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The prediction of radial tensile strength (RTS) of relaxing solid dosage forms by near-infrared hyperspectral chemical imaging was studied. Compacts consisting of starch, lactose, and a mixture of four components were created at different compression forces to develop density models. Predicted density distribution parameters were subsequently used to estimate RTS. Chemical images were collected shortly after compression, repeated every 30 min for 2 h, and a final image was collected after 2 weeks. A two step process, involving first the prediction of compact density at each pixel (using a partial least squares model) and second the relationship between compact density distributions and RTS was implemented. Among the parameters with a significant relationship with RTS, the median of the distribution of density predictions in an image was identified as a robust parameter. Coefficients of determinations for this prediction ranged from 0.96 to 0.99 were obtained with a maximum error in validation of 0.10 MPa for the four-component formulation compacts. The prediction of RTS of fully relaxed compacts from spectral data collected on relaxing compacts was demonstrated. These results demonstrate the potential to use near-infrared chemical imaging in real-time to predict RTS values of fully relaxed compacts.