GCMSScreeningVolatileImpuritiesQualityChecker

GC-MS Screening Volatile Impurities

LabEx laboratory QC CSV→JSON URS & FS chromatography utilities / cleanroom
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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

IDRequirementCriticalityAcceptance criterion
URS-001The utility shall accept input.csv with approved columns: BatchNumber, TotalImp_ppm, Benzene_ppm, DCM_ppm, SN_Ratio, RT_Min.HighThe CSV file is processed without manual header editing.
URS-002The utility shall perform deterministic evaluation for “GC-MS Screening Volatile Impurities” without machine learning.HighThe same input data produce the same JSON result.
URS-003The utility shall validate mandatory fields, numeric formats, flags, ranges and domain plausibility.HighSchema and conversion errors are explicitly reported.
URS-004The utility shall generate output.json with PASS / WARNING / FAIL statuses, source values, warnings and failures.HighThe JSON result is suitable for review, deviation investigation and integration.
URS-005The documentation shall support IQ/OQ/PQ or CSV/CSA verification.MediumURS/FS, CSV/JSON contract and test scenarios are supplied with the utility.

input.csv contract

#FieldSamplePurpose
1BatchNumberGCMS-API-2026-001Input parameter for deterministic QC evaluation.
2TotalImp_ppm120.0Input parameter for deterministic QC evaluation.
3Benzene_ppm0.5Input parameter for deterministic QC evaluation.
4DCM_ppm150.0Input parameter for deterministic QC evaluation.
5SN_Ratio45.0Input parameter for deterministic QC evaluation.
6RT_Min5.2Input parameter for deterministic QC evaluation.

FS — Functional Specification

IDFunctionImplementation
FS-001CSV importRead input.csv; validate header, column count and encoding.
FS-002Schema validationCheck required fields and permitted input values.
FS-003Rule engineApply domain rules, limits and system suitability/specification checks described in the source utility description.
FS-004Status aggregationProduce final status: FAIL for critical failure, WARNING for non-critical deviation, PASS for conformance.
FS-005JSON exportWrite 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 FAIL or 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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