02) Gouveia, M. A.; Seabra e Barros, J.; Tavares, M. J.; Freitas, M. C. J (G4) Routh, M. W. Spectroscopy 1987, 2, 45-52. (G5) Ottaway, J. M.; Littlejohn, D.; Marshall, J. Fortschr. Atomspektrom. (G6) Matuslewlcz, H. Chem. Inz. Chem. 1986, 76, 75-90. (G7) Willoughby, E. J. Commun. Sol Scl. Plant Anal. 1986. 77, 667-677. (G8) Sanz-Medel, A.; Rodriguez Roza, R.; Gonzalez Alonso, R.; Nova1 Valli-(G9) Kratochvil, B.; Motkosky, N.; Duke, M. J. M.: Ng., D. Can. J . Chem.the detailed discussion of computers is somewhat beyond the scone of the review as defined above. a decision was made not Steven D. Brown received his 0.5. degree in chemistry in 1972 and an M.S. in phyaC EaUnwgsnlC ChBmisbY in 1975. both from Pwtland-State Univer&, He obtained the m.D. degree in anawical chemistry from the University 01 Washlngton in 1978 working under the direction 01 Bruce KOwaiSkl. In 1978, he was appointed to the chemistry facuity at the University of California. where he held B joint appointment at the Lawrence m e l e y National Laboratory. He moved to 4 1 i Washington Slate Univer&in 1981. where he war Associate Prolesscr, prior to Ialning the chemistry faculty et the Unlverrny of Delaware In 1986. He is cunentiv AI SOCI --. --ate Professor 01 Chemistry and Operations Research. He I S also Associate Editor Of The Journal Of ChemomsWcs and serves on !he editorial boards of AnaWica Chimica Acta and Anaiyiicai Lstfers . His research interests encompass many aspects of the application Of Computers to chemical problems, including the areas Of real-time analysis, shoat ~rocesGinri. oo!imiratlan. mockling. and process control Todd 0. Barker obtained hi8 B S degree in Chemistry from the University of South Dakota in 1981 Afler working for the South Dakota Geoloaical Survev. he returned to the unker& 01 so&~Dakota. earning an M.A. in analytical chemistry in 1984. He began W.D. studies at Washington state universitv in 1984 and moved to the UniversW of &!aware in 1986 His re-, r L k search ihlere5ts include the development of Kalman finer* for reawme analysis and control and modeling 01 kinetics in flow in]ection anahsls. R a d J. LarlrN earned a B.A. deoree in chemistry from R i d e Island Coiiege in 1978. He began FhD. studies at Washington State Univerrltv ami moved to the LJniV-~~~~~ . ~. . ... ersity 01 Delaware in 1986. His research interests concern development of factor analysis methods fw investigBtion of metal speciation. S1.plrm L. Modre obtained a 0.5. degree in chembtry and computer Science from the University 01 Wisconsin-La Crasse in 1984. That year he moved to Washington State University. where he began Ph.D. studies in Chemistry. He moved lo Oehwars in 1986. where he Is now a graduate research assistant His research interests lie In dlgltal Riierlng. fuzzy matching. and rodeling. Hadan R. Wllk graduated trom Norm Carol!na State University wkh a 0.5. in chemical enginewing in 1984. After beginning his h.0. studies at Washlnglon State University. he moved I O the University Of Delaware h 19%. l i s research Werests lie in th...
A receptrode biosensor is presented that uses intact chemoreceptor-based molecular recognition from antennular structures of the Hawaiian swimming crab species Portunis sanguinolentus. The sensor is coupled to a learning, pattern recognition calculation for performing analytical chemistry. Action potential waveforms are used to establish the identity of individual action potential types that can be associated to particular analytes. The pattern recognition calculations used are referred to as cluster analysis (CA) and principal component analysis (PCA). Action potential similarities are determined by using a dendrogram plot of the cluster analysis results and further elucidated by using principal component scores plots. Quantitative analysis was performed after classification of analyte and background responses. Chemoresponses to salinity and trimethylamine N-oxide, two chemical constituents that are found in the crustacean living environment, were investigated and gave analytic responses over several orders of magnitude.
Multicomponent Analysis as a Routine Analytical ToolMany samples require rapid quantitative analysis of several components without taking the time required for separation of the components either from each other, or from the sample matrix. This need is especially critical for samples whose existence in some detection zone is transient, either because of some chemical reaction or because of sample transport rates through the analysis manifold. Mathematical analysis of data collected on such systems offers a practical route to the enhancement of detector selectivity without significant degradation of detection limits. A Brief Review of Digital FiltersA set of techniques which is particularly suited to use in the analysis of transient responses involves the use of digital filters based on the Kalman algorithm [1]. The Kalman filter is a recursive, digital filter which uses models for the measurement and optimal estimates of the system states as well as errors associated with those estimates. In essence, these filters make use of empirical models of analyte responses, as well as any available theory, to enable the resolution of overlapped responses from the chemical components of a mixture. The recursive nature of these filters makes them attractive for "on-line" analysis, because they can process data point-by-point, using only points with high information content, and because concentration estimates can be developed in "real time." There have already been a number of applications in analyticalBecause these filters are not demanding of memory, and because the simpler forms do not require extensive matrix manipulations, they are easy to implement on a small, low-cost computer. More complex filters can be developed to detect, and even to correct, errors in the chemical models used for the multicomponent analysis. These filters, as well as other "quality assurance" software, can be run as hierarchical filters, with the simple filters acting as intelligent data collectors, and the more complex large filters monitoring the performance of the multicomponent analysis accomplished by the simpler filters. These methods are also easily merged with other chemometric methods, such as factor analysis, to permit more complex, off-line calculations as necessary to provide for improved calibration models, or to model new chemical systems planned for routine study. Three Applications of Digital Filters in Multicomponent AnalysisThis paper reports some applications of three new Kalman filters. The first application demonstrates the feasibility of filter-based multicomponent analysis in the Fourier domain, or in fact any other domain reached by a linear transformation from the time domain. Multicomponent analysis in these domains has a number of advantages for realtime analysis: the nature of model errors is changed, and "on-line" FTIR-based multicomponent analysis is made feasible. Figure I illustrates filtering of FTIR data in the Fourier domain.The second application involves the development of new adaptive filters for detection a...
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