Performance characteristics of a composite multivariate quality control system

Abstract

We present the results of an evaluation of the performance characteristics of a composite multivariate quality control (CMQC) system that incorporates quality control rules for univariate, multivariate, and correlation conditions. The CMQC system evaluated is designed to help analysts detect unacceptable trends and systematic error in one or more variables, unacceptable random error in one or more variables, and unacceptable changes in the correlation structure of any pair of variables. It is also designed to be tolerant of missing data, to allow analysts to reject as few as one or as many as all variables in a run, and to provide analysts with control statistics and graphics that logically relate to sources of analytical error. We show that the various components of the CMQC system have adequate statistical power to detect systematic errors, random errors, and correlation changes under the conditions likely to be encountered with multivariate analytical measurement systems: (1) a single variable with increased systematic or random error; (2) all variables or a subgroup of variables affected by a common problem that increases systematic or random error; and (3) missing data for one or more variables in a run. We also show that the power of the multivariate component of the CMQC system to detect systematic and random errors is higher than the power of an alternative multivariate test criterion.

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