Efficient Near-Infrared Spectroscopic Calibration Methods for Pharmaceutical Blend Monitoring

Abstract

Near-infrared (NIR) spectroscopy is an important analytical tool for online process monitoring of pharmaceutical unit operations. Traditionally, the development and maintenance of robust, precise, and accurate quantitative NIR calibrations requires a substantial investment for the creation of sample sets. This study demonstrates the ability to develop efficient NIR calibrations using reduced sample sets. Prediction performance of several multivariate algorithms was compared on two different NIR spectrometers for pharmaceutical blend monitoring. Classical least-squares (CLS)-based algorithms took advantage of pure component scans to produce the most sensitive quantitative calibrations using reduced sample sets when compared to partial least squares (PLS) regression and two nonlinear methods. The PLS algorithm and the nonlinear methods produced models with low error but lacked the sensitivity needed to model subtle blending trends. The CLS-based methods produced models with adequate sensitivity for blend monitoring. The robustness of the CLS-based methods was further demonstrated in the ease of transfer between instruments using only a bias correction of the predictions.