Pharmaceutical Analysis

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Award recipient John McLean of Vanderbilt University said hybrid techniques do not exist purely as combinations of letters, slashes, and hyphens—they have been built on the shoulders of decades’ worth of analysis intended to refine and simplify workflow.

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The accuracy of prediction models based on spectroscopic measurements can be influenced by a variety of factors, including aging equipment. Continuous monitoring is key to managing the PAT model lifecycle and ensuring that changes over time do not negatively affect prediction model performance.

In this review, we show a wide range of examples of the expanding use of multivariate analysis (MVA) in pharmaceutical manufacturing and control. MVA is being used to resolve numerous analytical challenges, such as overcoming matrix effects, extracting reliable data from dynamic matrices, and more.