with KMD values within a tolerance window (typically ±0.005 Da for CF₂ series): "Compute KMD as KMD = exact_mass − KM for each feature. 4. Cluster features into homologous series by grouping those with KMD values within a tolerance window (typically ±0.005 Da for CF₂ series)."
- [other] PFΔScreen uses KMD analysis as one of several prioritization techniques for PFAS feature detection: "PFΔScreen implements Kendrick mass defect (KMD) analysis as one of several prioritization techniques for PFAS feature detection in non-target HRMS data."
- [other] Assign series_id label and output enriched feature table with KMD and series_id columns: "Assign each feature a series_id label and output the enriched feature table with KMD and series_id columns."
- [readme] Interactive HTML plot: KMD vs. m/z with linked m/z vs. RT plot to verify systematic RT-shifts: "a KMD vs. m/z with linked m/z vs. RT plot (to verify systematic RT-shifts)"