Automated combination of the results of elemental and structural analysis with the help of a learning knowledgebase to identify the contained analytes: A theoretical reflection

In nature, substances mostly occur in complex matrices. For methods in analytical measurement it is important to reach a high selectivity to get the correct qualitative and quantitative results for the substances in these mixtures. Therefor the general concept of the pre-, intra- and post-sensorial selectivity is suitable. The different analytical methods of the pre- and intra-sensorial steps are used to reach the required selectivity. Since automation gain growing importance in modern laboratories the data output is increased massively. With a manual data evaluation as a post-sensorial step it is not possible to overcome this high amount of data. Modern laboratories are mostly electronic and paperless, so databases and intelligent software can be used to interpret the received data from the analytical devices. This paper shows the necessity of the combination of different methods of the pre- and intra-sensorial steps or their results to obtain a higher selectivity. Also the developed system concept for the automated combination of the results of elemental and structural analysis with the help of a learning knowledgebase to identify the contained analytes inside a complex matrix is presented. Therefor the existing infrastructure of the software Project ADE is used.

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