Automated data analysis system

Automated Data Analysis System for a Gel Permeation Chromatograph with Multiple Detectors... 

T. Provder, "An Automated Data Analysis System for A Waters Model 150C ALC/GPC System with Multiple Detectors", This Volume. 

Lloyd et al.1 described automation processes for compound optimization and simultaneous implementation of (1) a LIMS system to automate and track the flow of sample information, data analysis, and reporting (2) an automated data archiving system to handle a large number of LC/ MS/MS data files (3) custom software to track a large number of protocol flows and (4) workstation automation. 

The major bottleneck created by these high-throughput NMR techniques is in the analysis of the vast amount of data that is generated. A number of commercial packages are now available that use chemical shift/structure databases to aid in the interpretation of the spectra. However, fully automated spectral analysis systems are still under development. 

A number of technology issues have been addressed concerning plates, liquid handling systems, methods of detection, and automated data analysis. It is possible to format an entire million-compound collection into 1536-well plates. With such ready availability, the number of assays that use this technology will increase significantly. 

One of the software systems available for pattern recognition studies is ADAPT (automated data analysis using pattern recognition techniques). The structure of each member of the data set is represented by molecular descriptors. These numerical indices, which encode information about the molecule, fall into four classes topological, geometrical, electronic and physicochemical. The data are analysed using pattern recognition techniques to develop a classifier which can discriminate between the classes of data. 

It is clear from the literature review that the need continues to exist for a high throughput functional toxin detection system, which could detect and identify unknown or unexpected toxic chemicals in continuous long term experiments in field conditions. Microelectrode array recordings may show some promise in some specific fields as they are relatively more rugged, simpler and cheaper to implement than automated patch clamp devices. However, in addition to international validation studies, cell type selection and automated data analysis capabilities of multiple signals will be critical. 

Fragments at double-digit millimolar concentrations are usually soaked into pre-formed crystals, though sometimes co-crystalhzation is used. Robotic systems handle the crystals and the data are collected using a standard X-ray source or the quicker and better (but less easily accessible) synchrotron radiation. Software hke Astex s AutoSolve is used to automate data analysis and interpretation. The end result can be tremendously useful high-resolution pictures of fragment-binding that can jumpstart subsequent SBDD optimization efforts. 

Fig. 1. Automated data acquisition system for the light-scattering (LS) spectrometer. Static LS data could be acquired by the DEC PDF-11/73 computer automatically. Dynamic LS data were transferred from the Brookhaven correlator to the computer for data analysis. 

Because of the large number of samples and repetitive nature of environmental analysis, automation is very important. Autosamplers are used for sample injection with gc and Ic systems, and data analysis is often handled automatically by user-defined macros in the data system. The high demand for the analysis of environmental samples has led to the estabUshment of contract laboratories which are supported purely by profits from the analysis. On-site monitoring of pollutants is also possible using small quadmpole ms systems fitted into mobile laboratories. 

The quahty of an analytical result also depends on the vaUdity of the sample utilized and the method chosen for data analysis. There are articles describiag Sampling and automated sample preparation (see Automated instrumentation) as well as articles emphasizing data treatment (see Chemometrics Computer technology), data iaterpretation (see Databases Imaging technology), and the communication of data within the laboratory or process system (see Expert systems Laboratory information managet nt systems). 

Bioprocess Control An industrial fermenter is a fairly sophisticated device with control of temperature, aeration rate, and perhaps pH, concentration of dissolved oxygen, or some nutrient concentration. There has been a strong trend to automated data collection and analysis. Analog control is stiU very common, but when a computer is available for on-line data collec tion, it makes sense to use it for control as well. More elaborate measurements are performed with research bioreactors, but each new electrode or assay adds more work, additional costs, and potential headaches. Most of the functional relationships in biotechnology are nonlinear, but this may not hinder control when bioprocess operate over a narrow range of conditions. Furthermore, process control is far advanced beyond the days when the main tools for designing control systems were intended for linear systems. 

In the analysis of vibration data there is often the need to transform the data from the time domain to the frequency domain or, in other words, to obtain a spectrum analysis of the vibration. The original and inexpensive system to obtain this analysis is the tuneable swept-filter analyzer. Because of inherent limitations of this system, this process, despite the use of automated sweep, is time-consuming when analyzing low frequencies. When the spectra data needs to be digitized for computer inputing, there are further limitations in capability of tuneable filter-analysis systems. 

Recent advancements in microprocessor technology coupled with the expertise of companies that specialize in machinery diagnostics and analysis technology, have evolved the means to provide vibration-based predictive maintenance that can be cost-effectively used in most manufacturing and process applications. These microprocessor-based systems simplify data acquisition, automate data management, and minimize the need for... 

The net result, at any rate, is that there has not been a strong need, apparently, to develop alternative systems and/or these systems have not gained wide popularity and use within the industrial and academic communities in the field. When this has been attempted, no clear wirmer has emerged when consideration was given to advantages and disadvantages of potential alternatives [6] and to an increase in the complexity of data analysis and automation. 

Automated electronic data capture systems have become increasingly important in the laboratory. They have improved the ease of manipulation and reporting of chromatography data. A regulatory requirement is that these systems must generate a permanent audit trail of the parameters employed in the collection and analysis of those data. 

There are many things to be considered when purchasing an automated chromatography data collection system. A needs analysis must be conducted, including a prioritization of proposed requirements and uses. No single system is the best for all situations. However, a prioritization of needs can reduce the search. Some of the major items to consider are as follows ... 

It is interesting to trace the development of instrument automation over the relatively brief period of the past ten to fifteen years. Early in this period, a truly automated instrument was a rare and expensive item built around a costly dedicated minicomputer. Automated data collection and analysis from any instrument which was not automated at the factory was usually accomplished by digitizing the data and storing it on a transportable media such as paper tape. These data were then delivered and fed to a timeshare system of some sort on which the data reduction program ran and which printed a report and sometimes a plot of the data. Often a considerable time delay occured between the generation and the analysis of the data. The scientist was at the mercy of the computer elite who could implement his data logger and provide the necessary computer resources to analyze his data. The process was expensive, both in time and in money. 

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