Standard Practice for Validation of Empirically Derived Multivariate Calibrations
|Publication Date:||15 December 2017|
|ICS Code (Metrology and measurement in general):||17.020|
This practice covers requirements for the validation of empirically derived calibrations (Note 1) such as calibrations derived by Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS), Artificial Neural Networks (ANN), or any other empirical calibration technique whereby a relationship is postulated between a set of variables measured for a given sample under test and one or more physical, chemical, quality, or membership properties applicable to that sample.
NOTE 1-Empirically derived calibrations are sometimes referred to as "models" or "calibrations." In the following text, for conciseness, the term "calibration" may be used instead of the full name of the procedure.
This practice does not cover procedures for establishing said postulated relationship.
This practice serves as an overview of techniques used to verify the applicability of an empirically derived multivariate calibration to the measurement of a sample under test and to verify equivalence between the properties calculated from the empirically derived multivariate calibration and the results of an accepted reference method of measurement to within control limits established for the prespecified statistical confidence level.
This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.
This 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.