Objective
The ESCA project aimed to replicate a specific area of human expertise in chromatography and chemistry by artificial intelligence systems. There were two main areas of research: the formalisation of the knowledge base in this area, and the selection of the most suitable expert system shells and tools to represent this type of knowledge base.
The first work-package was to select a suitable specific area of chromatography application to pharmaceutical analysis, where the knowledge is sufficiently established to provide a valid test of an artificial intelligence system.
The second work-package was to formalise the knowledge of this specific area by a set of logical rules and facts suitable for expression as an expert system.
It was planned to select about eight candidate shells and tool sets and to evaluate their suitability for representing this type of knowledge. The next step was make a selection of three of these candidates for the implementation task.
The application of AI to chemometrics, ie the use of mathematical techniques for setting up experiments and for analysing the results, was to be examined.
The final product was to be a comparison of the performance of these three expert systems (using different characteristics, but with identical knowledge) for real chemical analyses.
The project aimed to replicate a specific area of human expertize in chromatography and chemistry by artificial intelligence systems. There were 2 main areas of research: the formalization of the knowledge base in this area, and the selection of the most suitable expert system shells and tools to represent this type of knowledge base. The first work package was to select a suitable specific area of chromatography application to pharmaceutical analysis, where the knowledge is sufficiently established to provide a valid test of an artificial intelligence system. The second work package was to formalize the knowledge of this specific area by a set of logical rules and facts suitable for expression as an expert system. The application of artificial intelligence (AI) to chemometrics, ie, the use of mathematical techniques for setting up experiments and for analysing the results, was examined. High performance liquid chromatography (HPLC) was chosen as the area of application. Three expert system developement tools were selected from the 8 evaluated. The acquisition of knowledge from each of the application domains, which together cover the entire area of method development in HPLC, was completed. its representation in the form of several expert systems was carried out, and integration into one system (from the chemist's point of view) was achieved.
High Performance Liquid Chromatography (HPLC) was chosen as the area of application. Three expert system development tools were selected from the eight evaluated. The acquisition of knowledge from each of the application domains, which together cover the entire area of method development in HPLC, was completed. Its representation in the form of several expert systems was carried out, and integration into one system (from the chemist's point of view) was achieved.
The presentation of the results at international symposia has increased awareness of the field and heightened debate about the issues involved.
Exploitation
Chromatography is a major analytical tool in pharmaceutical research. However, its use requires the selection of a suitable chromatographic method and the optimisation of parameters for each analysis. At present these actions are dependent on the skills of an expert chromatographer.
The ESCA project aimed to alleviate this situation by developing the application of expert systems to a real-life analytical problem: method development for the analysis of novel compounds in the pharmaceutical industry.
The experience and knowledge gained through the prototypes developed and the comparison studies carried out will accelerate the introduction of expert systems in real-life industrial applications related to chemical domains.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
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Coordinator
CB1 2PX CAMBRIDGE
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.