MicrobialIdentificationSystemQualityChecker

Microbial Identification System

LabEx laboratory QC CSV→JSON URS & FS microbiology utilities / cleanroom
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Microbial Identification System Quality Checker — Control of Microbial Identification Quality

ℹ️  Utility checks reliability of microbial identification results (MALDI-TOF MS / Biochemical Systems):
   • Log Score (Confidence Index): ≥2.0 for species, ≥1.7 for genus
   • Discrimination Index (Difference from 2nd result): ≥0.2
   • Spectrum/Test Quality Status: Valid

⚠️  CRITICAL: Log Score < 1.7 means the microorganism is NOT IDENTIFIED!
   Confirmation by sequencing (16S rRNA) or other methods is required.

Usage:
 MicrobialIdentificationSystemQualityChecker.exe                            → demo mode (console output)
 MicrobialIdentificationSystemQualityChecker.exe input.csv output.json      → evaluate your data

Input format:
BatchNumber,IsolateID,IdentifiedSpecies,LogScore,SecondBestLogScore,SpectrumQCStatus

Example:
 MICRO-ID-2026-001,ISO-001,Staphylococcus_aureus,2.45,1.80,Valid

— WHY IS THIS NEEDED?
Rapid and accurate identification of contaminants is critical for investigating deviations in sterile manufacturing.
• Systems like MALDI-TOF MS (Bruker, Shimadzu) or Vitek 2 are standards in modern microbiology labs.
• Log Score reflects the degree of similarity of the mass spectrum or biochemical profile with the database.
• High score (>2.0) ensures species-level identification with high probability.
• Automation eliminates risk of erroneous interpretation of borderline results by operators.

⚠️  CRITICAL:
• Database must be regularly updated and validated.
• Quality of colony isolation (purity of culture) directly affects spectrum quality.
• Low discrimination index (small difference between 1st and 2nd result) indicates system inability to reliably distinguish close species.
• Results with low scores must be sent for confirmatory testing.

Key features:
• Assessment of identification reliability by Log Score.
• Verification of result specificity (Discrimination Index).
• Control of technical spectrum/test quality.

Critical parameters:
• Log Score: >= 2.0 (Species), >= 1.7 (Genus)
• Discrimination Delta: >= 0.2
• Spectrum QC: Valid

💡 Usage tips:
1. Use standard protein extraction protocols for MALDI-TOF.
2. Regularly calibrate instrument using reference strains.
3. For rare microorganisms, check for entries in the database.
4. Save raw spectra for retrospective analysis upon database updates.
5. Integrate results with Contamination Trending System.

⚠️ Note: Unlike phenotypic methods, MALDI-TOF is based on protein profile analysis. This makes it fast but sensitive to cultivation conditions (colony age, medium).

input.csv

BatchNumber,IsolateID,Species,LogScore,SecondBestScore,SpectrumQC
MICRO-ID-2026-001,ISO-001,Staphylococcus_aureus,2.45,1.80,Valid
MICRO-ID-2026-002,ISO-002,Escherichia_coli,1.50,1.45,Valid
MICRO-ID-2026-003,ISO-003,Pseudomonas_aeruginosa,2.30,2.25,Valid
MICRO-ID-FAIL-004,ISO-004,Unknown,0.50,0.40,Invalid

URS & FS — Microbial Identification System

This document describes the user requirements and functional specification for MicrobialIdentificationSystemQualityChecker. 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, IsolateID, Species, LogScore, SecondBestScore, SpectrumQC.HighThe CSV file is processed without manual header editing.
URS-002The utility shall perform deterministic evaluation for “Microbial Identification System” 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
1BatchNumberMICRO-ID-2026-001Input parameter for deterministic QC evaluation.
2IsolateIDISO-001Input parameter for deterministic QC evaluation.
3SpeciesStaphylococcus_aureusInput parameter for deterministic QC evaluation.
4LogScore2.45Input parameter for deterministic QC evaluation.
5SecondBestScore1.80Input parameter for deterministic QC evaluation.
6SpectrumQCValidInput 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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