MeltingPointChecker

Melting Point

dissolution EAEU impurities microbiology pharmacopoeia specification

Melting Point Checker — Melting point verification (Ph. Eur. 2.2.14)

ℹ️ The utility verifies melting point as an indicator of purity and identity.
ℹ️ Does not use machine learning — rule-based logic only.
⚠️ Deviation often indicates impurities or misidentification.

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

Input format:
MeasuredMeltingPoint,LowerLimit,UpperLimit,SubstanceName

Example:
148.5,146.0,150.0,Glucose
121.0,120.0,122.0,Acetylsalicylic Acid
135.0,138.0,142.0,Paracetamol

— WHY IS THIS NEEDED?
Melting point is a simple but reliable test for:
• Purity (impurities depress and broaden the range),
• Identity (each compound has a unique range),
• Compliance with Ph. Eur. 2.2.14 is mandatory for most organic substances.
Used in incoming QC and batch release.

input.csv

MeasuredMeltingPoint,LowerLimit,UpperLimit,SubstanceName
148.5,146.0,150.0,Glucose
121.0,120.0,122.0,Acetylsalicylic Acid
135.0,138.0,142.0,Paracetamol

URS & FS — user requirements and functional specification

This document defines user requirements and functional specification for the quality-control utility. It supports deployment discussion, IQ/OQ preparation and QC workflow integration.

Utility: MeltingPointCheckerAPI / object: Melting PointCategory: EAEU pharmacopoeia

1. Scope

The Melting Point Checker utility is used for Melting Point. The actual input.csv is the source of truth for the input structure; control criteria are defined by the executable and the domain description.

The utility does not use machine learning; decisions are produced by deterministic rules.

Categories: EAEU pharmacopoeia

Tags: dissolution, EAEU, impurities, microbiology, pharmacopoeia, specification

2. Execution modes

MeltingPointChecker.exe                            → demo mode (console output)
MeltingPointChecker.exe input.csv output.json      → evaluate your data

3. Key controlled areas

dissolution / disintegration
pH and physicochemical parameters

4. Domain limits and critical parameters

⚠️ Deviation often indicates impurities or misidentification.

Limits stated in the description must be verified against the current approved specification, pharmacopoeial monograph and registration dossier before production use.

5. URS — user requirements

ID	Requirement	Criticality	Acceptance criterion
URS-001	The system shall accept an `input.csv` file for Melting Point with the exact columns listed in the “Input data contract” section.	High	A file with the correct header is processed without manual editing; missing mandatory columns produce FAIL/import error.
URS-002	The system shall support execution without arguments in demo mode and execution with `input.csv output.json` for user data.	Medium	Both execution scenarios produce a predictable result or clear diagnostic error.
URS-003	The system shall perform rule-based controls for: dissolution / disintegration, pH and physicochemical parameters.	High	Each controlled parameter receives a status and message; the result does not depend on hidden Excel formulas or ML.
URS-004	The system shall preserve traceability between batch/lot, source values, applied rules and final verdict.	High	Output includes batch identifier, source values, parameter statuses and critical findings.
URS-005	The system shall generate machine-readable `output.json` for LIMS/ELN/MES, batch record and QA/QC review.	High	JSON contains overall status, check array, warnings, failures and source-file reference.
URS-006	The system shall support use in the client validation package: URS/FS, IQ/OQ preparation, installation and operational scenario checks.	High	The document, test scenarios and reproducible CSV/JSON flow are suitable for audit and internal approval.
URS-007	The system shall clearly separate technical data errors from specification nonconformities.	Medium	Schema/type errors are not mixed with pharmacopoeial deviations and are reported separately.

6. input.csv data contract

The source of truth for the input schema is the actual input.csv header. Column names are technical identifiers and are not translated.

#	Column	Type	Unit	Description	Sample	Control rule
1	`MeasuredMeltingPoint`	decimal	°C	Measured Melting Point	`148.5`	mandatory numeric value; rule comparison is performed by the utility
2	`LowerLimit`	decimal	as specified	Lower Limit	`146.0`	mandatory numeric value; rule comparison is performed by the utility
3	`UpperLimit`	decimal	as specified	Upper Limit	`150.0`	mandatory numeric value; rule comparison is performed by the utility
4	`SubstanceName`	text	as specified	Substance Name	`Glucose`	mandatory value; format and acceptability are checked by the utility

CSV example

MeasuredMeltingPoint,LowerLimit,UpperLimit,SubstanceName
148.5,146.0,150.0,Glucose

7. FS — functional specification

ID	Function	Implementation description
FS-001	CLI entry point	The executable `MeltingPointChecker.exe` supports demo mode and `input.csv output.json` processing mode.
FS-002	CSV parser	The import module reads CSV, validates header, column presence/order, value count and encoding. Decimal values are expected with a dot separator.
FS-003	Field conversion	Each column is converted to the expected type: identifier, text, decimal number, date/time or boolean flag.
FS-004	Domain rule engine	For Melting Point, explicit rules are applied: range, minimum, maximum, absence of prohibited flag, data completeness or calculation-based check.
FS-005	Criticality handling	Critical violations produce FAIL; non-critical deviations and incomplete data produce WARNING; full conformance produces PASS.
FS-006	JSON writer	`output.json` stores overall status, per-parameter results, source values, warnings, failures and diagnostic messages.
FS-007	Integration contract	The CSV → JSON format is stable for invocation from LIMS/ELN/MES, scheduled task or wrapper service.
FS-008	Error handling	Schema error, missing file, non-numeric value or JSON write failure returns diagnosable error without silent PASS.

8. output.json contract

The output file must be suitable for automated processing, audit review and correlation with the source input.csv row.

{
  "utility": "MeltingPointChecker",
  "api": "Melting Point",
  "batchNumber": "",
  "overallStatus": "PASS|WARNING|FAIL",
  "checkedAtUtc": "2026-05-18T00:00:00Z",
  "checks": [
    {
      "parameter": "MeasuredMeltingPoint",
      "value": "148.5",
      "unit": "°C",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "LowerLimit",
      "value": "146.0",
      "unit": "as specified",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "UpperLimit",
      "value": "150.0",
      "unit": "as specified",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "SubstanceName",
      "value": "Glucose",
      "unit": "as specified",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    }
  ],
  "criticalFindings": [],
  "sourceFile": "input.csv"
}

9. OQ/PQ test scenarios

ID	Scenario	Expected result
TC-001	Valid CSV with expected header and sample row	All rows are processed; output contains PASS/WARNING/FAIL and parameter-level detail.
TC-002	A mandatory input.csv column is missing	Import is rejected or the row receives FAIL with schema reference.
TC-003	A numeric field contains text or a blank value	Type conversion error is recorded; the result is not hidden as PASS.
TC-004	A parameter is outside specification or critical limit	Critical parameters produce FAIL; non-critical deviations produce WARNING according to the rule.

10. QA/QC, CSV and change control

Before production use, the client records executable version, checksum, specification/monograph version, test CSV, expected JSON and IQ/OQ results.
Column names must not be changed without updating the validator and test scenarios.
The source CSV, output.json and utility version should be stored together as an evidence package.
Any change in control rules must go through change control and repeated OQ scenario verification.

Included in packages

Diabetes QC Suite

QC package for diabetes medicines: insulins, metformin, SGLT2/DPP-4/GLP-1 products, glucagon and supporting dosage-form quality checks.

Open

EAEU Pharmacopoeia QC Suite

Package for EAEU pharmacopoeial methods: specifications, dissolution, microbiology, impurities and general tests.

Open