The use of spectroscopic methods, such as near-infrared or Raman, for quality control applications combined with the constant search for finer details leads to the acquisition of increasingly complex data sets. This should not prevent the user from characterizing a sample by identifying and mapping its chemical compounds. Multivariate data analysis methods make it possible to obtain qualitative and quantitative information from such data sets. However, samples containing a large (and/or unknown) number of species, segregated trace compounds (present in few pixels), low signal-to-noise ratios (SNR), and often insufficient spatial resolutions still represent significant hurdles for the analyst.