Tablet_Coating_Uniformity_NIR
Tablet Coating Uniformity NIR
Lumex QC URS & FS input.csv output.json rule-based LIMS-ready NIR
Open selectionUtility description: Tablet Coating Uniformity NIR
Tablet Coating Uniformity NIR — Assessment of Tablet Coating Uniformity (NIR) ℹ️ Utility checks critical parameters of film coating quality: • Mean coating thickness: within ±10% of target • Relative Standard Deviation (RSD): ≤5.0% • Coating Quality Index (CQI): ≥0.95 ⚠️ CRITICAL: High RSD (>5%) indicates uneven spraying or tablet movement! Low CQI may indicate surface defects (orange peel, bridging). Usage: Tablet_Coating_Uniformity_NIR.exe → demo mode (console output) Tablet_Coating_Uniformity_NIR.exe input.csv output.json → evaluate your data Input format: BatchNumber,MeanCoatingThicknessUm,StdDevCoatingThicknessUm,RSD_CoatingPercent,CoatingQualityIndex Example: COAT-NIR-2026-001,50.0,1.5,3.0,0.98 — WHY IS THIS NEEDED? Control of the pan-coating/film-coating process is critical for ensuring protective properties, taste masking, or modified release. • Traditional methods (weight gain, micrometry) are laborious and do not provide information on local non-uniformity. • Near-Infrared Spectroscopy (NIR) allows rapid, non-contact assessment of polymer film thickness. • The method is based on spectral differences between the tablet core and the polymer coating. • InfraLUM FT-12 instruments allow integration of probes directly into the coating drum (on-line/at-line). • Coating uniformity directly affects product stability and API release profile. ⚠️ CRITICAL: • Calibration model (PLS/PCR) must be built for the specific core/polymer combination. • Surface roughness and tablet orientation can affect the spectrum; averaged sampling is required. • RSD ≤5% is a generally accepted criterion for a good coating process for most standard tablets. • Monitoring humidity and temperature in the drum is important as they affect NIR spectra. Key features: • Rapid assessment of coating homogeneity. • Calculation of integral Coating Quality Index (CQI). • Support for Lumex NIR spectrometer data. Critical parameters: • Mean Thickness: Target ±10% • RSD: <= 5.0% • Coating Quality Index: >= 0.95 💡 Usage tips: 1. Perform measurements on a representative sample of tablets (min 20-30 pcs.) from different parts of the drum. 2. Use rotating holders or spectral averaging to minimize orientation influence. 3. Regularly update the calibration model when changing polymer supplier or core formulation. 4. Compare NIR data with reference methods (e.g., laser confocal microscopy) during validation. 5. Monitor coating thickness trends over time to optimize spray rate. ⚠️ Note: NIR is sensitive not only to thickness but also to polymer packing density. Changes in drying process may be interpreted as thickness changes, so stability of technological parameters is essential.
input.csv
BatchNumber,MeanThickness_um,StdDev_um,RSD_percent,CQI COAT-NIR-2026-001,50.0,1.5,3.0,0.98 COAT-NIR-2026-002,48.0,2.0,4.2,0.96 COAT-FAIL-2026-003,42.0,5.5,13.0,0.85
Utility description
Tablet Coating Uniformity NIR — Assessment of Tablet Coating Uniformity (NIR) ℹ️ Utility checks critical parameters of film coating quality: • Mean coating thickness: within ±10% of target • Relative Standard Deviation (RSD): ≤5.0% • Coating Quality Index (CQI): ≥0.95 ⚠️ CRITICAL: High RSD (>5%) indicates uneven spraying or tablet movement! Low CQI may indicate surface defects (orange peel, bridging). Usage: Tablet_Coating_Uniformity_NIR.exe → demo mode (console output) Tablet_Coating_Uniformity_NIR.exe input.csv output.json → evaluate your data Input format: BatchNumber,MeanCoatingThicknessUm,StdDevCoatingThicknessUm,RSD_CoatingPercent,CoatingQualityIndex Example: COAT-NIR-2026-001,50.0,1.5,3.0,0.98 — WHY IS THIS NEEDED? Control of the pan-coating/film-coating process is critical for ensuring protective properties, taste masking, or modified release. • Traditional methods (weight gain, micrometry) are laborious and do not provide information on local non-uniformity. • Near-Infrared Spectroscopy (NIR) allows rapid, non-contact assessment of polymer film thickness. • The method is based on spectral differences between the tablet core and the polymer coating. • InfraLUM FT-12 instruments allow integration of probes directly into the coating drum (on-line/at-line). • Coating uniformity directly affects product stability and API release profile. ⚠️ CRITICAL: • Calibration model (PLS/PCR) must be built for the specific core/polymer combination. • Surface roughness and tablet orientation can affect the spectrum; averaged sampling is required. • RSD ≤5% is a generally accepted criterion for a good coating process for most standard tablets. • Monitoring humidity and temperature in the drum is important as they affect NIR spectra. Key features: • Rapid assessment of coating homogeneity. • Calculation of integral Coating Quality Index (CQI). • Support for Lumex NIR spectrometer data. Critical parameters: • Mean Thickness: Target ±10% • RSD: <= 5.0% • Coating Quality Index: >= 0.95 💡 Usage tips: 1. Perform measurements on a representative sample of tablets (min 20-30 pcs.) from different parts of the drum. 2. Use rotating holders or spectral averaging to minimize orientation influence. 3. Regularly update the calibration model when changing polymer supplier or core formulation. 4. Compare NIR data with reference methods (e.g., laser confocal microscopy) during validation. 5. Monitor coating thickness trends over time to optimize spray rate. ⚠️ Note: NIR is sensitive not only to thickness but also to polymer packing density. Changes in drying process may be interpreted as thickness changes, so stability of technological parameters is essential.
URS & FS — User Requirements and Functional Specification
This document describes the controlled interface, user requirements and functional behaviour of Tablet_Coating_Uniformity_NIR. The utility is intended for automated verification of laboratory, pharmacopoeial, analytical or manufacturing QC parameters using input.csv and producing a structured output.json result.
Domain limits and critical parameters
Before production use, all limits must be verified against the approved specification, registration dossier, pharmacopoeial monograph, validated method and local SOPs.
- • Mean coating thickness: within ±10% of target
- • Relative Standard Deviation (RSD): ≤5.0%
- • Coating Quality Index (CQI): ≥0.95
- • Traditional methods (weight gain, micrometry) are laborious and do not provide information on local non-uniformity.
- • Near-Infrared Spectroscopy (NIR) allows rapid, non-contact assessment of polymer film thickness.
- • The method is based on spectral differences between the tablet core and the polymer coating.
- • InfraLUM FT-12 instruments allow integration of probes directly into the coating drum (on-line/at-line).
- • Coating uniformity directly affects product stability and API release profile.
- • Calibration model (PLS/PCR) must be built for the specific core/polymer combination.
- • Surface roughness and tablet orientation can affect the spectrum; averaged sampling is required.
- • RSD ≤5% is a generally accepted criterion for a good coating process for most standard tablets.
- • Monitoring humidity and temperature in the drum is important as they affect NIR spectra.
- • Rapid assessment of coating homogeneity.
- • Calculation of integral Coating Quality Index (CQI).
- • Support for Lumex NIR spectrometer data.
- • Mean Thickness: Target ±10%
URS — User Requirements Specification
| ID | Requirement | Criticality | Acceptance criterion |
|---|---|---|---|
| URS-001 | The utility shall accept an input.csv file with exact headers defined in the data contract. | High | The file is processed without manual header editing. |
| URS-002 | The utility shall perform deterministic evaluation for Tablet Coating Uniformity NIR using input values, approved limits and domain rules. | High | Each row receives a PASS / WARNING / FAIL status. |
| URS-003 | The utility shall validate mandatory fields, data types, numeric ranges, units and domain plausibility. | High | Schema, format and conversion errors are explicitly reported. |
| URS-004 | The utility shall identify critical deviations for parameters stated in the method description and specification. | High | A critical deviation causes FAIL or a dedicated critical finding. |
| URS-005 | The utility shall generate output.json with machine-readable results, source values, warnings and failures. | High | JSON is suitable for LIMS/ELN/MES integration, QA/QC review and archival. |
| URS-006 | The result shall not depend on machine learning or undocumented heuristics. | Medium | All decisions are based on explicit rules, thresholds and input values. |
| URS-007 | The system shall preserve traceability between batch/sample, input data, applied rules and final status. | High | The output contains the batch/sample identifier and checked parameters. |
| URS-008 | The documentation shall support IQ/OQ/PQ preparation and inspection discussion. | Medium | URS, FS, CSV/JSON contract and test scenarios are supplied with the utility. |
| URS-009 | The utility shall support batch processing of multiple input.csv rows. | Medium | Each row is evaluated independently; errors in one row do not mask errors in others. |
| URS-010 | The utility shall support a simple operating model: demo mode and execution with input/output files. | Medium | The CLI scenario is reproducible in test and production environments. |
input.csv contract
| # | Field | Type | Sample | Purpose |
|---|---|---|---|---|
| 1 | BatchNumber | string | COAT-NIR-2026-001 | Batch or lot identifier used for traceability, review and deviation investigation. |
| 2 | MeanThickness_um | string / decimal | 50.0 | Controlled input parameter used by deterministic QC rules and traceable result generation. |
| 3 | StdDev_um | string / decimal | 1.5 | Controlled input parameter used by deterministic QC rules and traceable result generation. |
| 4 | RSD_percent | decimal | 3.0 | Relative standard deviation; precision metric for the process or analytical method. |
| 5 | CQI | string / decimal | 0.98 | Controlled input parameter used by deterministic QC rules and traceable result generation. |
BatchNumber,MeanThickness_um,StdDev_um,RSD_percent,CQI COAT-NIR-2026-001,50.0,1.5,3.0,0.98 COAT-NIR-2026-002,48.0,2.0,4.2,0.96 COAT-FAIL-2026-003,42.0,5.5,13.0,0.85
FS — Functional Specification
| ID | Function | Implementation |
|---|---|---|
| FS-001 | CSV import | Read input.csv in UTF-8/CSV-compatible format and validate the header and expected columns. |
| FS-002 | Schema validation | Check mandatory fields, column count, critical missing values and row structure. |
| FS-003 | Type conversion | Convert numeric, flag and text values; invalid formats are recorded as row-level errors. |
| FS-004 | Domain rule engine | Apply domain rules for Tablet Coating Uniformity NIR, including limits from the utility description and approved specification. |
| FS-005 | Status aggregation | Produce final status: FAIL for critical failure, WARNING for non-critical deviation, PASS for conformance. |
| FS-006 | JSON export | Write output.json with detailed checks, source values, warnings, failures and critical findings. |
| FS-007 | Audit support | Keep the result structure suitable for review, deviation investigation, calculation reproduction and IQ/OQ/PQ preparation. |
| FS-008 | Integration contract | Support the production scenario: LIMS/ELN/MES creates input.csv, the utility returns output.json, and the portal displays description and documentation. |
| FS-009 | Error handling | Report errors unambiguously and do not substitute missing values with calculated values unless the rule is explicitly defined. |
| FS-010 | Version control support | Document the utility version, input contract, executable checksum and rule application date. |
Example output.json
{
"utilityId": "tablet-coating-uniformity-nir",
"utilityName": "Tablet_Coating_Uniformity_NIR",
"overallStatus": "PASS|WARNING|FAIL",
"sourceFile": "input.csv",
"checks": [
{
"parameter": "BatchNumber",
"value": "COAT-NIR-2026-001",
"status": "PASS|WARNING|FAIL",
"message": "Rule-based check result"
},
{
"parameter": "MeanThickness_um",
"value": "50.0",
"status": "PASS|WARNING|FAIL",
"message": "Rule-based check result"
},
{
"parameter": "StdDev_um",
"value": "1.5",
"status": "PASS|WARNING|FAIL",
"message": "Rule-based check result"
},
{
"parameter": "RSD_percent",
"value": "3.0",
"status": "PASS|WARNING|FAIL",
"message": "Rule-based check result"
},
{
"parameter": "CQI",
"value": "0.98",
"status": "PASS|WARNING|FAIL",
"message": "Rule-based check result"
}
],
"criticalFindings": [],
"warnings": [],
"generatedFor": "QA/QC review and LIMS integration"
}
Traceability matrix
| URS | FS | OQ/PQ coverage |
|---|---|---|
| URS-001, URS-003 | FS-001, FS-002, FS-003 | OQ-001/OQ-002/OQ-003 |
| URS-002, URS-004 | FS-004, FS-005 | OQ-004/PQ-001 |
| URS-005, URS-007 | FS-006, FS-007 | OQ-005/PQ-002 |
| URS-008, URS-010 | FS-008, FS-010 | IQ-001/OQ-006 |
OQ/PQ test scenarios
| ID | Scenario | Expected result |
|---|---|---|
| OQ-001 | Valid sample row | PASS or acceptable WARNING according to the rules. |
| OQ-002 | Mandatory column missing | Schema error or FAIL. |
| OQ-003 | Non-numeric value in numeric field | Type-conversion error. |
| OQ-004 | Critical parameter outside limit | FAIL and critical finding. |
| OQ-005 | Multiple rows with different statuses | Independent row-level evaluation. |
| PQ-001 | User real batch/sample | Reviewed result with retained input/output files. |
QA/QC and change control
- Do not rename columns without updating the validator, documentation and test set.
- Retain
input.csv,output.json, executable version, documentation and checksum. - Before production use, perform IQ/OQ/PQ or equivalent CSV/CSA verification.
- Critical limits must be verified against the approved specification, local SOPs and registration dossier.
Included in packages
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A single Lumex package combining the former Lumex and Lumex2 sets: instrumental and general pharmaceutical QC, AAS/ICP, CE, HPLC, NIR/PAT, stability, dissolution, content uniformity, system suitability and statistical control.
OpenRadiology QC Suite
QC package for radiology and diagnostic imaging: radiopharmaceuticals, PET/SPECT, contrast media, iodinated and gadolinium products, plus particle, sterility and endotoxin checks.
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