Application of artificial intelligence techniques for inductively coupled plasma spectrometry

The development of intelligent components for the automated analysis of samples by inductively coupled plasma (ICP) Spectrometry is presented. An expert system for diagnosing an ICP atomic emission spectrometry (AES) system using a blank solution was developed as a warning system. This expert system was able to warn the system of major malfunctions and was able to identify most problems. Three pattern recognition techniques were compared in their ability to recognize similar geological samples in small databases. Two of these techniques, k-Nearest Neighbours and Bayesian Classification, worked extremely well with over 96% success. The development of an objective function for multi-element optimizations in ICP-AES is presented. Various aspects of the application of a Simplex optimization were explored for the optimization of the ion optics of an ICP-mass spectrometry (MS) system. An algorithm for the automatic selection of internal standards for analytes in difficult samples in ICP-MS is presented.