Counter-propagation neural networks are used to model and predict activities of carboquinones and of benzodiazepines from physicochemical parameters. For carboquinones, networks with one hidden layer processing element (PE) for each compound achieved significantly better training set RMSE values than corresponding back-propagation and multiregression results and test set RMSE values as good or slightly worse than back-propagation. Test set results improved by 10-15% using networks with fewer hidden layer PEs than carboquinones; the smallest test set RMSE values are between 0 and 10% better than back-propagation values, about 1.3 times greater than corresponding training set values, and occur when there are about as many competitive layer PEs as there are compounds in the data set. Training set RMSE values increase with decreasing number of competitive layer PEs and approach those of test sets. Both counter-propagation and back-propagation networks, however, have worse predictive capability than multiregression. For benzodiazepines, networks with one hidden layer PE for each compound achieved significantly better training set RMSE values than back-propagation and multiregression results and test set RMSE values slightly worse than back-propagation. Test set results improved by 10-15% using fewer hidden layer PEs than benzodiazepines; the smallest test set RMSE values are 0-10% better than back-propagation values, about 1.3 times greater than training set values, and occur when there are about half as many competitive layer PEs as there are compounds in the data set. Training set RMSE values increase with decreasing number of competitive layer PEs and approach those of test sets. Counter-propagation, back-propagation, and multiregression all have similar predictive capabilities.

Model compound testing was conducted in a batch reactor to evaluate the effects of trace contaminant components on catalytic hydrogenation of sugars. Trace components are potential catalyst poisons when processing biomass feedstocks to value-added chemical products. Trace components include inorganic elements such as alkali metals and alkaline earths, phosphorus, sulfur, aluminum, silicon, chloride, or transition metals. Protein components in biomass feedstocks can lead to formation of peptide fractions (from hydro-lysis) or ammonium ions (from more severe breakdown), both of which might interfere with catalysis. The batch reactor tests were performed in a 300-mL stirred autoclave, with multiple liquid samples withdrawn over the period of the experiment. Evaluation of these test results suggests that most of the catalyst inhibition is related to nitrogen-containing components.

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= 5%). With the chromatographic system used in this report, an increased resolution for phosphatidylglycerol and other phospholipids is achieved and permits a better accuracy in the quantitative analysis. In addition, the separation of phosphatidylinositol molecular species gives a new chromatographic profile of the lung surfactant.An automated version of the switching system will allow the elution of all phospholipid classes by heart cut techniques with different mobile phases. It could be suitable for a routine analysis of phospholipids especially to investigate fetal pulmonary maturity in the amniotic fluid.ACKNOWLEDGMENT We are indebted to Martin Czok and Emmanuelle Brialix for helpful discussions and technical assistance.

INTRODUCTIONChemists are being confronted with a confounding array of data at an ever increasing pace. The advent of new experimental techniques, the development of cheaper, faster, and more precise instrumentation, and the availability of desktop computing power that only a decade ago would have filled a small house have all contributed to this situation. Medicinal chemists using combinatorial chemistry methods have generated huge libraries of chemical compounds that must be assessed for pharmaceutical activity. Spectroscopists search and analyze huge databases of spectra. Computational chemists generate vast numbers of points describing potential energy surfaces in n-dimensional spaces.The issue facing chemists today is not how to generate data, which not so long ago was actually quite difficult and time-consuming, but how to extract useful information from the data generated. As a consequence, a branch of chemistry known as chemometrics began to evolve in the late 1960s.l Chemometrics is broadly concerned with the extraction of useful information from chemical data. Until about 1990, chemometrics primarily involved appli-