This study was designed to assess the potential of transferring a calibration model from pharmaceutical powder mixtures to compacts of identical compositions using prediction augmented classical least squares (PACLS). A 2-factor, 5-level, full-factorial design was used to generate powder mixtures and compacts with a variety of formulation compositions. Spectra representative of powder mixtures were used for calibration, while spectra collected on both unrelaxed and relaxed compacts were used as the prediction dataset. The CLS augmentation strategy was to add empirically determined spectral shapes representative of the density differences between powder mixtures and compacts to the original K matrix. The performance of PACLS was compared to other commonly used modeling techniques, including classical least squares (CLS) and partial least squares (PLS) with and without spectral pretreatments and standardization. Significantly improved prediction performance (<i>p</i> < 0.05) was demonstrated by PACLS approaches compared to other techniques. This work demonstrated a technique to orthogonalize the spectral differences between powder and compact samples, allowing the prediction of chemical properties in compacts. Specific precautions for applying PACLS in a calibration transfer from powder mixtures to compacts are also discussed.

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