PATTERN RECOGNITION STUDIES OF COMPLEX CHROMATOGRAPHIC DATA

Chromatographic fingerprinting of complex biological samples is an active research area with a large and growing literature. Multivariate statistical and pattern recognition techniques can be effective methods for the analysis of such complex data. However, the classification of complex samples on the basis of their chromatographic profiles is complicated by two factors: (1) confounding of the desired group information by experimental variables or other systematic variations, and (2) random or chance classification effects with linear discriminants. Several interesting projects involving these effects and methods for dealing with them will be discussed.;Gas chromatography and pattern recognition methods were also used to develop a potential method for differentiating between European and Africanized bees based on chemical constitution. One hundred and nine European and Africanized honeybees were characterized by thirty peak gas chromatographs of cuticular hydrocarbon extracts. Discriminants were developed that correctly classified the bees, and these discriminants were used successfully to classify bees of unknown origin, including hybrids.;Previously, Monte Carlo simulation studies were carried out to assess the probability of chance classification for nonparametric linear discriminant functions. The level of expected chance classification as a function of the number of observations, the dimensionality, and the class membership distributions was examined. These simulation studies establish limits on the approaches that can be taken with real data sets so that chance classifications are improbable.;In one study, pattern recognition analysis of one hundred and forty-four pyrochromatograms (PyGC's) from cultured skin fibroblasts was used to differentiate cystic fibrosis carriers from presumed normal donors. Several experimental variables (donor gender, chromatographic column number, etc.) were observed to contribute to the overall classification process. Notwithstanding these effects, discriminants were developed from the chromatographic peaks that assigned a given PyGC to its respective class (CF carrier versus normal) largely on the basis of the desired pathological difference. In another study gas chromatographic profiles of cuticular hydrocarbon extracts obtained from one hundred seventy-nine red fire ant samples were analyzed using pattern recognition methods. Clustering according to the biological variables of social caste and colony was observed.