Demystifying Multivariate Analysis

This quick, one-time registration gives you access to members-only site benefits. Other strengths include performance on data with significant noise, missing values and the ability to model not only relationships between the X (raw materials and in-process data) and Y space (quality metrics), but also the internal correlational structure of X. The ability to model the internal structure of the X space is of fundamental importance, because a prediction method is only valid within the range of its calibration. Modeling the X space provides a means for recognizing whether a new set of data is similar or different from the training set the model was built on. Thus, MV methods can help predict and justify quality metrics. If the raw materials are dissimilar or a processing unit was operated differently from what the calibration data show, the confidence of the predicted quality metrics must be considered low. The FDA alludes to this point in its definition of process understanding in the PAT guidance as “the accurate and reliable prediction of product quality attributes over the design space established for materials used, process parameters, manufacturing, environmental and other conditions.” The model of the X space contained in these MV modeling methods is essentially a mathematical description of the design space the FDA is referring to in their guidance. The objective in modeling and analysis of this data is to develop process understanding. The FDA provides direction on what it means and how to develop process understanding. The FDA guidance on process analytical technology (PAT) provides four types of tools for generation and application of process understanding including: . This quick, one-time registration gives you access to members-only site benefits. Link to this article from your web site or blog Copyright © 2004 — 2009 Pharma Manufacturing.