GCMSScreeningVolatileImpuritiesQualityChecker
GC-MS Screening Volatile Impurities
GC-MS Screening Volatile Impurities Quality Checker — Screening of Volatile Impurities (GC-MS) ℹ️ Utility checks critical parameters of volatile organic compound content: • Total volatile impurities: ≤500 ppm (example screening limit) • Benzene (Class 1): ≤2.0 ppm (strict ICH Q3C limit) • Dichloromethane (Class 2): ≤600 ppm (ICH Q3C limit) • Signal-to-Noise Ratio (S/N): ≥10 (for reliable quantification) ⚠️ CRITICAL: Exceeding benzene content is unacceptable due to its carcinogenicity! Residual solvents can affect product safety and stability. Usage: GCMSScreeningVolatileImpuritiesQualityChecker.exe → demo mode (console output) GCMSScreeningVolatileImpuritiesQualityChecker.exe input.csv output.json → evaluate your data Input format: BatchNumber,TotalVolatileImpuritiesPPM,BenzenePPM,DCM_PPM,SignalToNoiseRatio,RetentionTimeMin Example: GCMS-API-2026-001,120.0,0.5,150.0,45.0,5.2 — WHY IS THIS NEEDED? Control of residual solvents is a mandatory requirement of ICH Q3C and pharmacopoeias (USP <467>, Ph. Eur. 2.4.24). • Gas Chromatography with Mass Spectrometry (GC-MS) allows identification and quantification of a wide range of volatile impurities. • The method has high sensitivity and selectivity, allowing detection of impurities at ppm levels. • Screening is necessary to confirm the safety of API synthesis and raw material purification. • Automation of limit checks accelerates batch release and reduces human error risk. ⚠️ CRITICAL: • Benzene belongs to Class 1 (known carcinogens) and must be strictly limited or excluded. • Class 2 solvents (e.g., DCM, acetonitrile) must be limited to safe levels. • Signal-to-Noise Ratio (S/N) must confirm that measurement is performed above the Limit of Quantitation (LOQ). • Peak identification by retention time and mass spectrum must be confirmed by standards. Key features: • Verification of compliance with ICH Q3C limits for critical solvents. • Assessment of total volatile contaminant level. • Analytical signal quality control (S/N). Critical parameters: • Total Volatiles: <= 500 ppm (Example) • Benzene: <= 2.0 ppm • DCM: <= 600 ppm • Signal-to-Noise: >= 10 💡 Usage tips: 1. Use Headspace analysis to minimize sample matrix influence. 2. Regularly calibrate mass spectrometer using perfluorotributylamine (PFTBA) or similar standard. 3. Apply internal standards (e.g., 1,4-difluorobenzene) to correct for losses during sample preparation. 4. Check chromatographic system leak tightness to exclude air ingress. 5. Update mass spectral libraries for accurate identification of unknown peaks. ⚠️ Note: GC-MS allows not only quantitative determination of known impurities but also screening of unknown compounds via library spectra, making it a powerful tool for deviation investigation.
input.csv
BatchNumber,TotalImp_ppm,Benzene_ppm,DCM_ppm,SN_Ratio,RT_Min GCMS-API-2026-001,120.0,0.5,150.0,45.0,5.2 GCMS-API-2026-002,85.0,0.1,50.0,60.0,5.2 GCMS-FAIL-BENZ-003,200.0,3.5,100.0,50.0,5.2
URS & FS — GC-MS Screening Volatile Impurities
This document describes the user requirements and functional specification for GCMSScreeningVolatileImpuritiesQualityChecker. The utility is intended for QC laboratory use as a rule-based check of data prepared from an instrument, LIMS, ELN, MES or an approved input.csv.
URS — User Requirements Specification
| ID | Requirement | Criticality | Acceptance criterion |
|---|---|---|---|
| URS-001 | The utility shall accept input.csv with approved columns: BatchNumber, TotalImp_ppm, Benzene_ppm, DCM_ppm, SN_Ratio, RT_Min. | High | The CSV file is processed without manual header editing. |
| URS-002 | The utility shall perform deterministic evaluation for “GC-MS Screening Volatile Impurities” without machine learning. | High | The same input data produce the same JSON result. |
| URS-003 | The utility shall validate mandatory fields, numeric formats, flags, ranges and domain plausibility. | High | Schema and conversion errors are explicitly reported. |
| URS-004 | The utility shall generate output.json with PASS / WARNING / FAIL statuses, source values, warnings and failures. | High | The JSON result is suitable for review, deviation investigation and integration. |
| URS-005 | The documentation shall support IQ/OQ/PQ or CSV/CSA verification. | Medium | URS/FS, CSV/JSON contract and test scenarios are supplied with the utility. |
input.csv contract
| # | Field | Sample | Purpose |
|---|---|---|---|
| 1 | BatchNumber | GCMS-API-2026-001 | Input parameter for deterministic QC evaluation. |
| 2 | TotalImp_ppm | 120.0 | Input parameter for deterministic QC evaluation. |
| 3 | Benzene_ppm | 0.5 | Input parameter for deterministic QC evaluation. |
| 4 | DCM_ppm | 150.0 | Input parameter for deterministic QC evaluation. |
| 5 | SN_Ratio | 45.0 | Input parameter for deterministic QC evaluation. |
| 6 | RT_Min | 5.2 | Input parameter for deterministic QC evaluation. |
FS — Functional Specification
| ID | Function | Implementation |
|---|---|---|
| FS-001 | CSV import | Read input.csv; validate header, column count and encoding. |
| FS-002 | Schema validation | Check required fields and permitted input values. |
| FS-003 | Rule engine | Apply domain rules, limits and system suitability/specification checks described in the source utility description. |
| FS-004 | Status aggregation | Produce final status: FAIL for critical failure, WARNING for non-critical deviation, PASS for conformance. |
| FS-005 | JSON export | Write machine-readable output.json for LIMS/ELN/MES and QA/QC review. |
OQ/PQ scenarios
- OQ-001: a valid sample row shall be processed without schema error.
- OQ-002: a missing mandatory column shall produce a schema error.
- OQ-003: a non-numeric value in a numeric field shall produce a conversion error.
- OQ-004: a critical parameter outside the limit shall produce
FAILor a critical finding. - PQ-001: user real batches shall be checked with retention of
input.csv,output.json, utility version and checksum.
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