Analyses of gamma-ray spectra, acquired through non-invasive techniques, have found applications in fields such as medicine, industry and homeland security. Constituent gamma-ray spectra of a chemical compound have been determined from its sole spectrum through a forward Monte Carlo simulation coupled with a least squares method (MCLLS). The method's limitations include its linearity assumption and its oversensitivity to correlated or noisy data, which render the method unfit to deal with such numerical ill conditioning. Recently this issue was tackled by iteratively reducing the condition number of the linear system of equations. Despite its superior results, it is not suitable for cases where there are missing libraries in the analysis. Our work introduces a novel framework that allows treating spectral analyses problems through geometrical insights. Based on this it was possible to propose solutions to three problems regarding the missing library: to find its photopeak, its most probable fraction, and an envelope around its spectrum. We successfully validated these on some Monte Carlo-generated radionuclide gamma-ray spectra.