Reliable Quality Control using Images

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Quality Control (QC) using Imaging

Produce Consistent and Quantitative Quality Control Data in a Production Environment: plus-Image QC Analysis On-line or At-line

Why Imaging plus Image Analysis?

Process of "Use of Image Analysis" is a way of producing consistent quality control data from quantifiable visual analysis to better inform the decision-making process. Intertek personnel provide assessing imaging + image analysis approaches in the lab, designing a workable solution and then implementing it either at-line or on-line. Applications include the automatic analysis of samples formulated, mixed and delivered robotically to an imaging station in high throughput experimentation (HTE) systems.

Why consider Imaging plus Image Analysis? Operators and QC Lab personnel can be in a position of using their eyes plus their judgement to make process decisions on a daily basis. But experience and tests show that the judgements made vary quite markedly between different people. While visual assessment is and always will be a powerful tool, use of plus-Image QC analysis will help reduce QC assessment variability and improve QC data for a more consistent QC measurement.

Applications for clients using Imaging plus Image Analysis include:

Imaging QC Application	Image Analysed	Quality Control Outcome
Black speck measurement on a white filter in a QC lab:Black speck measurement on a white filter in a QC lab:		A combined system for measuring micro (<80 microns) and macro specks was implemented at-line. Both number and total area of the specks was analysed and used to assess product quality.
Mean granule diameter measurement whilst they were in-flight in a batch reactor:		Following work on the reactor a lighting/ imaging/ image analysis system was specified and fitted onto the plant. A running diameter value was valuable in determining reaction end point.
Measurement of foam height and bubble size distribution in HTE system:		Foam height and bubble size distribution were measured automatically for robotically-produced and delivered foams. Data helped determine surfactant formulation quality.