Hyperspectral imaging helps in identifying patterns and objects in an observed hyperspectral scene on the basis of their unique spectral signatures; such identification is otherwise difficult using regular imaging. Recently, ink mismatch detection analysis based on hyperspectral imaging has shown enormous potential in distinguishing visually similar inks. Such analysis provides significant information to forensic document examiners to determine the authenticity of the questioned documents. However, major challenge still exist in disproportionate ink mismatch detection because it is inherently an unbalanced clustering problem. The presented approach deals with ink mismatch detection in unbalanced clusters by using hyperspectral unmixing scheme. It identifies the spectral signatures (endmembers) of the inks and their corresponding proportions (abundances). Our results show that HySime outperforms other methods in signal subspace estimation. Hyperspectral unmixing is done by using minimum volume enclosing simplex algorithm. Efficacy of the purposed approach is demonstrated by successfully distinguishing varying disproportionate ink datasets generated from UWA database and results are compared with existing state of the art methods in hyperspectral ink mismatch detection field. We expect that these finding will further encourage the use of hyperspectral imaging in document analysis, particularly towards automated questioned document examination.