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Biomedical subjects

B K Lavine

Publications and source records attributed to B K Lavine.

6 recordsLinked to original sources
PubMed2001

Fuel spill identification using solid-phase extraction and solid-phase microextraction. 1. Aviation turbine fuels.

The water-soluble fraction of aviation jet fuels is examined using solid-phase extraction and solid-phase microextraction. Gas chromatographic profiles of solid-phase extracts and solid-phase microextracts of the water-soluble fraction of kerosene- and nonkerosene-based jet fuels reveal that each jet fuel possesses a unique profile. Pattern recognition analysis reveals fingerprint patterns within the data characteristic of fuel type. By using a novel genetic algorithm (GA) that emulates human pattern recognition through machine learning, it is possible to identify features characteristic of the chromatographic profile of each fuel class. The pattern recognition GA identifies a set of features that optimize the separation of the fuel classes in a plot of the two largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected features is primarily about the differences between the fuel classes.

Journal Article↗
PubMed2000

Source identification of underground fuel spills by solid-phase microextraction/high-resolution gas chromatography/genetic algorithms.

Solid-phase microextraction (SPME), capillary column gas chromatography, and pattern recognition methods were used to develop a potential method for typing jet fuels so a spill sample in the environment can be traced to its source. The test data consisted of gas chromatograms from 180 neat jet fuel samples representing common aviation turbine fuels found in the United States (JP-4, Jet-A, JP-7, JPTS, JP-5, JP-8). SPME sampling of the fuel's headspace afforded well-resolved reproducible profiles, which were standardized using special peak-matching software. The peak-matching procedure yielded 84 standardized retention time windows, though not all peaks were present in all gas chromatograms. A genetic algorithm (GA) was employed to identify features (in the standardized chromatograms of the neat jet fuels) suitable for pattern recognition analysis. The GA selected peaks, whose two largest principal components showed clustering of the chromatograms on the basis of fuel type. The principal component analysis routine in the fitness function of the GA acted as an information filter, significantly reducing the size of the search space, since it restricted the search to feature subsets whose variance is primarily about differences between the various fuel types in the training set. In addition, the GA focused on those classes and/or samples that were difficult to classify as it trained using a form of boosting. Samples that consistently classify correctly were not as heavily weighted as samples that were difficult to classify. Over time, the GA learned its optimal parameters in a manner similar to a perceptron. The pattern recognition GA integrated aspects of strong and weak learning to yield a "smart" one-pass procedure for feature selection.

Algorithms↗
PubMed1993

Survey of the Anopheles maculatus complex (Diptera: Culicidae) in peninsular Malaysia by analysis of cuticular lipids.

Anopheles maculatus Theobald sensu lato is a species complex now consisting of eight sibling species; An. maculatus is still represented by two cytologically distinct forms; i.e., the widely distributed sensu strictu or B, and E from southern Thailand and adjacent areas in northern Malaysia. Cuticular lipid profiles in conjunction with principal component analysis was used to separate An. maculatus form E from sensu stricto form B in a preliminary survey of the An. maculatus complex at five locations spanning peninsular Malaysia. The relative rank orders, from the areas of the five gas chromatographic peaks used to determine lipid differences for specimens from peninsular Malaysia, matched well with those from cytogenetically identified colony specimens of An. maculatus forms B and E. The two-dimensional principal component pattern of specimens identified as form E was highly clumped, which indicated that very similar cuticular lipids were present within this putative malaria vector. Both forms coexisted in peninsular Malaysia, but form E may be dominant except in the south.

Animals↗
PubMed1990

Cuticular lipid differences between the malaria vector and non-vector forms of the Anopheles maculatus complex.

Two chromosomal forms (E and F) of the Anopheles maculatus Theobald complex were distinguished by gas-liquid chromatographic (GC) analysis of cuticular lipids in association with a multivariate principal component analysis. The GC chromatogram obtained from n-hexane extracts of individual specimens showed no consistent qualitative differences in normalized peak areas between forms. Of the seventeen consistent peaks, five were found to be quantitatively different between forms at a high (99.5-99.95%) level of statistical confidence. Relative ratios of these five quantitatively different GC peaks were used as criteria to distinguish single specimens as either form E or form F. Chemical structures of the five GC peaks were assigned by both electron impact and chemical ionization gas chromatography/mass spectrometry analysis. The first three peaks, which were always doublets, were partially resolved saturated and mono-unsaturated free fatty acids; the other two peaks were n-alkanes. Principal component analysis substantiated that the vector form E has very similar cuticular lipid profiles and is well separated from the non-vector form F.

Animals↗
PubMed1987

Electronic factors and acridine frameshift mutagenicity--a pattern recognition study.

Using the ADAPT and CHEMLAB-II systems for structure-activity analysis, computer-calculated electronic properties of molecules were used to derive structure-activity relationships for predicting the mutagenicity of a set of substituted acridines in strain TA1537 of the Ames Salmonella assay. A collection of 40 acridines, with a variety of substituents, was examined. A set of 4 electronic descriptors was found which could be used to correctly classify all but two of the compounds as mutagenic or nonmutagenic. A negative correlation was found between the sum of the Hammett aromatic substituent parameters and the level of mutagenicity of the structures, expressed as log(number of revertants/plate + 1) at a 20-micrograms dose. This correlation, however, was not high enough to allow precise estimation of the mutagenicity values.

Acridines↗