Knowledge-based systems for analytical chemistry

Analytical chemists have made extensive use of computer technology, automating most analytical instruments, many analyses and reporting activities. We now need to expand the areas to which computers can be applied by addressing problems of greatly increased complexity. These problems fall into two general classes, the first class of problems are those thatcannot be solved using only first principle information and, the second class are those problems thatcan be solved using only first principles, but that are so complex that the traditional approach is often not cost effective.The discussion will center on how artificial intelligence technology (AI) can provide the means for using heuristics together with first principle information to solve instances of the first class of problems. The knowledge required to provide the solution is formulated as facts, rules (heuristics) and an inference engine.The same AI technology can also be used to refine specifications and provide cost effective solutions for very complex problems involving only first principle information. For this class of problems an AI work station can provide the software development conductive to rapid prototyping and specification refinement.Discussion of several expert systems will be used to describe the capabilities and features of rule-based systems. The strengths and weaknesses of one system, use of IR spectra for structure elucidation, will be examined in some detail. It is an expert system consisting of rule sets organized into logic trees, thus it will provide an opportunity to describe how the use of more advanced AI technology could further improve the program's performance.