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PubMed2000

Assessment of metal pollution based on multivariate statistical modeling of 'hot spot' sediments from the Black Sea.

The paper deals with application of different statistical methods like cluster and principal components analysis (PCA), partial least squares (PLSs) modeling. These approaches are an efficient tool in achieving better understanding about the contamination of two gulf regions in Black Sea. As objects of the study, a collection of marine sediment samples from Varna and Bourgas "hot spots" gulf areas are used. In the present case the use of cluster and PCA make it possible to separate three zones of the marine environment with different levels of pollution by interpretation of the sediment analysis (Bourgas gulf, Varna gulf and lake buffer zone). Further, the extraction of four latent factors offers a specific interpretation of the possible pollution sources and separates natural from anthropogenic factors, the latter originating from contamination by chemical, oil refinery and steel-work enterprises. Finally, the PLSs modeling gives a better opportunity in predicting contaminant concentration on tracer (or tracers) element as compared to the one-dimensional approach of the baseline models. The results of the study are important not only in local aspect as they allow quick response in finding solutions and decision making but also in broader sense as a useful environmetrical methodology.

Geologic Sediments↗
PubMed2002

Application of multivariate statistical analyses in the interpretation of geochemical behaviour of uranium in phosphatic rocks in the Red Sea, Nile Valley and Western Desert, Egypt.

Factor and cluster analyses as well as the Pearson correlation coefficient have been applied to geochemical data obtained from phosphorite and phosphatic rocks of Duwi Formation exposed at the Red Sea coast. Nile Valley and Western Desert. Sixty-six samples from a total of 71 collected samples were analysed for SiO2, TiO2, Al203, Fe2O3, CaO, MgO, Na2O, K2O, P2O5, Sr, U and Pb by XRF and their mineral constituents were determined by the use of XRD techniques. In addition, the natural radioactivity of the phosphatic samples due to their uranium, thorium and potassium contents was measured by gamma-spectrometry. The uranium content in the phosphate rocks with P2O5 > 15% (average of 106.6 ppm) is higher than in rocks with P2O5 < 15% (average of 35.5 ppm). Uranium distribution is essentially controlled by the variations of P2O5 and CaO, whereas it is not related to changes in SiO2, TiO2, Al2O3, Fe2O3, MgO, Na2O and K2O concentrations.-Factor analysis and the Pearson correlation coefficient revealed that uranium belaves geochemically in different ways in the phosphatic sediments and phosphorites in the Red Sea, Nile Valley and Western Desert. In the Red Sea and Western Desert phosphorites, uranium occurs mainly in oxidized U6+ state where it seems to be fixed by the phosphate ion, forming secondary uranium phosphate minerals such as phosphuranylite. In the Nile Valley phosphorites, ionic substitution of Ca2+ by U4 is the main controlling factor in the concentration of uranium in phosphate rocks. Moreover, fixation of U6- by phosphate ion and adsorption of uranium on phosphate minerals play subordinate roles.

Egypt↗
PubMed1986

[The good use of multivariate statistics].

Within a multidisciplinary team, it is important to establish a common language for the statistician and the electroencephalographer. Using an example, we propose a brief introduction to correspondence analysis. Within a set of 8 variables, we show how to isolate a subset of variables which are characteristic either for partial epilepsy or generalized epilepsy.

Adult↗
PubMed2002

Application of (1)h NMR and multivariate statistics for screening complex mixtures: quality control and authenticity of instant coffee.

Principal components analysis (PCA) followed by linear discriminant analysis (LDA) of the nuclear magnetic resonance (NMR) spectra from 98 instant spray-dried coffees, obtained from 3 different producers, correctly attributed 99% of the samples to their manufacturer. Blind testing of the PCA model with a further 36 samples of instant coffee resulted in a 100% success rate in identifying the samples from the 3 manufacturers. Coffees from one manufacturer were also assigned into 2 groups using these techniques, and the compound 5-(hydroxymethyl)-2-furaldehyde was identified as the primary marker of differentiation.

Analysis of Variance↗
PubMedDate not supplied

Multivariate statistical methods in toxicology. III. Specifying joint toxic interaction using multiple regression analysis.

Multiple regression is widely employed to study the contribution of components to the toxicologic effect of a mixture. Here, use is made of the fact that data obtained from standard curves of substances and from their mixtures are separable in regression analysis. Thus, under an assumption of additivity of responses, regression coefficients obtained for components in mixtures alone should be the same as for the individual substances. A t-test is developed such that nonsignificant t values support additivity, negative significant values support antagonism, and positive significant values support synergism. The results are applied to data on the mutagenicity of binary mixtures of azaserine, 4-nitroquinoline N-oxide, and 9-aminoacridine in TA 100 in the Ames assay.

4-Nitroquinoline-1-oxide↗
PubMed2000

Forensic discrimination of automotive paint samples using pyrolysis-gas chromatography-mass spectrometry with multivariate statistics

Analytical pyrolysis-gas chromatography (Py-GC) has been a standard method for the forensic analysis of automotive paint for a number of decades. Automotive paints are often identified by visual comparison of pyrograms for peak presence and intensities; however, such analyses can be subjective and time consuming. A preliminary investigation based on Py-GC-mass spectrometric analysis of 100 automobile paint samples of five different colors is presented. Designed experiments are employed to select pyrolysis conditions for adequate discrimination. Pattern recognition techniques including principal component analysis and canonical variates analysis are used to visualize clustering of pyrograms to validate comparisons between different automotive paint pyrograms. These methods have the potential to ease the interpretation task for data sets involving a large number of comparisons.

Journal Article↗
PubMed1987

An analysis of the nature of depressive phenomena in primary health care utilising multivariate statistical techniques.

The nature of depressive phenomena in primary health care was explored with data obtained from three primary health care clinics situated in the periphery of the city of Calcutta in India. The Self-Reporting Questionnaire (SRQ) and the Screening for Depression Questionnaire (SDQ-9) were used as the first stage and the Clinical Interview Schedule (CIS) and the Hamilton Rating Scale for Depression (Hamilton) as the second stage instruments respectively. Health workers with limited training administered the first stage instruments to consecutive adult clinic attenders. Principal components analysis followed by multiple linear regression analysis and discriminant function analysis were applied to the data. It was concluded that depressive phenomena in primary health care settings were largely undifferentiated in nature.

Adolescent↗