A batch modelling approach to monitor a freeze-drying process using in-line Raman spectroscopy

Freeze-drying or lyophilisation is a batch wise industrial process used to remove water from solutions, hence stabilizing the solutes for distribution and storage. The objective of the present work was to outline a batch modelling approach to monitor a freeze-drying process in-line and in real-time using Raman spectroscopy. A 5% (w/v) d-mannitol solution was freeze-dried in this study as model. The monitoring of a freeze-drying process using Raman spectroscopy allows following the product behaviour and some process evolution aspects by detecting the changes of the solutes and solvent occurring during the process. Herewith, real-time solid-state characterization of the final product is also possible. mely spectroscopic measurements allowed the differentiation between batches operated in normal process conditions and batches having deviations from the normal trajectory. Two strategies were employed to develop batch models: partial least squares (PLS) using the unfolded data and parallel factor analysis (PARAFAC). It was shown that both strategies were able to developed batch models using in-line Raman spectroscopy, allowing to monitor the evolution in real-time of new batches. However, the computational effort required to develop the PLS model and to evaluate new batches using this model is significant lower compared to the PARAFAC model. Moreover, PLS scores in the time mode can be computed for new batches, while using PARAFAC only the batch mode scores can be determined for new batches.