Determination of 3D pore network structure of freeze-dried maltodextrin. Issue 4 (22nd February 2022)

• Record Type: Journal Article
• Authors: Thomik, M.; Gruber, S.; Foerst, P.; Tsotsas, E.; Vorhauer-Huget, N.
• Is Part Of: Drying technology. Volume 40:Issue 4(2022)
• Page Start: 748
• Page End: 766
• Publication Date: 2022-02-22
• Subjects: X-ray tomography -- image processing -- pore network modeling -- freeze-drying -- maltodextrin; Drying -- Periodicals; Desiccation; 660.28426
• DOI: 10.1080/07373937.2021.1966030
• Languages: English
• ISSNs: 0737-3937

Abstract

Abstract: This article describes and discusses the determination of the pore network structure from X-ray microcomputed tomography images of freeze-dried maltodextrin based on image processing. Maltodextrin is used because of its relevance for research and pharmaceutical applications. Image processing is based on conventional methods for image filtering and binarization, which are specifically the Gaussian filter, anisotropic diffusion filter, and Otsu thresholding as well as adaptive thresholding. It is shown, that the combination of the different image processing methods has only a minor impact on the final results of the estimated porosity. More critical is the segmentation of the overall void space into individual pores using the watershed algorithm, especially if the markers of the Euclidean distance map are not accurately defined. Oversegmentation of the void space into too many small pores was obviated by the use of a Gaussian smoothing filter on the Euclidean distance map. The approximation of the structure by spherical or cylindrical geometrical objects was addressed in further discussions as an essential step for the determination of the mathematical pore network model. It was found that the choice of the specific geometrical object has a major impact on the final pore size distribution of the generated pore networks. For the case of maltodextrin, with long and narrow pores, the approximation by segmented cylinders, replacing the individual pore subvolumes, yields the best agreement. The computed pore network structures are further investigated in regard of the correlation of vapor transport regimes and structure variation. It is shown that the small differences obtained from the various combinations of image processing and network reconstruction methods have a negligible impact if temperature and pressure are low ( T = −18 °C, P = 10 Pa) and constant.