A Calibration Model Maintenance Roadmap

Abstract

Multivariate calibration, classification and fault detection models are ubiquitous in QbD (Quality by Design) and PAC and PAT (Process Analytical Chemistry and Technology, respectively) applications. They occur in the both the development of processes and their permissible operating limits, (i.e. models for relating the process design space to product quality), and in manufacturing (i.e. models used in monitoring and control). Model maintenance is the ongoing servicing of these multivariate models in order to preserve their predictive abilities. It is required because of changes to either the sample matrices or the instrument or response. The goal of model maintenance is to sustain or improve models over time and changing conditions with the least amount of cost and effort. This paper presents a roadmap for determining when model maintenance is required, the probable source of the response variations, and the appropriate approaches for achieving it. Methods for evaluating model robustness in order to identify models with lower ongoing maintenance costs are also discussed.