Related Experiment Videos
Development of the Regular Interlaboratory Counting Exchanges (RICE) scheme to include visual reference counts and
P W Brown1, N P Crawford, A D Jones
1Institute of Occupational Medicine, Edinburgh, U.K.
The Annals of Occupational Hygiene
|October 1, 1994
Summary
The RICE proficiency testing scheme now uses visual counts from laboratories to create reliable reference counts for industrial asbestos samples. This enhances accuracy for both industrial and clearance asbestos testing.
Area of Science:
- Occupational Health and Safety
- Environmental Science
- Analytical Chemistry
Background:
- The RICE proficiency testing scheme previously relied on image analysis for asbestos fibre counting.
- Participating laboratories used visual counting methods alongside the established scheme.
- Asbestos clearance operations presented unique counting challenges.
Purpose of the Study:
- To develop reliable visual reference counts for industrial asbestos samples using historical RICE data.
- To establish suitable reference counts and performance limits for asbestos clearance samples.
- To integrate visual reference counts and clearance samples into the RICE scheme.
Main Methods:
- Utilized visual counts from participating laboratories within the RICE scheme.
- Developed reference counts for industrial asbestos samples.
- Developed reference counts and performance limits for asbestos clearance samples.
- Analyzed data variation to define performance intervals on log and square-root scales.
Main Results:
- Established reliable visual reference counts for industrial asbestos samples.
- Created validated reference counts and performance limits for asbestos clearance samples.
- Successfully integrated visual reference counts and clearance samples into the RICE scheme in 1992.
Conclusions:
- Visual counting data from laboratories can provide reliable reference counts for asbestos testing.
- The updated RICE scheme offers improved accuracy for both industrial and clearance asbestos sample analysis.
- Performance limits are adapted to sample density variations for more robust testing.