Particulate matter library for visual inspection in parenteral fill-finish

What the module does

• Library entries by class: extrinsic, intrinsic and inherent particulates, and container, cosmetic or fill defects, with criticality, visibility, morphology, color and size range; inspection artefacts such as bubbles can be recorded so inspectors learn what is not a defect.
• Evidence on each entry: high-resolution PNG or JPEG images up to 20 MB each, the FTIR spectrum, Raman or SEM-EDX reports, and the material identity with method, instrument, characteristic bands, their assignments and the library match.
• AI characterization from the images and the analytical data: a proposed class and identity with the rationale, the introduction point and likely source, the inspection cue, the controls and the questions; the analyst records what is confirmed.
• Introduction points mapped relative to the 0.2 µm sterilizing filter: upstream bulk and compounding, the filter assembly, filling equipment, primary packaging components, environment and personnel, stoppering, lyophilization and capping, product-inherent formation after filling, and downstream handling.
• The inspection cue on each defect: what the inspector looks for, how reliably manual inspection and AVI detect it, and the reject category.
• QA approval with an electronic signature, after which the entry is locked; a re-characterization is a new version that supersedes the old one, and a retired defect leaves with its reason.
• Defect sets per product, container, line and site, with the procedure, the effective day and the basis, holding approved defects with their units and their role in training, testing or both; each set needs at least three defects and at least one critical defect.
• An AI coverage review of each set against the approved library and the linked deviations: missing critical and post-filtration defects, classes and introduction points without an example, quantities too small to measure detection, superseded or retired members.
• Inspector training, qualification and requalification, AVI challenge tests and AVI recipe verification against an approved set, with detections per defect and false rejects judged against the procedure's criteria (by default 100% critical, 90% major and 70% minor detection, at most 5% false rejects).

How it fits the platform

A defect first seen in a deviation or a complaint is characterized from that record, and the library entry links back. Knowing which side of the filter a particle entered on directs the investigation and the CAPA: a particle the filter would retain, found in a filled unit, entered after the filter, from the filling equipment, the components, the environment or the closure operations. Deviations, investigations, CAPAs and change controls, such as a new component supplier, link to the defects they produced, and Q Intelligence answers questions such as which defects a kit lacks.

Approved sessions are the qualification record of each inspector and each AVI recipe against the defect set in force at the time. When the library changes, the set is revised as a new version, and the next requalification runs against the updated process.

Where people decide

The AI proposes the characterization and points at the gaps in a set; the analyst records the identity, the introduction point and the source, each value with a reason once set. QA approves every library entry, every defect set and every session outcome with an electronic signature, and the session records the actions for anyone who failed: retraining, restriction from inspection or an AVI recipe change.