Abundances of material components in objects are usually computed using techniques such as linear spectral unmixing on individual pixels captured on hyperspectral imaging devices. The effectiveness of these algorithms usually depends on how distinct the spectral signatures in the libraries used in them are. This can be measured by SVD or Least Squares based figures of merit such as the condition number of the matrix consisting of the library signatures. However, it must be noted that each library signature usually is the mean of a number of signatures representing that material, or class of objects. This aspect of how individual library spectral signatures vary in real-world situations needs to be addressed in order to more accurately assess linear unmxing techniques. These same considerations also goes for signature libraries transformed into new ones by wavelet or other transforms. Figures of merit incorporating variations within each library signature (which more accurately reflects real measurements) will be implemented and compared with other figures of merit not taking these variations into account.