ASTM D6299-23a

Standard Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measurement System Performance

ASTM International , 12/01/2023

Publisher: ASTM

File Format: PDF

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Description

Standard By:ASTM International , 12/01/2023

Published:12/01/2023

Pages:36

File Size:1 file , 1.6 MB

Language:English

Note:This product is unavailable in Russia, Ukraine, Belarus

1.1This practice covers information for the design and operation of a program to monitor and control ongoing stability and precision and bias performance of selected analytical measurement systems using a collection of generally accepted statistical quality control (SQC) procedures and tools.

Note 1:A complete list of criteria for selecting measurement systems to which this practice should be applied and for determining the frequency at which it should be applied is beyond the scope of this practice. However, some factors to be considered include (1) frequency of use of the analytical measurement system, (2) criticality of the parameter being measured, (3) system stability and precision performance based on historical data, (4) business economics, and (5) regulatory, contractual, or test method requirements.

1.2This practice is applicable to stable analytical measurement systems that produce results on a continuous numerical scale.

1.3This practice is applicable to laboratory test methods.

1.4This practice is applicable to validated process stream analyzers.

1.5This practice is applicable to monitoring the differences between two analytical measurement systems that purport to measure the same property provided that both systems have been assessed in accordance with the statistical methodology in Practice D6708 and the appropriate bias applied.

Note 2:For validation of univariate process stream analyzers, see also Practice D3764.

Note 3:One or both of the analytical systems in 1.5 may be laboratory test methods or validated process stream analyzers.

1.6This practice assumes that the normal (Gaussian) model is adequate for the description and prediction of measurement system behavior when it is in a state of statistical control.

Note 4:For non-Gaussian processes, transformations of test results may permit proper application of these tools. Consult a statistician for further guidance and information.

1.7This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.