Principal components analysis sites

To highlight and explain the quantitative chemical differences between the pitches found in the two archaeological sites, a chemometric evaluation of the GC/MS data (normalized peak areas) by means of principal component analysis (PCA) was performed. The PCA scatter plot of the first two principal components (Figure 8.6) highlights that the samples from Pisa and Fayum are almost completely separated into two clusters and that samples from Fayum form a relatively compact cluster, while the Pisa samples are... 

The multivariate statistical data analysis, using principal component analysis (PCA), of this historical data revealed three main contamination profiles. A first contamination profile was identified as mostly loaded with PAHs. A samples group which includes sampling sites R1 (Ebro river in Miranda de Ebro, La Rioja), T3 (Zadorra river in Villodas, Alava) and T9 (Arga river in Puente la Reina, Navarra), all located in the upper Ebro river basin and close to Pamplona and Vitoria cities,... 

To illustrate the environmental application of the SIMCA method we examined a set of isomer specific analyses of sediment samples. The data examined were derived from more than 200 sediment samples taken from a study site on the Upper Mississippi River (41). These analytical data were transferred via magnetic tape from the laboratory data base to the Cyber 175 computer where principal component analysis were conducted on the isomer concentration data (ug/g each isomer). 

Principal Component Analysis (PCA) is performed on a human monitoring data base to assess its ability to identify relationships between variables and to assess the overall quality of the data. The analysis uncovers two unusual events that led to further investigation of the data. One, unusually high levels of chlordane related compounds were observed at one specific collection site. Two, a programming error is uncovered. Both events had gone unnoticed after conventional univariate statistical techniques were applied. These results Illustrate the usefulness of PCA in the reduction of multi-dimensioned data bases to allow for the visual inspection of data in a two dimensional plot. 

All of the compounds measured In the monitoring program are listed In the report by Thrane (VI). Table I lists the compounds which were selected as variables for the cluster analysis. Feature (l.e. attribute) selection for the cluster analysis was partially based upon the results of a principal component analysis (Henry, 12). Additional features were Included If (1) the compound occurred In relatively large concentrations, or (2), If a compound was known to have adverse health effect. Wind direction, wind speed, and temperature were recorded as ordered variables. The chemical measurements were taken at five locations. Descriptions of those sites and of the methods and techniques used to collect the data are described in detail in the report by Thrane. 

Gatz (8) applied a principal components analysis to aerosol composition data for St. Louis, Mo taken as part of project METROMEX (13-1 t). Nearly 400 filters collected at 12 sites were analyzed for up... 

Ochre is very common in the Terminal Archaic-Early Formative archaeological site of Jiskairumoko, (Rio Have, Lake Titicaca Basin, southern Peru). Within the site, ochre was found on tools, palettes, and in burials and soil deposits within structures in several contexts, suggesting both symbolic and functional uses of ochre. Variations in the color and contexts imply possibilities for different uses of ochre.. Instrumental neutron activation analysis was used to analyze the ochre samples found in Jiskairumoko. Multivariate analysis of the elemental data by principal components analysis suggests trends in the data related to the compositional variation of ochres on the site. Further analysis of the ochre will lead to conclusions about the variation in composition of the ochres from Jiskairumoko and possible archaeological conclusions about ancient technologies and uses of ochre on the site. 

Once the toxicity parameters were computed to a spreadsheet yielding a table of 30 rows (effluents) and 9 columns (bioassays), we ran a principal component analysis (PCA) to check the diversity patterns of effluents and the correlation between tests. The PCA calculations were carried out using the ADE 3.6 statistical package on a Macintosh computer. ADE was developed by the University of Lyon II and by the French National Centre of Scientific Research (CNRS) common biometry laboratory. The new version ADE version 4 running on Mac and PC computers is now available on this university s internet site at http //pbil.univ-lvon 1. fr/ADE-4/... 

Two collectors were operated at each site with these paired results statistically screened for potential contamination based on indpendently measured experimental uncertainties. Geographical mapping of rainwater c ncentrations demonstrated a clear enhancement of H, excess SO., and trace metals downwind of the smelter. Principal component analysis revealed the influence of seasalt, crustal material, and a component interpreted to represent smelter S02 and trace metal emissions. 

Comparison and ranking of sites according to chemical composition or toxicity is done by multivariate nonparametric or parametric statistical methods however, only descriptive methods, such as multidimensional scaling (MDS), principal component analysis (PCA), and factor analysis (FA), show similarities and distances between different sites. Toxicity can be evaluated by testing the environmental sample (as an undefined complex mixture) against a reference sample and analyzing by inference statistics, for example, t-test or analysis of variance (ANOVA). 

Transformations of a set of molecular descriptors are often performed when there is the need of a —> variable reduction or the need to modify binary vectors, such as site and substituent-oriented variables, into real-valued variable vectors. The milestone of these techniques is the —> Principal Component Analysis (PCA), but also —> Fourier analysis and —> Wavelet analysis are often used, especially for spectra descriptors compression. 

We used principal component analysis to identify correlated motions in different forms of hPNP, namely, its apo and complexed forms, and assess whether they facilitate the 241-265 loop rearrangement prior to the subsequent phosphorolysis reaction. We compared the principal components for the apo and complexed hPNP simulations, and examined the different correlated motions for each form of the enzyme, comparing directly to the crystallographic B-factors. Finally, via experimental site-directed mutagenesis, several residues implicated in the correlated motion were mutated, and the kinetic constants kcat and KM (fingerprints of catalytic efficiency), were measured to weigh the impact of these residues in the phosphorolytic efficiency. 

Corresponding to the dimension d = 2, the poset shown in Fig. 19 can alternatively be visualized by a two-dimensional grid as is shown in Fig. 22. Both visualizations have their advantages. Structures within a Hasse diagram, e.g., successor sets, or sets of objects separated from others by incomparabilities, can be more easily disclosed by a representation like that of Fig. 19. In multivariate statistics reduction of data is typically performed by principal components analysis or by multidimensional scaling. These methods minimize the variance or preserve the distance between objects optimally. When order relations are the essential aspect to be preserved in the data analysis, the optimal result is a visualization of the sediment sites within a two-dimensional grid. 

Fig. (4). Principal component analysis (PCA) with respect to the occurrence of various potential reactive sites in 4861 STL. Scores (A) and loadings (B) of the first three PCs are plotted in X, Y and Z direction. The scores show the clustering of the compounds into 16 major groups while the loadings show the influence of the variables on the position of these clusters in feature space and the distinction between the clusters.

While the deuterium spectra of benzaldehydes from different sources do not appear (upon visual inspection) to have significant differences from one another, other than some variation in intensity, the deuterium distribution of benzaldehyde does form clusters on a principal component analysis plot. Benzaldehyde products from the same source have similar deuterium distributions and are therefore close to each otlier on the plot (Figure 1). Thus, the origin of benzaldehyde can be differentiated based on site-specific deuterium distribution. Products outside of the clusters of knoivn samples are normally considered as originating from an unknown source or as a mixture of benzaldehyde from different known sources. 

The objectives of this study were to quantify the trace metals in PM2 5 of Eastern and Western Canada, to analyze their annual and seasonal trends and identify their source origin, by evaluating a database of trace metal concentrations obtained over a 2-year period (May 2004-December 2006) from the NAPS network. Over 1000 PM2 5 samples collected at seven selected sites were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) after microwave assisted acid digestion. This technique offers low detection limits, wide linear dynamic range, multielement capability, ability to measure isotope ratios and high sample throughput. Principal Components Analysis (PCA) was used to identify sources of trace metals at each sampling site. 

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