SamplePrepHomogenizationQualityChecker
Sample Prep Homogenization
Sample Prep Homogenization Quality Checker — Homogenization Control for Sample Preparation
ℹ️ Utility assesses the homogeneity of pharmaceutical powders and blends before analysis or compression:
• RSD (Relative Standard Deviation) ≤ 2.0% — key indicator of homogeneity
• Max Deviation from Mean ≤ 5.0% — absence of "hot spots"
• Particle Size D90 ≤ 100 µm — prevention of segregation
• Statistical significance (≥10 subsamples) according to USP <905>
• Moisture control (≤1.0%) — prevention of agglomeration
⚠️ CRITICAL: High RSD (>2.0%) indicates component segregation!
Insufficient homogenization leads to dosage uniformity issues in finished tablets/capsules.
Usage:
SamplePrepHomogenizationQualityChecker.exe → demo mode (console output)
SamplePrepHomogenizationQualityChecker.exe input.csv output.json → evaluate your data
Input format:
BatchNumber,ProductName,MeanContentPercent,RSD_Percent,MaxDeviationPercent,ParticleSizeD90_um,NumberOfSubsamples,MoistureContentPercent
Example:
HOMO-2026-001,Paracetamol Powder Blend,100.2,1.2,3.5,85.0,10,0.4
— WHY IS THIS NEEDED?
Homogenization is a critical step in sample preparation and manufacturing of solid dosage forms.
A non-uniform mixture leads to some tablets containing too little or too much active ingredient,
which is a GMP violation and poses a risk to the patient.
⚠️ CRITICAL:
• RSD ≤ 2.0% — gold standard for well-mixed powders. Values >5% are unacceptable for most APIs.
• Number of subsamples ≥ 10 — necessary for statistically significant homogeneity assessment (USP <905>).
• Particle size — large particles are prone to segregation (layering) during transport and vibration.
• Moisture — excess moisture causes particle sticking (agglomeration), disrupting homogeneity.
Key features:
• Homogeneity assessment via statistical parameters (RSD, Max Deviation)
• Control of physical parameters (particle size, moisture)
• Verification of sampling sufficiency (number of subsamples)
• Generation of reports for LIMS LabWare
• Compliance with USP <905> Uniformity of Dosage Units principles
Critical parameters:
• Mean Content: 98.0–102.0%
• RSD: ≤ 2.0%
• Max Deviation: ≤ 5.0%
• Particle Size D90: ≤ 100 µm
• Number of Subsamples: ≥ 10
• Moisture: ≤ 1.0%
💡 Usage tips:
1. Sampling: Use a sample thief to take samples from different parts of the mixer (top, middle, bottom).
2. Statistics: Use at least 10 independent subsamples to calculate RSD.
3. Particle Size: If RSD is high, check particle size distribution. Additional milling may be required.
4. Moisture: Ensure raw materials have appropriate moisture content before mixing. Use desiccants if necessary.
5. Validation: The mixing process must be validated with proof of achieving homogeneity (RSD < 2-3%).
⚠️ Note: Homogenization is important not only for finished products but also for analytical sample preparation. A non-homogeneous analytical sample will yield false results for assay and impurities.input.csv
BatchNumber,ProductName,MeanContentPercent,RSD_Percent,MaxDeviationPercent,ParticleSizeD90_um,NumberOfSubsamples,MoistureContentPercent HOMO-2026-001,Paracetamol Powder Blend,100.2,1.2,3.5,85.0,10,0.4 HOMO-2026-002,Ibuprofen Granules,99.5,2.5,6.0,120.0,8,1.2
URS & FS — Sample Prep Homogenization
This document describes the user requirements and functional specification for SamplePrepHomogenizationQualityChecker. 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, ProductName, MeanContentPercent, RSD_Percent, MaxDeviationPercent, ParticleSizeD90_um, NumberOfSubsamples, MoistureContentPercent. | High | The CSV file is processed without manual header editing. |
| URS-002 | The utility shall perform deterministic evaluation for “Sample Prep Homogenization” 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 | HOMO-2026-001 | Input parameter for deterministic QC evaluation. |
| 2 | ProductName | Paracetamol Powder Blend | Input parameter for deterministic QC evaluation. |
| 3 | MeanContentPercent | 100.2 | Input parameter for deterministic QC evaluation. |
| 4 | RSD_Percent | 1.2 | Input parameter for deterministic QC evaluation. |
| 5 | MaxDeviationPercent | 3.5 | Input parameter for deterministic QC evaluation. |
| 6 | ParticleSizeD90_um | 85.0 | Input parameter for deterministic QC evaluation. |
| 7 | NumberOfSubsamples | 10 | Input parameter for deterministic QC evaluation. |
| 8 | MoistureContentPercent | 0.4 | 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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