When quantifying impurities in steel and other materials, researchers at Los Alamos National Laboratory, Los Alamos, N. Mex., use a statistical short cut. Iron, the main elemental component of steel, interferes with the telltale signals of impurities from atomic emission spectroscopy. Normally the iron must be removed through a laborious chemical process that could contaminate the sample. But the researchers now have a multivariate algorithm that uses the entire spectrum available from spectral analysis, not just one wavelength. That mathematically "teases" out the signatures of impurities in the sample, researchers explain. They say the technique can be applied to any product with a bulb component that interferes with spectroscopic analysis.