ESCA, Expert Systems Applied to Chemical Analysis, started its research in March 1987, with the aim of building prototype expert systems for HPLC method development. Results of this research have been published as the work has progressed. The project is now completed and this paper summarises some of the overall project conclusions. Seven different expert systems have been built which tackle problems throughout the process of method development, four stand-alone systems and three integrated systems. The object of ESCA was to evaluate the applicability of expert system technology to analytical chemistry and not all the systems were built for commercial uses. Many of the systems tackle problems specific to one or more of the partners and thus may not be useful outside this environment. However, the results of the work are still pertinent to analysts wishing to build their own systems. These results are described, however, the emphasis of the paper is on those systems developed for method validation.Method validation for HPLC is a complex task which requires many characteristics of the method to be tested, e.g. accuracy, precision, etc. The expert systems built within ESCA concern the validation of precision. Two systems were developed for repeatability testing and ruggedness testing. The method validation process can be divided into several discrete stages, these include: (1) The selection of the method feature to test, for instance which factors can influence the ruggedness of a method. (2) The definition of a test procedure, for instance an efficient statistical design. (3) The execution of experiments and the interpretation of results. (4) A diagnosis of any observed problem. This paper describes these two systems in some detail and summarises some of the results obtained from their evaluation. It concludes that expert systems can be useful in solving analytical problems and the integration of several expert systems can provide extremely powerful tools for the analyst. 相似文献
A new and simple LC-MS method for analysis of flavonoids from Sambucus ebulus berry extracts was developed and validated. Successfully were quantitated seven polyphenols: epicatechin, epigallocatechin gallate, rutin, resveratrol, myricetin, quercetin, and kaempferol.
Two detectors, working in parallel, were used: photodiode-array and single quadrupole mass-detector. The mass detection was used for identification and quantification of the analytes, while the diode-array detector was as confirmation tool. The following m/z were tracked: 457.15 (epigallocatechin gallate); 289.06 (epicatechin); 609.13 (rutin); 227.05 (resveratrol); 317.0 (myricetin); 301.02 (quercetin); 285.02 (kaempferol). For optimization the chromatographic separation three wavelengths 205?nm, 305?nm, 272?nm were monitored. The method was capable to detect in one run compounds with no UV or fluorescence chromophore and with very similar structures, such as plant polyphenols. The linearity was from 0.05?mg/L to 50?mg/L (R2 0.9962–0.9987). The recoveries for all tested analytes were between 81.6% and 104.7%.
The method was applied for analysis of crude extract of Sambucus ebulus ripe fruits. Three major polyphenols – epicatechin (0.84?mg/100gFW), quercetin (0.15?mg/100gFW) and kaempferol (0.05?mg/100gFW) were identified and quantified.
The proposed method could be successfully used for routine analysis of epigallocatechin gallate, epicatechin, rutin, resveratrol, myricetin, quercetin, and kaempferol in Sambucus ebulus extracts. 相似文献
Industrially advanced countries and inereasingly also agricultural countries today use radioisotopes as labelled compounds or scaled sources in order to solve their scientific and technological problems. 相似文献
Reaction optimisation and understanding is fundamental for process development and is achieved using a variety of techniques. This paper explores the use of self-optimisation and experimental design as a tandem approach to reaction optimisation. A Claisen-Schmidt condensation was optimised using a branch and fit minimising algorithm, with the resulting data being used to fit a response surface model. The model was then applied to find new responses for different metrics, highlighting the most important for process development purposes. 相似文献