Chromatographic Data Sets Research Articles

Multivariate Observations of the Distribution of Polychlorinated Biphenyls in Environmental Compartments of Two Harbours

Abstract The objectives of this study were to identify, classify and categorize polychlorinated biphenyl (PCB) residues in samples collected from the Hamilton and Wheatley Harbour environmental compartments. The study is built around the use of a principal components analysis method, namely the soft independent modelling of class analogy (SIMCA) technique. This multivariate method is widely used for evaluating differences and observing similarities among multiple objects. The results obtained from this work confirm that the gas chromatographic data sets obtained with the samples provide a good approximation of the pattern derived from a determination of the composition of commercial Aroclors in water, sediment and biota samples. The data analysis technique provides insight into the origin of PCB contamination in environmental samples and indicates pathways for the environmental degradation or bioaccumulation of PCBs. This investigation contributes some evidence that multivariate reduction techniques are s...

Pattern recognition studies in chemical communication: Nestmate recognition in Camponotus floridanus

Lavine, B.K., Morel, L., Vander Meer, R.K., Gunderson, R.W., Han, J.H., Bonanno, A. and Stine, A., 1990. Pattern recognition studies in chemical communication: nestmate recognition in Camponotus floridanus. Chemometrics and Intelligent Laboratory Systems, 9: 107–114. The combination of gas chromatography and pattern recognition (GC/PR) analysis is a powerful tool for investigating complicated biological problems. Clustering, mapping, and principal component modelling are necessary to analyze large chromatographic data sets and to seek meaningful relationships between chemical constitution and biological variables. We have applied GC/PR to the problem of deciphering the complex chemical messages of Camponotus floridanus (a carpenter ant) and have learned that GC traces of soaks obtained from the carpenter ants are characteristic of their colony of origin, social caste, and social experience. In this study gas chromatographic data obtained from 119 red carpenter ants was analyzed using principal component analysis and the FCV clustering algorithm.

Estimation of the reversed-phase liquid chromatographic lipophilicity parameter log k′ w using ET-30 solvatochromism

The estimation of the reversed-phase liquid chromatographic lipophilicity parameter log k′ w has been studied using the solvatochromic E T(30) polarity scale. Over 200 sets of chromatographic retention data were used to compare the log k′ w estimates made from the volume percent of organic modifier (%) and E T(30) models of mobile phase strength. It was found that linear extrapolations of log k′ versus E T(30) plots to the polarity of unmodified aqueous mobile phase gave a more reliable value of log k′ w than linear regressions of log k′ versus volume percent. This evaluation was based upon the relative value of the 95% confidence interval about log k′ w, the point of intersection of log k′ versus solvent strength plots for different modifiers, the scatter of estimations with different modifiers and the goodness-of-fit of the data to the linear model. Estimations of log k′ w using the solvatochromic approach are found to give a more sensitive lipophilicity description than the volume percent approach. Recommended procedures for carrying out lipophilicity estimations with reversed-phase liquid chromatography are also discussed.

Topological Indices in Correlation Analysis. Part 2. Molecular Shape Indices in Modelling of the Chromatographic Retention Parameters

AbstractDifferent chromatographic data sets for hydrocarbons, alcohols, aldehydes, ketones, esters and barbiturates picked up from the literature were correlated with the kappa molecular shape indices. All correlations were statistically highly significant. Application of the kappa indices in such modelling is discussed.

Pattern recognition studies of complex chromatographic data sets: Design and analysis of pattern recognition experiments

Chromatographic fingerprinting of complex biological and environmental samples is a 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 the effects are discussed. Complex chromatographic data sets often contain information dependent on experimental variables as well as information which differentiates classes. The existence of these types of complicating relationships is an innate part of fingerprint-type data. ADAPT, an interactive computer software system, has the clustering, mapping, and statistical tools necessary to identify and study these effects in realistically large data sets. In one study, pattern recognition analysis of 144 pyrochromatograms from cultured skin fibroblasts was used to differentiate cystic fibrosis carriers from presumed normal donors. Several experimental variables (door gender, chromatographic column, etc.) were observed to contribute to the overall classification process. Notwithstanding these effects, discriminants were developed from the chromatographic peaks that assigned a given pyrochromatogram to its respective class (cystic fibrosis carrier versus normal) largely on the basis of the desired pathological difference. In another study gas chromatographic profiles of cuticular hydrocarbon extracts obtained from 170 red fire at samples were analyzed using pattern recognition methods. Clustering according to the biological variables of social caste and colony was observed. Previously, Monte-Carlo simulation studies have been carried out to assess the probability of chance classification for nonparametric linear discriminants. The level of expected chance classification as a function of the number of observations, the dimensionality, class membership distribution, and covariance structure of the data were examined. These simulation studies established limits on the approaches that can be taken with real data sets so that chance classifications are improbable.

Physical model for gas-solid chromatography with volatile and nonvolatile modifiers

The two-dimensional (adsorption) version of the scaled particle solution theory has been used to interpret the single-component isotherms of propane, butane, and acetone on a graphitized carbon black adsorbent over a range of temperatures. In addition, the multicomponent adsorption model has been used to develop an equation to describe the retention volume of an infinite dilution solute as a function of surface coverage by a volatile modifier as well as to interpret the experimental results from previous investigations of such quasi-binary chromatographic systems. Several sets of binary chromatographic data in the form of the retention volume of one solute as a function of the surface coverage by a different solute were regressed to the proposed retention volume equation. The adsorption coefficients of each solute obtained from the binary isotherm data (propane + butane and acetone + butane) agreed with those determined from the single-component isotherms as well as those calculated from specific retention volumes of the infinite dilution solutes with no modifier present. The retention volume equation provides the first quantitative description for the retention of one solute as a function of the surface coverage by a second component.

Pattern Recognition Studies of Complex Chromatographic Data Sets.

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 analyisis 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. We will treat several current projects involving these effects and methods for dealing with the effects. Complex chromatographic data sets often contain information dependent on experimental variables as well as information which differentiates between classes. The existence of these types of complicating relationships is an innate part of fingerprint-type data. ADAPT, an interactive computer software system, has the clustering, mapping, and statistical tools necessary to identify and study these effects in realistically large data sets. In one study, pattern recognition analysis of 144 pyrochromatograms (PyGCs) 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 involved in relationships that had to be separated from the sought relationships. Notwithstanding these effects, discriminants were developed from the chromatographic peaks that assigned a given PyGC to its respective class (CF carrier vs normal) largely on the basis of the desired pathological difference. In another study, gas chromatographic profiles of cuticular hydrocarbon extracts obtained from 179 fire ants were analyzed using pattern recognition methods to seek relations with social caste and colony. Confounding relationships were studied by logistic regression. The data analysis techniques used in these two example studies will be presented. Previously, Monte Carlo simulation studies were carried out to assess the probability of chance classification for nonparametric and parametric linear discriminants. The level of expected chance classification as a function of the number of observations, the dimensionality, and the class membership distributions were examined. These simulation studies established limits on the approaches that can be taken with real data sets so that chance classifications are improbable.