Relationship between heavy metal distribution in sediment samples and their ecotoxicity by the use of the Hasse diagram technique

Many studies assessing the quality of sediments and their pollution impact use monitoring data consisting predominantly of chemical indicators. Recently, ecotoxicity estimates have been used as very important parameters of the ecological state of sediment samples. Thus, a more complete sediment risk assessment is achieved and more reliable information on the sediment pollution history is extracted. The data interpretation could be improved if multivariate statistical techniques were applied to data classification, modelling and interpretation. The starting classification of the data was performed using self-organizing maps (SOM) approach in order to reveal specific relationship patterns for objects and for variables. The original element of the present study is the use of the Hasse diagram technique (HDT) for partial ordering in order to explain some specific relations between the chemical indicators analysed (heavy metal content in different sediment compartments) and the ecotoxicity tests for acute and chronic toxicity. In principle, a reliable estimate of the pollution impact of a large environmental object (the Mar Menor lagoon in Spain) is achieved. The specific role of each one of the five heavy metals involved (Zn, Cu, Mn, Pb, and Cd) is interpreted in the context of the additional ecotoxicity tests.     

Air-quality assessment of Pico-mountain environment (Azores) by using chemometric and trajectory analyses

This article illustrates the use of chemometrics in the interpretation of aerosol data collected by a seven-wavelength aethalometer at the PICO-NARE observatory, in Pico island, Azores, Portugal. Samples were assessed through k ₀-standardized, instrumental neutron activation analysis (k ₀-INAA), and concentrations of up to 20 airborne elements were determined. The chemometric analysis by self-organizing maps (SOM) tried to identify groups of similarity for sampling events and chemical tracers, discriminating in this way each group of similarity thus obtained. Additionally, synoptic back trajectories for each of the sampling days distributed into four clusters were calculated, in order to associate the classified groups with possible pollution sources.     

Localization maps by orbital partitioning of the electron density

Summary We define a localization measure for one-determinantal wave-functions based on the partitioning of the total electron density to orbital contributions. The set of occupied orbitals is the more localized the fewer terms are necessary to describe the total density. This measure varies from point to point in space which leads to characteristic localization maps for molecules.Supported in part by the grant Hungarian Research Fund OTKA 517/1990     

Raman spectrometry and neural networks for the classification of wood types. 2. Kohonen self-organizing maps

One- and two-dimensional Kohonen self-organizing maps (SOMs) were successfully used for the unsupervised differentiation of the Fourier transform Raman spectra of hardwoods from softwoods. The SOMs were also applied to differentiate temperate woods from tropical woods, and results showed that the two types of woods could only be partly differentiated. A semi-quantitative method that is based on the Euclidean distances of the weight matrix has been developed to assist the automatic clustering of the neurons in a two-dimensional SOM.     

Comparison of potential energy maps and molecular shape invariance maps for two-dimensional conformational problems

Summary It is well known that many molecular properties are strongly dependent on internal nuclear arrangements. Two possible, independent approaches can be followed while studying changes in molecular characteristics as functions of the nuclear geometry. These are the analysis of potential energy surfaces and the analysis of molecular shape. In this work, we seek to establish relationships between potential energy maps and shape invariance region maps, where each point of these maps represents a nuclear configuration. The study is performed by analyzing the occurrence and lack of certain symmetries in both types of maps. As illustrative examples, we consider the three structural isomers of the dihydroxybenzene molecule. Potential energy is computed at the STO-3G ab initio level, while the shape is described by the shape group method as applied to fused-sphere van der Waals surfaces. It is shown that the symmetry of the shape invariance maps follows closely, but not exactly, the symmetry of potential energy surfaces. The molecular surface is thus blind to some small changes of the potential (a feature to be expected to hold also for molecular surfaces defined in terms of electronic charge density). Our findings suggest that a crude fused-sphere model may suffice to describe some of the structural changes in molecular surfaces, as well as their relationships to the electronic energy.     

Closure of analytical chemical data and multivariate classification

When it is not possible to analyze an exactly reproducible amount of sample (or whenever samples contain indefinite amounts of extraneous materials) it is customary to normalize the data by making, for example, the sum of the concentrations obtained for each sample equal to 100. Although the data normalized (or ;closed') in such a manner have been criticized, it is empirically shown that closure is appropriate in order to compare and classify samples of the type indicated above.     

Application of chemometrics to the classification of orange essence oil varieties by GLC

Orange essence oils of the Navel and Valencia variety were analyzed by GC and GC/MS using two parallel capillary columns of different selectivity. Data were normalized, converted into a computer compatible format, and transferred via an asynchronous communications interface to a mainframe computer for statistical treatment, using the chemometrics software package ARTHUR. After suitable preprocessing of the raw data, reliable classification was possible. It appears that four substances which are present in both varieties can be used for that purpose.     

Underwater sediment analyses by laser induced breakdown spectroscopy and calibration procedure for fluctuating plasma parameters

Laser Induced Breakdown Spectroscopy (LIBS) was applied on sediments directly under water. The aim of the research was to develop a method for measuring the sediment elemental composition, including minor elements, which could be implemented in-situ. The plasma was generated by a double-pulse, Q-Switched Nd:YAG laser operated at 1064 nm. For signal detection, both ICCD and non-gated, compact detectors were used. The major difficulties in underwater sediment analyses are related to the natural and laser induced surface roughness, and to the sample softness. The latter is responsible for the formation of particle clouds above the surface, which scatter both the laser and plasma radiation, and often results in breakdown formation above the analyzed surface. In such cases, a broad sonoluminescence emission from water, formed during the gas bubble collapse was sometimes registered. Under optimized experimental conditions, even by using a non-gated detector and single shot acquisition, it was possible to detect several minor sediment constituents, such as titanium, barium, manganese and others. A crude estimation of the Limit of Detection (LODs) for these elements was performed by underwater measurements on certified soils/sediments. Due to strong shot-to-shot fluctuations in the plasma temperature, well correlated calibration curves, aimed for quantitative analyses, could only be obtained after applying an appropriate data processing procedure. The latter selects automatically only the spectra characterized by similar plasma parameters, which are related to their continuum spectral distribution. Application of such a procedure improves the measurement accuracy also in other surroundings and on samples different from the ones analyzed here.     

N-way modelling of sediment monitoring data from Mar Menor lagoon, Spain

The present paper deals with the application of Tucker3 modelling to a sediment monitoring data set from the area of Mar Menor coastal lagoon (Spain). The aim of the study is to model and interpret the fractionation of heavy metals in the suspended particulate matter and sediment fractions resulting by sedimentation processes. Since the lagoon is seriously influenced by anthropogenic activities the modelling aims an assessment of the environmental hazard, too. After application of various scaling and centering procedures and estimation of the model dimensionality, an optimal (3, 3, 3) Tucker3 model was chosen for data interpretation. Using the model output (factor loadings connected to the four main core elements) it could be concluded that the heavy metal concentrations in the suspended particulate matter and sediment fractions increase in order Cu > Mn > Zn ≈ Pb > Cd and could be examined as estimation of basic levels for all heavy metals caused by different sedimentation processes. The second important core element summarizes the anthropogenic influence of the mining activity in the region. The third important core element shows the different mobility of the heavy metals. The fourth important core element should be related to the specific sediment formation at one of the sampling location.     

Geometrical bounding of data space and nonlinear classification of chemical data using MPGA algorithm

This paper develops a multi-parturition genetic algorithm (MPGA) to be used in geometrical bounding of the overlapped clusters in a data set for the classification of chemical data. Two new operators have been introduced to modify the conventional genetic algorithm, namely, multi-parturition and decimation and orientated creation to improve the linear classification results and diminish the computational time. To circumvent the difficulty commonly encountered in the treatment of linearly inseparable chemical data sets, the optimized linear classifier is further modified to provide a complementary nonlinear classifier. For this reason the space regions of the overlapped clusters have been bounded by erection of half-hyperellipsoids over the linearly misclassified patterns. The proposed MPGA was applied to classify a number of chemical and other data sets with a dimension from 4 to 14. Experimental results have indicated that the proposed MPGA could classify seriously overlapped data sets with an acceptable error rate.     

Identification of the regulatory proteins in human pancreatic cancers treated with Trichostatin A by 2D-PAGE maps and multivariate statistical analysis

In this paper, principal component analysis (PCA) is applied to a spot quantity dataset comprising 435 spots detected in 18 samples belonging to two different cell lines (Paca44 and T3M4) of control (untreated) and drug-treated pancreatic ductal carcinoma cells. The aim of the study was the identification of the differences occurring between the proteomic patterns of the two investigated cell lines and the evaluation of the effect of the drug Trichostatin A on the protein content of the cells. PCA turned out to be a successful tool for the identification of the classes of samples present in the dataset. Moreover, the loadings analysis allowed the identification of the differentially expressed spots, which characterise each group of samples. The treatment of both the cell lines with Trichostatin A therefore showed an appreciable effect on the proteomic pattern of the treated samples. Identification of some of the most relevant spots was also performed by mass spectrometry.     

Two-dimensional linear discriminant analysis for classification of three-way chemical data

The two-dimensional linear discriminant analysis (2D-LDA) algorithm was originally proposed in the context of face image processing for the extraction of features with maximal discriminant power. However, despite its promising performance in image processing tasks, the 2D-LDA algorithm has not yet been used in applications involving chemical data. The present paper bridges this gap by investigating the use of 2D-LDA in classification problems involving three-way spectral data. The investigation was concerned with simulated data, as well as real-life data sets involving the classification of dry-cured Parma ham according to ageing by surface autofluorescence spectrometry and the classification of edible vegetable oils according to feedstock using total synchronous fluorescence spectrometry. The results were compared with those obtained by using the spectral data with no feature extraction, U-PLS-DA (Partial Least Squares Discriminant Analysis applied to the unfolded data), and LDA employing TUCKER-3 or PARAFAC scores. In the simulated data set, all methods yielded a correct classification rate of 100%. However, in the Parma ham and vegetable oil data sets, better classification rates were obtained by using 2D-LDA (86% and 100%), compared with no feature extraction (76% and 77%), U-PLS-DA (81% and 92%), PARAFAC-LDA (76% and 86%) and TUCKER3-LDA (86% and 93%).     

Variable alignment of high resolution data by cluster analysis

High resolution time-of-flight secondary ion mass spectrometry (HR TOF-SIMS) is a powerful surface analytical method. For complex samples, this technique may yield intricate spectra that are difficult to interpret visually. Chemometric methods are useful for data analysis. However, these methods require that spectra are represented in a matrix format. Variances in mass measurements caused by calibration or instrumental effects may present difficulties in properly aligning mass spectral peaks into the correct columns of the data matrix. Cluster analysis of resolution elements is proposed as an alternative approach to construct the data matrix. An automated method for optimizing the data alignment is presented and evaluated for standard steel samples.     

Simulation of chemical metabolism for fate and hazard assessment. IV. Computer-based derivation of metabolic simulators from documented metabolism maps

Computer simulation of xenobiotic metabolism and degradation is usually performed proceeding from a set of expert-developed rules modelling the actual enzyme-driven chemical reactions. With the accumulation of extensive metabolic pathway data, the analysis required to derive such chemical reaction patterns has become more objective, but also more convoluted and demanding. Herein we report on our computer-based approach for the analysis of metabolic maps, leading to the construction of reaction rules statistically suitable for simulation purposes. It is based on the set of so-called bare transformations which encompass all unique reaction patterns as obtained by a heuristically enhanced maximum common subgraph algorithm. The bare transformations guarantee that no existing metabolite is missed in simulation at the expense of an enormous amount of false positive predictions. They are rendered more selective by correlating the generated true and false positives to the locations of typical chemical functional groups in the potential reactants. The approach and its results are illustrated for a metabolic map collection of 15 cycloalkanes.     

Improvement of extraction recovery for the monobutyltin species from sediment

An improvement of the extraction recovery of the MBT species from sediment has been achieved by using a more polar solvent, toluene, and tropolone as chelating agent. A recovery of 84.7 ± 6.3% from spiked sediment has been achieved, which is the highest recovery of MBT species from sediment so far reported using solvent extraction techniques. The relative detection limit for organotin compounds in general in sediment (2 g) is 0.5 ng g⁻¹ when gas chromatography with atomic emission spectrometric detection is used for the analysis. Relative standard deviations (%) for recovery of MBT, DBT and TBT in spiked sediment range from 2.5 to 6.3% (at 0.5 μg g⁻¹ level). The extraction solution also recovers satisfactorily 7 other commonly used organotin compounds. Analyses of selected environmental samples and the Reference Sediment PACS-1 are given.     

An emphatic orthogonal signal correction-support vector machine method for the classification of tissue sections of endometrial carcinoma by near infrared spectroscopy

A new application of emphatic orthogonal signal correction (EOSC) for baseline correction of near infrared spectra from reflectance measurements of tissue sections is introduced. EOSC was evaluated and compared with principal component orthogonal signal correction (PC-OSC) by using support vector machine (SVM) classifiers. In addition, some exemplary synthetic data sets were created to characterize EOSC coupled to SVM for classification. Orthogonal experimental design coupled with analysis of variance (ANOVA) was used to determine the significant parameters for optimization, which were the OSC method and number of components for the model. EOSC combined with the SVM gave better predictions with respect to a larger number of components and was not as susceptible to overfitting the data as the classifier built with PC-OSC data. These results were supported by simulations using synthetic data sets. EOSC is a softer signal correction approach that retains more signal variance which was exploited by the SVM. Classification rates of 93 ± 1% were obtained without orthogonal signal correction with the SVM. PC-OSC and EOSC data gave similar peak prediction accuracies of 94 ± 1%. The key advantages demonstrated by EOSC were its resistance to overfitting, fine-tuning capability or softness, and the retention of spectral features after signal correction.     

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates.     

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates.