System computer-aided identification

The correct interpretation of measured process data is essential for the satisfactory execution of many computer-aided, intelligent decision support systems that modern processing plants require. In supervisory control, detection and diagnosis of faults, adaptive control, product quality control, and recovery from large operational deviations, determining the mapping from process trends to operational conditions is the pivotal task. Plant operators skilled in the extraction of real-time patterns of process data and the identification of distinguishing features in process trends, can form a mental model on the operational status and its anticipated evolution in time. 

Computer-aided trend identification offers potential benefits, but is dependent on the quality of the input information. Expert systems and artificial intelligence are tools being tested. When successful, they may give improved insight into identifying common causes and trend analyses. 

Ames BN, Durston WE, Yamasaki E, Lee FD (1973) Carcinogens are mutagens a simple test system combining liver homogenates for activation and bacteria for detection. Proc Natl Acad Sci USA 70 2281-2285 Cunningham AR, Klopman G, Rosenkranz HS (1998) Identification of structural features and associated mechanisms of action for carcinogens in rats. Mutat Res 405 9-27 Dearden JC (2003) In silico prediction of drug toxicity. J Comput Aided Mol Des 17 119-127... 

If the mappings T in the sensor equation (6.75) and the actuator equation (6.76) are purely hysteretic they can be modeled by a Prandtl-Ishhnskii operator H, a modified Prandtl-Ishlinskii operator M or a Preisach hysteresis operator R depending on the degree of symmetry of the branching behaviour. The calculation of these hysteresis operators and the corresponding compensators from the measured output-input characteristic requires special computer-aided synthesis procedures which is based on system identification methods. Due to a lack of space, this article cannot further comment on these synthesis methods. However, a detailed description of both the synthesis method and the mathematical basics can be found in the literature [332,341,350-352,356]. 

One of the most serious limitations in the application of these powerful GC/MS and LC/MS systems is the accurate and efficient identification of this flood of unknown mass spectra. A variety of computer-assisted techniques have been proposed (2, 3, 4), which can be classified generally as "retrieval" or "interpretive programs (2). The former matches the unknown mass spectrum against a data base of reference spectra the ultimate limitation of this approach is the size of the data base, which currently contains the mass spectra of 33,000 different compounds (j>, 6), less than 1% of the number listed by Chemical Abstracts. If a satisfactorily-matching reference spectrum cannot be found by the retrieval program, an interpretive algorithm can be used to obtain partial or complete structure information, or to aid the human interpreter in this task (7-10). 

The variations in human metabolic profiles can seldom permit visual observations of meaningful metabolic deviations from the normal. However, large computer systems do have the general capability to extract the distinct features from large data sets, and reduce the bulk of data from capillary GC of numerous patients to a more easily understandable form. Precisely measured retention characteristics and the peak areas form the basis for such comparisons. Pattern recognition methods have been utilized to classify diabetic samples [169,170] and those of virus-infected patients [171] with the aid of training sets from clinically defined cases. In addition, the feature extraction approach [169,170] permits identification of important metabolite peaks in complex chromatograms. 

The main analytical instrumentation in the vehicle is a benchtop GC/MS by Hewlett Packard, HP 5890 GC/5970 MSB (mass selective detector). A basic configuration for analyzing liquid samples includes a heated injection port and a capillary fused-silica column interfaced directly to the MS via a heated transfer line. The MS is a quadrupole design operated under vacuum provided by a diffusion pump and backed by a mechanical rotary pump. System operation and data analysis are performed by a Pentium-level personal computer loaded with proprietary software and a NBS spectral library to aid with identification of unknowns. Depending on the mission, a simpler installation may consist of a GC equipped with an appropriate detector such as the electron capture detector (ECD). 

In those cases where a laboratory is routinely handling natural products that have been seen before in the same laboratory, a self-constructed library of mass spectra is a valuable aid to identification. Such a system is particularly useful in the GC/MS analysis of body fluids (in biomedical research and clinical diagnosis) and in the perfume and flavor industries e.g. terpene identification). Metabolic profiling by GC/MS 48) of the components of urine or serum provides an instance where computer techniques are a necessity and have become quite sophisticated. 

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