Simultaneous prediction of the API concentration and mass gain of film coated tablets using Near-Infrared and Raman spectroscopy and data fusion

[Display omitted] •PLS models using NIR and Raman spectroscopy were evaluated for predicting API concentration and mass gain during film coating.•The study explored data fusion techniques (low-, mid-, and high-level) to improve the accuracy of these predictions.•Transmission spectroscopy models excelled at predicting API concentration by capturing detailed information from the entire tablet.•Reflection spectroscopy models were most effective for mass gain prediction, emphasizing the tablet’s surface layer.•Data fusion strategies significantly enhanced prediction accuracy, with mid-level fusion yielding the most robust results. This study investigates the simultaneous prediction of active pharmaceutical ingredient (API) concentration and mass gain in film-coated tablets using Partial Least Squares (PLS) regression combined with three data fusion (DF) techniques: Low-Level (LLDF), Mid-Level (MLDF), and High-Level (HLDF). Near-Infrared (NIR) and Raman spectroscopy were utilized in both reflection and transmission modes, providing four types of spectral data per tablet. Transmission models proved more effective for API prediction by capturing data from the entire tablet, while reflection models excelled in assessing mass gain by focusing on the surface layer. Among the DF strategies, MLDF with Principal Component Analysis (PCA) offered the most significant improvements in predictive accuracy by filtering out irrelevant information. Variable selection methods further enhanced model performance by reducing the number of latent variables required. Overall, the integration of multiple spectral datasets and DF techniques resulted in models that gave predictions for evaluation samples with lower errors, demonstrating their potential to optimize quality control in pharmaceutical manufacturing.