Many pharmaceutical problems require chemical identification of the ingredients present in a drug product, e.g., a tablet. Examples include the identification of the compounds present in many steps of the manufacturing process and the chemical characterization of counterfeit and third-party tablets. Hyperspectral unmixing of near-infrared images is a key method for solving the above problems, as it provides estimates of the number of pure compounds present in a mixture, their spectral signatures, and the corresponding spatially mapped abundance fractions. The performance of hyperspectral unmixing depends upon the degree of homogeneity of the tablets, as well as the pixel resolution used for image acquisition. This work explores the use of the recent <i>simplex identification via split augmented Lagrangian</i> (SISAL) algorithm to unmix near-infrared images of tablets under different homogeneity and pixel resolution conditions. SISAL is known to solve complex problems beyond the reach of previous hyperspectral unmixing methods. The tablets used in this study are 4- and 5-compound model pharmaceutical mixtures, produced with good and poor blending processes, and the acquisition was performed at three pixel resolutions: 8.1, 27.9, and 40.3 μm/pixel. Heterogeneity proved to increase SISAL's accuracy, as did increased pixel resolution in homogeneous tablets. Given the fast image acquisition and algorithm execution times, low- and high-resolution images should always be acquired; combined with the homogeneity grade of the samples, this may be determinant to a case-by-case decision on the proper action to be taken next.

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