Teaching a computer ion chromatography from a database of published methods
Mulholland, M and Preston, P and Hibbert, DB and Haddad, PR and Compton, P, Teaching a computer ion chromatography from a database of published methods, Journal of Chromatography A, 739, (1-2) pp. 15-24. ISSN 0021-9673 (1996) [Refereed Article]
As ion chromatography (IC) has matured as an analytical technique it has become more automated. Most instrument control and data handling is now handled by computers. However, IC has not seen the abundance of automated method optimisation techniques which are provided to conventional chromatography. To a certain extent this was because IC differed greatly in the approach required to optimise selectivity and sensitivity. There was quite a diverse range of chemistries (or separation mechanisms) applicable to IC, such as ion exchange, ion interaction, etc.
This paper describes an effort to fill this gap by developing an expert system which can give comprehensive advise on suitable method conditions for a variety of IC mechanisms. To build this system we applied an approach known as induction by machine learning, which was developed within the field of artificial intelligence (AI). A database of over 4000 published methods using IC, where the sample information and the chromatographic conditions were recorded, was used to train an expert system (ES).
Both induction and a neural network model were applied to this task and an expert system which can advise on the following IC method conditions: mobile phase, column, pH, mechanism, post-column reactors, suppressor use and gradient applicability, was successfully developed. This paper presents a summary of the most pertinent conclusions from this study.
A test set of different methods was extracted from the database and they were not applied in the training of the expert system. These were used to test the expert system and different amounts of information were used as inputs. The resulting outputs of the expert system were evaluated by the expert, who decided whether the method would work or not and if it was a good method or the ideal method for the application. Over 85% of methods were found to work and almost 62% of the methods were considered ideal. These were acceptable results when one considers the limitations of using a database of published methods as a learning set and the time saved by the use of machine learning.
Induction by machine learning; Expert systems; Optimization; Ion chromatography optimization; Machine learning; Anions; Cations