Intelligent Instrumentation

Feasibility studies on the application of multivariate statistical and mathematical algorithms to chemical problems have proliferated over the past 15 years. In contrast to this. most conunercia11y available computerized analytical instruments have used in the data systems only those algorithms which acquire, display, or massage raw data. These techniques would fall into the "preprocessing stage" of sophisticated data analysis studies. An exception to this is, of course, are the efforts of instrumental manufacturers in the area of spectral library search. Recent firsthand experiences with several groups designing instruments and analytical procedures for which rudimentary statistical techniques were inadequate have focused efforts on the question of multivariate data systems for instrumentation. That a sophisticated and versatile mathematical data system must also be intelligent (not just a number cruncher) is an overriding consideration in our current development. For example, consider a system set up to perform pattern recognition. Either all users need to understand the interaction of data structures with algorithm type and assumptions or the data system must possess such an understanding. It would seem, in such cases, that the algorithm driver should include an expert systems specifically geared to mimic a chemometrician as well as one to aid interpretation in terms of the chemistry of a result. Three areas of modem analysis will be discussed: 1) developments in the area of preprocessing and pattern recognition systems for pyrolysis gas chromatography and pyrolysis mass spectrometry; 2) methods projected for the cross interpretation of several analysis techniques such as several spectroscopies on single samples; and 3) the advantages of having weIl defined chemical problems for expert systems/pattern recognition automation.