Model maintenance: the unrecognized cost in PAT and QbD

摘要

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 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 on-going servicing of these multivariate models in order to preserve their predictive and diagnostic abilities. It is required because of changes to either the sample matrices or the instrument response. The goal of model maintenance is to sustain or improve models over time and react to changing conditions with the least amount of cost and effort. A model maintenance roadmap is presented. It includes procedures for determining when model maintenance is required, the probable source of the model/data mismatch, and the best approaches for bringing model performance back to acceptable levels.