Laboratory Automation & Information Management最新文献

In today's laboratories, increased efficiency and productivity are of extreme importance. Equally important is the ability to automate analyses without sacrificing sample integrity or data quality. A new vial autosampler has been developed to fully automate purge and trap analysis of water and wastewater samples in accordance with current EPA methods for volatile analysis. Research is presented demonstrating the autosampler's ability to transfer sample aliquots with the addition of internal standard or surrogate solutions. Data are evaluated for linearity, precision, and accuracy. In addition, the instrument's sample pathway is evaluated for carryover, inertness, and reliability.

Clinical laboratories produce on a daily basis huge amounts of data, i.e., test results of chemical determinations. The production and the use of these data should be extremely well structured. This and the urge to improve quality was the reason to design a reference information model for clinical laboratories, abbreviated as RILA. A software toolbox facility was used to the design. RILA can be used in any clinical laboratory, but it can also be made laboratory situation-specific. This is done through the implementation of location-specific and detailed information concerning procedures, instruments, capacities, etc. By doing this, the interaction with the laboratories environment has to be defined as well. Certain application areas such as total quality management, workflow analysis and cost analysis can be facilitated through the implemented reference information model. Furthermore, RILA has to be positioned at a proper location with regard to the existing hospital and laboratory automation modules.

This article concerns a new expert system for elucidating structures of organic compounds from their spectra, where two principally different molecular generators are used. The system is based on a database of ${}^{13}\text{C}$ NMR spectra (135,000 structures), a library of molecular fragments and their ${}^{13}\text{C}$ NMR subspectra (more than 400,000) and a library of spectrum-to-structure correlations for both NMR and IR spectra. At the first stage, the system tries to automatically determine the structure of the unknown compound using fragments taken from the library of ${}^{13}\text{C}$ NMR spectra. The structures are generated by joining fragments that have common atoms. Knowledge of the molecular mass and the molecular formula is not needed for this stage. In case of failure the program automatically determines the molecular formula from the molecular mass (different formulas can have the same mass) and then forms the sets of fragments. The structures are generated with the help of another generator designed for constructing structures from non-overlapping fragments. The libraries of spectrum-to-structure correlations are used for determination of the molecular formula as well as for spectral filtration of fragments and structures. The most preferable structure is selected by predicting ${}^{13}\text{C}$ and ${}^1\text{H}$ NMR spectra. Some examples of problem solution are cited.

For laboratory automation and quality schemes to succeed, there must be management commitment to the project. To get significant improvements in laboratory performance, critical changes are needed which require open-minded and team approaches. Unless this happens, the project will fail. This paper explores the reasons why older style managers fail to support these projects. Radical changes in management are proposed; from direction to teamwork and commitment; from power to delegated authority and empowerment. This already happens in the leading companies, but many organisations have older management styles which should change.

This article is an extract of a presentation given at the LIMS conference in the Hague June 4, 1997 [S.M. Fransen et al., Presentation given at the conference LIMS 97, June 4 1997 in the Hague.]. It describes the project steps for the LIMS project in the Danish company Novo Nordisk, with focus on vendor evaluation, validation and implementation of a ‘Standard’ LIMS software. The main project steps are described and experiences gained are discussed.