Evaluation of in–line Raman data for end-point determination of a coating process: Comparison of Science–Based Calibration, PLS-regression and univariate data analysis

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Abstract

A multivariate analysis method, Science–Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol–polyethylene glycol graft–copolymer and titanium dioxide to a maximum coating thickness of 80 μm. Raman spectroscopy was used as in–line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well–known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R2) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power.

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