Computer-aided identification

In the past, PTRC screening was mainly based on gas chromatography-mass spectrometry (GC-MS) [116]. The choice of GC-MS was based on a number of good reasons (separation power of GC, selectivity of detection offered by MS, inherent simplicity of information contained in a mass spectrum, availability of a well established and standardized ionization technique, electron ionization, which allowed the construction of large databases of reference mass spectra, fast and reliable computer aided identification based on library search) that largely counterbalanced the pitfalls of GC separation, i.e., the need to isolate analytes from the aqueous substrate and to derivatize polar compounds [117]. 

The GC is often connected to a mass spectrometer. Mass spectrometry (MS) breaks samples apart and separates the ionized fragments by mass and charge. Vast libraries of comparison fragments make computer-aided identification of materials possible even when the sample is very small. Most forensic laboratories have access to a combined gas chromatograph/mass spectrometer (GC/MS). High pressure liquid chromatography (HPLC) separates many types of drugs and may also be combined with MS. 

There are two general approaches for computer-aided identification of infrared spectra of unknown compounds [173,196-199,248-250]. The most common approach uses software designed to identify an unknown spectrum by its similarity to a limited number of reference spectra selected from a general or customized library of reference spectra measured under similar conditions (e.g. vapor phase, solid phase, etc.) Commercial... 

Johnson, R. 1979. Computer-aided identification. FDA (Food and Drug Administration) By-Lines. 9, 235-250. 

Computer-Aided Identification of Benchmark Criticelity Data, L. Koponen(LLNL)... 

During the last years, a number of articles have been published by Casanova and coworkers (e.g., Bradesi et al. (1996) and references cited therein). In addition, papers dealing with computer-aided identification of individual components of essential oils after C-NMR measurements (e.g., Tomi et al., 1995), and investigations of chiral oil constituents by means of a chiral lanthanide shift reagent by carbon-13 NMR spectroscopy have been published (Ristorcelli et al., 1997). 

Ionov, I., Kapitanova, D., and Blajev, K. (1995). Computer-aided identification of TLC-separated pesticides. Adv. Forensic Sci., Proc. Meet. Int. Assoc. Forensic ScL, 13th, 5 114-117. 

Siek, T. J., Stradling, C. W., McCain, M. W., and Mehary, T. (1997). Computer-aided identification of thin layer chromatography patterns in broad-spectrum drug screening. Clin. Chem. (Washington, D.C.) 43 619-626. 

Computer-aided identification keys, including Internet-based keys. 

Edwards, M. and Morse, D.R. (1995) The potential for computer-aided identification in biodiversity research. Trends in Ecology and Evolution, 10 153-158. 

WUlcox, W.R. and Lapage, S.P. (1975) Methods used in a program for computer-aided identification of bacteria. In Biological Identification with Computers (ed R.J. Pankhurst), Academic Press, London, pp. 103-119. 

Industrial scale polymer forming operations are usually based on the combination of various types of individual processes. Therefore in the computer-aided design of these operations a section-by-section approach can be adopted, in which each section of a larger process is modelled separately. An important requirement in this approach is the imposition of realistic boundary conditions at the limits of the sub-sections of a complicated process. The division of a complex operation into simpler sections should therefore be based on a systematic procedure that can provide the necessary boundary conditions at the limits of its sub-processes. A rational method for the identification of the subprocesses of common types of polymer forming operations is described by Tadmor and Gogos (1979). 

Patel Y, Gillet VJ, Bravi G, Leach AR. A comparison of the pharmacophore identification programs Catalyst, DISCO and GASP. J Comput Aided Mol Des 2002 16(8-9) 653-81. 

Filikov AV, Mohan V, Vickers TA, Griffey RH, Cook PD, Abagyan RA, James TL. Identification of ligands for RNA targets via structure-based virtual screening HIV-1 TAR. / Comput Aided Mol Design 2000 14 593-610. 

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. 

Clerc, T., and Erni, F. Identification of Organic Compounds by Computer-Aided Interpretation of Spectra. 39, 91-107 (1973). 

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. 

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