In summary, the technique uses wavelet decomposition on the grayscale images to reduce them to a vector of energies for multiple “frequencies” (called details in wavelet theory), such vectors are then compressed using Principal Components Analysis (for characterization) or used as regressors against a property of interest (e.g. in vitro dissolution, compression profile)
This method is illustrated with two case studies:
i) The analysis of an intermediate pharmaceutical product (wet granules from a high shear wet granulation step) to provide a quantitative envelope for the material, and also to predict its manufacturability at the compression step (after drying and milling).
ii) The characterization of a modified release film coated tablets, where the textural characteristics of the tablet are linked to the in vitro dissolution of the product.
Using a latent variable regression model, it was found that the appearance of the wet granules is able to predict 80% of the hardness compression profile for the material. In the case of the modified release tablets, the visual characteristics of the tablet alone can predict dissolution at 9hr with a 91% R2 accuracy. A model that considers the processing conditions in the film coater and the visual appearance is able to predict dissolution to a 97% R2.&'
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