During the past decade, near-infrared (NIR) spectroscopy has been applied for in-line moisture content quantification during a freeze-drying process. However, NIR has been used as a single-vial technique and thus is not representative of the entire batch. This has been considered as one of the main barriers for NIR spectroscopy becoming widely used in process analytical technology (PAT) for freeze-drying. Clearly it would be essential to monitor samples that reliably represent the whole batch. The present study evaluated multipoint NIR spectroscopy for in-line moisture content quantification during a freeze-drying process. Aqueous sucrose solutions were used as model formulations. NIR data was calibrated to predict the moisture content using partial least-squares (PLS) regression with Karl Fischer titration being used as a reference method. PLS calibrations resulted in root-mean-square error of prediction (RMSEP) values lower than 0.13%. Three noncontact, diffuse reflectance NIR probe heads were positioned on the freeze-dryer shelf to measure the moisture content in a noninvasive manner, through the side of the glass vials. The results showed that the detection of unequal sublimation rates within a freeze-dryer shelf was possible with the multipoint NIR system in use. Furthermore, in-line moisture content quantification was reliable especially toward the end of the process. These findings indicate that the use of multipoint NIR spectroscopy can achieve representative quantification of moisture content and hence a drying end point determination to a desired residual moisture level.
In conclusion, tablets could be successfully prepared by a continuous direct compression process and process conditions affected to some extent tablet properties.
Near-infrared (NIR) spectroscopy permits non-contact analysis of solid samples in the diffuse reflectance (DR) measurement mode. However, uncontrolled physical variations between solid samples, such as changes in packing density and particle size distribution, have a complex nonlinearizing effect on the NIR spectra which complicates the extraction of chemical information from data. Blind source separation (BSS) methods attempt to blindly factorize the measured mixture spectra into the pure analyte spectra and their concentration profiles. The physical interferences, however, make the application of BSS methods difficult on the NIR spectra of solids. The application of independent component analysis (ICA) on NIR DR spectra is discussed, and a three-phase preprocessing procedure of the measured spectral signals designed to improve the separation capability of ICA is proposed in this work. The method involves the removal of nonlinear effects from the measured spectra using scatter correction, denoising with rank reduction and alteration of the sample statistics of the signals via differentiation with respect to the wavelength. The procedure is tested and the explanatory power of BSS is demonstrated using a laboratory data set comprising ternary mixtures of pharmaceutical powders.
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