Statistical Principal Component Analysis Research Articles

Methods for Analysing Web Citations: A Study of Web-Coupling in a Closed Environment

To reveal the structure of the relationships that establish themselves on the World Wide Web, one needs to apply tools that faithfully represent the virtual environment. Some of the most interesting relationships are those that are brought to light by web-coupling (the Web analogue of bibliographic coupling). We here propose an analysis of this type based on the common links that are generated within a closed web environment, using multivariate statistics (principal component analysis, and multidimensional scaling) and a connection-based technique (Kohonen's self-organizing maps). The results show that it is possible to use the common links of web spaces in order to reveal the structures and the underlying relationships in a thematic closed environment.

Simulation of coal pyrolysis in plasma jet by CPD model

Data-driven fault detection technique has exhibited its wide applications in industrial process monitoring. However, how to extract the local and non-Gaussian features effectively is still an open problem. In this paper, statistics locality preserving projections (SLPP) is proposed to extract the local and non-Gaussian features. Firstly, statistics pattern analysis (SPA) is applied to construct process statistics and grasp the non-Gaussian statistical property using high order statistics. Then, locality preserving projections (LPP) method is used to discover local manifold structure of the statistics. In essence, LPP tries to map the close points in the original space to close in the low-dimensional space. Lastly, T2 and squared prediction error (SPE) charts of SLPP model are used to detect process faults. One simple simulated system and the Tennessee Eastman process show that the proposed SLPP method is more effective than principal component analysis, LPP and statistics principal component analysis in fault detection performance.

Major and trace element characterization of prehistoric ceramic from Rezende archaeological site

Forty one ceramic fragment samples from Rezende archaeological site, Centralina city, Minas Gerais State, Brazil, were analyzed using INAA to determine the concentration of 24 chemical elements: As, Ba, Ce, Co, Cr, Cs, Eu, Fe, Hf, K, La, Lu, Na, Nd, Rb, Sb, Sc, Sm, Ta, Tb, Th, U, Yb and Zn. Three multivariate statistical methods, cluster, discriminant and principal components analysis were performed on the data set. The results showed that the large majority of the samples (94%) can be considered to be manufactured using the same source of raw material.

Comment on "Multivariate and principal component statistical analysis of contamination in Urban and agricultural soils from North Jordan"

Comment on "Multivariate and principal component statistical analysis of contamination in Urban and agricultural soils from North Jordan"

Multivariate and principal component statistical analysis of contamination in urban and agricultural soils from north Jordan

A Multivariate analysis and principal component analysis was performed on 37 samples gathered from different regions in north Jordan. Twenty-eight descriptors (variables) for each sample were used in these calculations, among them were metal ion concentration, such as Pb, Cr, Co, Zn, Ca, Mg, Fe, Na, K, Al, Cu, Ni, Ti, Si and Mn. Other descriptors were pH, electrical conductivity (EC), and grain sizes. It was found that the samples form three clusters, namely, agriculture, industrial and waste treatment plane regions. Statistical analysis showed that the samples are classified into seven classes, with the majority of the samples classified into three clusters. The first three principal components explained 99.2% of the variance. This method offers a method to classify geographical regions on a pollution bases and to determine the possible sources of pollution for such regions. This study showed that some of the agricultural farms were remote from the pollution sources clusters with polluted regions such as industrial or waste treatment complexes.

A Comparative Ecological Study of Two Acidic Rivers in Southwestern Spain.

> Abstract We have analyzed select physicochemical and biological characteristics of two acidic rivers, the Tinto and the Odiel, located in the Pyritic Belt of Southwestern Spain. Despite a common geography and geochemistry, they exhibit important differences. Most of the measured physicochemical parameters, especially pH and those related to conductivity (heavy metal concentrations), showed more extreme values in the Tinto than in the Odiel. However, it was at the microbiological level (fungal and bacterial populations) where both rivers showed the most important differences according to the statistical analysis of principal components. Lithoautotrophic bacteria were more abundant in the more acidic Tinto River and fungal diversity varied significantly from one river to the other.http://link.springer-ny.com/link/service/journals/00248/bibs/38n2p146.html

Diagnosing Faults at Different Process Operating Points Using Neural Networks

Abstract Industrial processes often operate at different operating points, mainly to manufacture at different customer specifications. The demonstrated applications of neural networks for fault diagnosis usually consider only a primary operating point. This paper investigates their application for the diagnosis of non-catastrophic faults at different operating points on an industrial nuclear processing plant. Data conditioning methods, including statistical scaling and principal component analysis, are investigated to facilitate fault classification and to reduce the complexity of neural networks. Results are presented to illustrate the performance of trained neural networks for classifying process faults using simulated and real industrial data.

Principal component analysis of Draize eye Irritation tissue scores from 72 samples of 55 chemicals in the ECETOC data bank

The multivariate statistical method Principal Component Analysis (PCA) has been applied to a set of data from the ECETOC reference chemical data bank. PCA is a multivariate method that can be used to explore a complex data set. The results of the analysis show that most of the variability in the values for tissue damage scores for the 55 chemicals can be described by a single principal component which explains nearly 80% of the variability. This component is derived by giving approximately equal weight to each of the 18 individual measures made on the tissues over the 24-, 48- and 72-hr observation period. The principal component scores on the first component (PC I) are very highly correlated with the maximum individual weighted Draize scores or total Draize scores (TDS) derived using the Draize scoring method. A second principal component, describing about 7% of the variability, contrasts damage measured on the iris and cornea with that measured on the conjunctiva. Plots of principal component scores show the overall pattern of responses. In general, low measures of the TDS and a positive (PC I) score are associated with iris and conjunctival damage (damage to the iris was never recorded in the absence of damage to the conjunctiva). High TDS and negative PC I scores are associated with corneal and/or iris and conjunctiva damage. Plots of the principal component scores identify some chemicals that appear to cause unusual patterns of damage and identify some individual animals as having outlying or idiosyncratic responses. However, the analysis suggests that (i) there is only limited evidence for differential responses of different tissues and (ii) that attempts to identify alternative tests which predict specific types of tissue damage based on the results collected in a Draize test are likely to be unsuccessful. It indicates that further refinement of the results of the in vivo Draize test will not arise from more detailed analysis of the tissue scores but by refinement in the understanding of the mechanisms associated with the test. PCA was shown to be a powerful statistical tool for the investigation of complex data sets and provides a succinct description of such data sets, allowing patterns to be identified and the potential to develop further hypotheses for investigation.

A multiannual experiment on tropospheric aerosols at Terranova Bay (Antarctica): role of PIXE analysis and related techniques

In the framework of a multiannual research programme three summer sampling campaigns (1990/1991, 1993/1994, 1994/1995) were undertaken at a coastal site (lat. 72°42′43″ S, long. 164°06′58″ E) with a 12 h time resolution. Important topics of interest are associated with the roles of natural aerosols in the climatic system and biogeochemical cycles, as well as with aerosol transport, origin and interactions. Despite low concentrations, three basic techniques (a specially designed “inertial impactor for Antartica” (IIA); PIXE analysis; and multivariate statistical principal components analysis (PCA)) allowed (1990/1991 data) identification of two principal components (PCs) in the coarse fraction. “Non-sea-salt sulphur” and “sea-salt” aerosol dominated respectively the fine and coarse aerosol fractions. The 1993/1994 data are much more abundant (198 double samples) and the PIXE sensitivity was higher (systematic detection of Na and Mg). Preliminary results for the fine fraction, based on Na, Mg, Al, S, Ca and Fe, show the existence of three major PCs (interpreted as “n.s.s. sulphur”, “sea-salt” and “crustal”), each element being highly and significantly correlated to a particular PC. The addition of K does not change the main properties of the PCs and K is associated with both PC 2 and PC 3. Strong Cl depletion is observed. S, Na and Al are convenient tracers for obtaining continuous PC time distributions. A strong increase in S concentration is observed during the sampling period. Some short “episodes” are observed in all time distributions, possibly associated with aerosol transport phenomena. The 1993/1994 data and the present performance of IIA indicate that systematic work based on PIXE is now possible, despite the difficult Antarctic conditions.

Principal Descriptors of Body Condition Score in Holstein Cows

The objective of this study was to assess objectively the ability of observers to assess body condition of dairy cows. Four observers independently assigned a body condition score (five-point scale, .25 increments) and described the appearance of seven body regions of 225 Holstein cows. Areas described were the thurl region, ischial and ileal tuberosities, ilio-sacral and ischio-coccygeal ligaments, transverse processes of the lumbar vertebrae, and spinous processes of the lumbar vertebrae. An absolute body condition score was designated for each cow based on the modal body condition score for all observers. If there was no modal body condition score, the mean score was used for the absolute body condition score. Statistical analysis of principal components was used to examine the relationship between body region description and absolute body Condition score. Descriptions of body regions were highly correlated across all absolute body condition scores. Four principal component vectors explained 83.6% of the variation of the body region correlation matrix. The first principal latent vector accounted for 55% of the variation and was uniformly correlated with all body regions. Analysis of variance of first principal latent vector as the dependent variable and absolute body condition score as the class variable indicated that body condition could be separated into .25 units between 2.5 and 4.0, inclusively. Below 2.5 and >4.0, body condition could only be separated by .5 units. Distinct changes in specific body regions were associated with change in absolute body condition score. Observers agreed with the absolute score 58.1% of the time, deviating by .25 units 32.6% of the time. A body condition score can be given to a cow based on principal descriptors of specific body regions between 2.5 and 4.0 by .25 units.

Aroma Profile of Subprimals From Beef Carcasses Decontaminated With Chlorine and Lactic Acid

Aroma notes of chuck rolls from decontaminated beef carcasses were evaluated. Carcasses were spray-treated with either water, 200 ppm chlorine or 3% lactic acid immediately after inspection and again after spray chilling. Following fabrication, each chuck roll was divided into four pieces; vacuum-packaged; and stored for 10, 40, 80 or 120 days at 4°C. At different storage times, a six-member, professional, sensory panel evaluated beefy, bloody, sour, grassy, spoiled and metallic aromatic impressions on cooked patties made from ground chuck roll pieces using a 15-point attribute scale. Psychrotrophic bacterial counts were conducted on raw, ground samples. Principal component statistical analysis showed that the first principal component described 96% of the data and, therefore, it was used as an average acceptability score that explained all aroma descriptors. Chucks from chlorine-treated carcasses tended to have higher (P = 0.08) acceptability scores, followed by lactic acid – and water-treated counterparts. The rate of change in aroma occurred faster between 10 and 40 days for lactic acid – and water-treated samples and between 40 and 80 days for chlorine-treated samples. Bacterial counts increased during storage up to 80 days; however, treatments were not different (P >0.05).

Non-destructive approach for analysing the germinability of acorns

A novel application of the ultrasonic velocity method is proposed to separate germinated and non-viable acorns and in this way to predict their germinability, based on morphological (dimensions of two axes) and physical criteria (density, ultrasonic velocity, impedance and acoustic stiffness). All specimens were 6 months old. They had an average trading quality, and were characterized by a high ratio (50%) of non-viable acorns. Relationships between morphological and physical characteristics were established. Experimental advances have been accomplished in the understanding of acoustical parameters able to predict the germinability, using statistical principal-component analysis. Germinability is related to density and ultrasonic velocity. A high density and a high velocity characterize a viable acorn.

Application of multivariate statistics to the problems of upper palaeolithic and mesolithic samples

Multivariate statistics (discriminant function analysis and principal component analysis) have been applied to a broad sample of Upper Paleolithic and mesolithic skulls. In addition to some methodological problems concerning the evaluation of missing data by principal component analysis, we discussed the possibility of misclassifications (14%).