BUILDING EXPERT-SYSTEMS; FUEL PROPERTIES; SPECTROSCOPY; MODEL

A quiet but interesting trend has been occurring in material analysis, coincident with the rise of artificial intelligence (Al) and so-called ""deep"" machine learning methods. Astute spectroscopists have always known that there is more information in the spectra that they obtain than simply the molecular or atomic peaks that are directly measured. Particularly with methods such as infrared, Raman, and laser-induced breakdown spectroscopy (LIBS), the spectral background contains a wealth of information about the sample, and analytical combinations of the peaks can provide material properties. Traditionally, such analytical combinations of peaks were performed explicitly by analysts, but now information about material properties embedded in the spectra can be derived implicitly by Al and machine learning algorithms. This column introduces these ideas and touches on recent results indicative of what more may be coming in this direction.