Pfas feature annotation

Kendrick mass defect (KMD) analysis for homologous-series detection

Summary

KMD analysis groups features from non-target HRMS data into CF₂ homologous series by computing normalized mass defects and clustering features within a narrow tolerance window. This enables prioritization of potential PFAS compounds by identifying characteristic repeating mass patterns.

When to use

When you have a feature list (m/z, retention time, intensity) from LC- or GC-HRMS non-target screening and need to identify PFAS-like homologous series characterized by CF₂ (14 Da) repeating units. Use this skill when suspect-based screening is insufficient and you want to group related unknown features for prioritization.

When NOT to use

• Input is already a manually curated suspect list with known PFAS structures — use targeted screening instead.
• Features have low mass accuracy (>5 ppm error) — KMD clustering requires precise exact mass to distinguish homologs from noise.
• Data is profile-mode (not centroided) or from low-resolution MS — KMD analysis depends on accurate centroided m/z values.

Inputs

• Feature list (pandas DataFrame or Excel table with columns: exact m/z, retention time, intensity)
• Tolerance threshold for KMD window (default ±0.005 Da)

Outputs

• Annotated feature table with KMD column (computed mass defect values)
• Series_id column assigning each feature to a homologous cluster
• Interactive HTML plot: KMD vs. m/z with linked m/z vs. retention time visualization

How to apply

Load the feature list as a pandas DataFrame with exact m/z values. For each feature, calculate Kendrick mass using KM = (exact_mass / 14.01565) × 14, where 14 represents the CH₂ unit mass used as the normalization basis. Compute 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 series). Assign each cluster a series_id label and append KMD and series_id columns to the enriched feature table. The rationale is that true PFAS homologs share identical KMD values while differing by multiples of 14 Da in exact mass, creating a fingerprint detectable by this algebraic transformation.

Related tools

• PFΔScreen (GUI-based orchestrator that implements KMD analysis as part of automated PFAS prioritization workflow) — https://github.com/JonZwe/PFAScreen
• pyOpenMS (Feature detection in raw MS data (upstream preprocessing that generates m/z, RT, intensity features for KMD analysis))
• pandas (DataFrame manipulation for KMD calculation and homolog clustering)

Examples

kmd_values = (feature_df['exact_mass'] / 14.01565) * 14; feature_df['KMD'] = feature_df['exact_mass'] - kmd_values; clusters = feature_df.groupby(pd.cut(feature_df['KMD'], bins=np.arange(feature_df['KMD'].min() - 0.005, feature_df['KMD'].max() + 0.005, 0.01))); feature_df['series_id'] = clusters.ngroup()

Evaluation signals

• KMD values within each series cluster differ by <0.01 Da (all features in a group share nearly identical mass defect).
• Consecutive features in the same series differ by exactly 14 Da (or integer multiples thereof) in exact m/z, verifying CF₂ repeatability.
• Retention time trend: features in the same homologous series show systematic retention time shifts correlating with increasing chain length (detectible in the interactive KMD vs. m/z linked RT plot).
• Series_id uniqueness: each feature is assigned to exactly one series; no orphaned or multi-assigned features.
• Tolerance window consistency: all features within a cluster meet the ±0.005 Da KMD criterion; features outside clusters exceed it.

Limitations

• KMD analysis assumes all homologs in a series differ by integer multiples of 14 Da; structural PFAS variants with different functional groups (e.g., sulfonic acid vs. carboxylic acid) may not cluster together despite related structures.
• Tolerance threshold (±0.005 Da) is empirically tuned for CF₂ series; other homolog types (e.g., CHF₂, CF₃ chains) may require different thresholds not addressed in the article.
• False positives can occur when unrelated features accidentally share similar KMD values; KMD alone does not confirm PFAS identity — must be combined with MS2 fragment analysis and diagnostic fragments for prioritization.
• Requires high mass accuracy (ideally sub-ppm) to resolve small KMD differences; lower-accuracy instruments may produce overlapping clusters.

Evidence

• [other] Calculate 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] Compute KMD as KMD = exact_mass − KM for each feature.: "Compute KMD as KMD = exact_mass − KM for each feature."
• [other] Cluster features into homologous series by grouping those with KMD values within a tolerance window (typically ±0.005 Da for CF₂ series).: "Cluster features into homologous series by grouping those with KMD values within a tolerance window (typically ±0.005 Da for CF₂ series)."
• [intro] PFΔScreen uses several techniques for prioritization such as the MD/C-m/C approach, Kendrick mass defect (KMD) analysis and fragment mass differences and diagnostic fragments in the MS2 data: "PFΔScreen uses several techniques for prioritization such as the MD/C-m/C approach, Kendrick mass defect (KMD) analysis and fragment mass differences and diagnostic fragments in the MS2 data"
• [intro] Raw mass spectrometric data can be included vendor-independently in the mzML format (data-dependent acquisition with centroided spectra: "Raw mass spectrometric data can be included vendor-independently in the mzML format (data-dependent acquisition with centroided spectra"
• [readme] A 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)"