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Results for “Principal Component Analysis”

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PubMed2005

Serum 1H-nuclear magnetic spectroscopy followed by principal component analysis and hierarchical cluster analysis to demonstrate effects of statins on hyperlipidemic patients.

Use of statins for prevention of coronary heart disease is based on the decrease of serum cholesterol and LDL cholesterol. To better investigate the changes in lipid profile after statin treatment, we propose here to use an analysis of serum by proton nuclear magnetic resonance (NMR) spectroscopy associated with a multivariate analysis of the main spectral components. Sera were obtained from 60 male patients treated for 6 weeks with simvastatin (30 patients) or atorvastatin (30 patients) for who LDL cholesterol decreased by over 45% in all selected patients. Proton nuclear magnetic resonance spectra were obtained and the region of methyl resonance from lipids was separated into six consecutive lines attributed to lipids which were analyzed by principal component analysis (PCA) and clustering by hierarchical cluster analysis (HCA) based on Euclidian distance coupled with the Ward's minimum variance method. PCA and HCA gave a map discriminating the 120 samples into five clusters, three clusters containing samples obtained at baseline and two others containing samples obtained after treatment. Both statins produced a decrease in lower-density lipoprotein components and an increase in higher density lipoprotein components. Patients with a coronary heart disease history could be discriminated after treatment by the increase in the component containing the highest proportion of HDL. Proton NMR spectroscopy of sera coupled with a PCA and an HCA was able to detect variations in the metabolism of lipids resulting from statin treatments.

Aged↗

PubMed1984

Principal component analysis as a tool for studying interdependences among trace metals in edible mussels from the Gulf of Trieste.

Principal component analysis has been applied to analyze the correlation matrix obtained from a (6 X 44) data matrix. The 6 trace metals are Co, Ni, Cu, Cd, Hg, and Pb, which are contained in the soft part of edible mussels (Mytilus galloprovincialis Lamarck) from a hatchery on the gulf of Trieste. In the space of "eigenvectors", the dimensions are reduced to 3 principal axes, which account for about 77% of the total variance. The unrotated factor matrix obtained by the principal components shows that Co, Ni and Cu are bonded to the first principal component, Cd and Pb to the second, Hg to the third. This picture is also assessed by the Varimax-rotated matrix and it is tentatively explained, at least in part, by considering the associations of the trace metals from various geographical sources.

Animals↗

PubMedDate not supplied

Generalized 2D principal component analysis for face image representation and recognition.

In the tasks of image representation, recognition and retrieval, a 2D image is usually transformed into a 1D long vector and modelled as a point in a high-dimensional vector space. This vector-space model brings up much convenience and many advantages. However, it also leads to some problems such as the Curse of Dimensionality dilemma and Small Sample Size problem, and thus produces us a series of challenges, for example, how to deal with the problem of numerical instability in image recognition, how to improve the accuracy and meantime to lower down the computational complexity and storage requirement in image retrieval, and how to enhance the image quality and meanwhile to reduce the transmission time in image transmission, etc. In this paper, these problems are solved, to some extent, by the proposed Generalized 2D Principal Component Analysis (G2DPCA). G2DPCA overcomes the limitations of the recently proposed 2DPCA (Yang et al., 2004) from the following aspects: (1) the essence of 2DPCA is clarified and the theoretical proof why 2DPCA is better than Principal Component Analysis (PCA) is given; (2) 2DPCA often needs much more coefficients than PCA in representing an image. In this work, a Bilateral-projection-based 2DPCA (B2DPCA) is proposed to remedy this drawback; (3) a Kernel-based 2DPCA (K2DPCA) scheme is developed and the relationship between K2DPCA and KPCA (Scholkopf et al., 1998) is explored. Experimental results in face image representation and recognition show the excellent performance of G2DPCA.

Algorithms↗

PubMed2005

Energy landscape of a small peptide revealed by dihedral angle principal component analysis.

A 100 ns molecular dynamics simulation of penta-alanine in explicit water is performed to study the reversible folding and unfolding of the peptide. Employing a standard principal component analysis (PCA) using Cartesian coordinates, the resulting free-energy landscape is found to have a single minimum, thus suggesting a simple, relatively smooth free-energy landscape. Introducing a novel PCA based on a transformation of the peptide dihedral angles, it is found, however, that there are numerous free energy minima of comparable energy (less than or approximately 1 kcal/mol), which correspond to well-defined structures with characteristic hydrogen-bonding patterns. That is, the true free-energy landscape is actually quite rugged and its smooth appearance in the Cartesian PCA represents an artifact of the mixing of internal and overall motion. Well-separated minima corresponding to specific conformational structures are also found in the unfolded part of the free energy landscape, revealing that the unfolded state of penta-alanine is structured rather than random. Performing a connectivity analysis, it is shown that neighboring states are connected by low barriers of similar height and that each state typically makes transitions to three or four neighbor states. Several principal pathways for helix nucleation are identified and discussed in some detail.

Energy Transfer↗

PubMed1993

[Principal components analysis].

OBJECTIVE: This study wants to identify factors or components latent besides health indicators from Spanish regions, and its graphic output. DESIGN: Observational study. SETTING: Twenty eight indicators from regions were used: mortality, morbidity, communicable and no-communicable diseases, diet, dwelling and sanitary resources. Measurement was made between 1980-1988. INTERVENTIONS: Principal component analysis has been applied to the indicators, reducing data dimension. MEASUREMENT AND MAIN RESULTS: Eight factors have been extracted, which explain 90% of the original information. This analysis, as can be seen from communnalities, represents correctly the set of original variables. The factors with more easily interpretation were: shortage sanitary resources, develop diet, mortality, chronic diseases and accidental. CONCLUSIONS: Only reduction data dimension could be justify the use of principal component analysis. Behind the agrupation of variables is mostly the socioeconomic background.

Health Status Indicators↗

PubMed2006

[Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model].

A new method for the discrimination of varieties of apple by means of near infrared spectroscopy (NIRS) was developed. First, principal component analysis (PCA) was used to compress thousands of spectral data into several variables and describe the body of spectra, the analysis suggested that the cumulate reliabilities of PC1 and PC2 (the first two principle components) were more than 98%, and the 2-dimentional plot was drawn with the scores of PC1 and PC2. It appeared to provide the best clustering of the varieties of apple. The loading plot was drawn with PC1 and PC2 through the whole wavelength region. The fingerprint spectra, which were sensitive to the variety of apple, were obtained from the loading plot. The fingerprint spectra were applied as ANN-BP inputs. Seventy five samples from three varieties were selected randomly, then they were used to build discrimination model. This model was used to predict the varieties of 15 unknown samples; the distinguishing rate of 100% was achieved. This model is reliable and practicable. So the present paper could offer a new approach to the fast discrimination of varieties of apple.

Malus↗

PubMed2003

Experimental comparison of three monoclonal antibodies for the class-selective immunoextraction of triazines. Correlation with molecular modeling and principal component analysis studies.

The specificity of three immunosorbents (ISs) based on different monoclonal anti-triazine antibodies has been characterized by extraction recoveries studies and with step elution experiments. Both indicated that the anti-dichloroatrazine IS is specific of terbutylazine and cyanazine. The anti-atrazine IS is specific of the chlorotriazines, whereas the anti-ametryn IS can trap all the triazines. This confirms the great influence of the hapten design on the specificity of the resulting antibodies, even if the target molecules are small. Moreover, the anti-ametryn IS is suitable for class-selective extraction of triazines contained in complex matrices. An approach designed to learn more about the specificity for a group of structurally related compounds of antibodies produced with a given compound is proposed and evaluated. Molecular modeling followed by principal component analysis has been used to obtain distribution maps with the relative position of each immunoconjugate and all the triazines. In all three cases, conclusions on specificity made with the analysis of the maps fit well with the experimental results. Consequently, molecular modeling coupled with principal component analysis seems to be a unique, inexpensive, and rapid tool to select the appropriate hapten providing highly specific or class-specific antibodies according to the given problem.

Antibodies, Monoclonal↗

PubMed1979

On the independence of P300 and the CNV: a short critique of the principal components analysis of Donchin et al. (1975).

Donchin, Tueting, Ritter, Kutas and Heffley (1975) present evidence from a principal components analysis (PCA) that the CNV and P300 are independent. This short critique points out a number of erros in their PCA and presents a reworking of their analysis. A number of further aspects of Donchin et al.'s paper are also discussed. The general value of this contribution in drawing the attention of EP researchers to potential sources of error in the application of factor analysis is emphasized.

Contingent Negative Variation↗

PubMedDate not supplied

Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbon-hydrogen bonds with molybdenum-oxo bonds.

Principal component analysis (PCA) is a favorite tool in chemometrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approaches to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of outliers. In this paper we discuss a robust fuzzy PCA algorithm (FPCA). The new algorithm is illustrated on a data set concerning interaction of carbon-hydrogen bonds with transition metal-oxo bonds in molybdenum complexes. Considering, for example, a two component model, FPCA accounts for 97.20% of the total variance and PCA accounts only for 69.75%.

Journal Article↗

PubMed2002

A near infrared spectroscopic discrimination of noodle flours using a principal-component analysis coupled with chemical information.

Using a portable near infrared (NIR) spectrometer, we discriminated flours for making Japanese noodles (Soba), not only relying on a statistical and mathematical approach, but also on a chemical interpretation of the NIR spectra. In original NIR spectra, the particle-size difference, which results in an undesired systematic variation, was extracted and interpreted as the first-principal component factor by a principal-component analysis. The discrimination of flour materials cannot be satisfied by this factor. However, after a standardized treatment for the original spectra, the particle-size effects were eliminated; alternatively, differences in the chemical contents were extracted as principal-component factors. Using these factors, flour material discrimination was achieved much better. This study suggests a novel idea of utilizing the wavelength contribution ratio spectra for interpreting the factors extracted from the principal-component analysis for the NIR spectra. This report also describes the relationship between the NIR spectra and the chemical-analysis data.

Fagopyrum↗

PubMed2001

Bleachability and characterization by Fourier transform infrared principal component analysis of Acetosolv pulps obtained from sugarcane bagasse.

Sugarcane bagasse Acetosolv pulps were bleached by xylanase and the pulps classified by using Fourier transform infrared (FTIR) spectroscopy and principal component analysis (PCA). Pulp was treated with xylanase for 4-8 hwith stirring at 30 degrees C. Some samples were further extracted with NaOH for 1 h at 65 degrees C. FTIR spectra were recorded directly from the dried pulp samples by using the diffuse reflectance technique. Reduction in kappa number of 69% was obtained after sequence xylanase (4 h)-alkaline extraction. During bleaching the viscosity decreased only 12%. FTIR-PCA showed that the first three principal components (PCs) explained more than 90% of the total variance of the pulp spectra. PC2 x PC1 plot showed that the points related to pulps from sequence xylanase (4 h)-alkaline extraction are different from the other. This group is enlarged by plotting PC3 x PC1 or PC3x PC2 containing all pulps submitted to alkaline extraction. PC2 and PC3 are the principal factor for differentiation of the pulps. These PCs suffer influence of the ester bands (1740 and 1244 cm(-1)). On the other hand, the pulps bleached only with xylanase could not be differentiated from the nonbleached pulps.

Biotechnology↗

PubMed2005

Quince (Cydonia oblonga miller) fruit characterization using principal component analysis.

This paper presents a large amount of data on the composition of quince fruit with regard to phenolic compounds, organic acids, and free amino acids. Subsequently, principal component analysis (PCA) is carried out to characterize this fruit. The main purposes of this study were (i) the clarification of the interactions among three factors-quince fruit part, geographical origin of the fruits, and harvesting year-and the phenolic, organic acid, and free amino acid profiles; (ii) the classification of the possible differences; and (iii) the possible correlation among the contents of phenolics, organic acids, and free amino acids in quince fruit. With these aims, quince pulp and peel from nine geographical origins of Portugal, harvested in three consecutive years, for a total of 48 samples, were studied. PCA was performed to assess the relationship among the different components of quince fruit phenolics, organic acids, and free amino acids. Phenolics determination was the most interesting. The difference between pulp and peel phenolic profiles was more apparent during PCA. Two PCs accounted for 81.29% of the total variability, PC1 (74.14%) and PC2 (7.15%). PC1 described the difference between the contents of caffeoylquinic acids (3-O-, 4-O-, and 5-O-caffeoylquinic acids and 3,5-O-dicaffeoylquinic acid) and flavonoids (quercetin 3-galactoside, rutin, kaempferol glycoside, kaempferol 3-glucoside, kaempferol 3-rutinoside, quercetin glycosides acylated with p-coumaric acid, and kaempferol glycosides acylated with p-coumaric acid). PC2 related the content of 4-O-caffeoylquinic acid with the contents of 5-O-caffeoylquinic and 3,5-O-dicaffeoylquinic acids. PCA of phenolic compounds enables a clear distinction between the two parts of the fruit. The data presented herein may serve as a database for the detection of adulteration in quince derivatives.

Amino Acids↗

PubMed1999

Motor coordination in a multi-muscle system as revealed by principal components analysis of electromyographic variation.

The variation in electromyographic output of twelve trigeminal muscles of the rabbit was studied to test the hypothesis that they are under the control of a small number of independent neural factors. Jaw muscle electromyograms (EMGs) of eight animals were recorded in 95 chewing sequences, each consisting of 40-75 chewing cycles. The within-sequence correlations of the EMG burst amplitudes (integrated per cycle) and burst onsets were calculated between the muscles. The correlation matrix was subjected to a principal components analysis. This method aims at describing the variation in EMG amplitude and timing by means of the smallest possible set of newly defined variables, or principal components. Of the variation in EMG amplitude values of the twelve muscles, 75-90% could be accounted for by only three principal components. Each principal component was characterized by a group of muscles with high mutual positive correlations; they had zero correlation with other principal components. The first component represents the jaw closers: most of the bilateral masseter and the medial pterygoid muscles. The second represents the openers: the bilateral digastric and lateral pterygoid muscles. This demonstrates the tight control of both the jaw openers and closers, each by a single neural factor; these two factors are independent of one another. They most likely originate from the specific inputs from primary afferents to the opener and closer motoneurons. Unexpectedly, a third independent principal component appeared to control the closing activity of the non-chewing side, posterior deep masseter muscle. It was hypothesized that this muscle acts independently of the other closers to disengage the teeth and resets the jaw for a new chewing cycle. Principal components analysis of variation in timing of EMG onset revealed a grouping of all masticatory muscles in a single cluster, independent of EMG amplitude. This supports the hypothesis that timing and amplitude of masticatory EMG patterns are controlled independently.

Animals↗

PubMed2006

Assessing newborn body composition using principal components analysis: differences in the determinants of fat and skeletal size.

BACKGROUND: Birth weight is a composite of skeletal size and soft tissue. These components are likely to have different growth patterns. The aim of this paper is to investigate the association between established determinants of birth weight and these separate components. METHODS: Weight, length, crown-rump, knee-heel, head circumference, arm circumference, and skinfold thicknesses were measured at birth in 699 healthy, term, UK babies recruited as part of the Exeter Family Study of Childhood Health. Corresponding measurements were taken on both parents. Principal components analysis with varimax rotation was used to reduce these measurements to two independent components each for mother, father and baby: one highly correlated with measures of fat, the other with skeletal size. RESULTS: Gestational age was significantly related to skeletal size, in both boys and girls (r = 0.41 and 0.52), but not fat. Skeletal size at birth was also associated with parental skeletal size (maternal: r = 0.24 (boys), r = 0.39 (girls) ; paternal: r = 0.16 (boys), r = 0.25 (girls)), and maternal smoking (0.4 SD reduction in boys, 0.6 SD reduction in girls). Fat was associated with parity (first borns smaller by 0.45 SD in boys; 0.31 SD in girls), maternal glucose (r = 0.18 (boys); r = 0.27 (girls)) and maternal fat (r = 0.16 (boys); r = 0.36 (girls)). CONCLUSION: Principal components analysis with varimax rotation provides a useful method for reducing birth weight to two more meaningful components: skeletal size and fat. These components have different associations with known determinants of birth weight, suggesting fat and skeletal size may have different regulatory mechanisms, which would be important to consider when studying the associations of birth weight with later adult disease.

Adiposity↗

PubMed1982

Principal component analysis of pain-related cerebral potentials to mechanical and electrical stimulation in man.

Single trial event-related cerebral potentials (ERPs) in response to skin stimuli of various intensities and qualities in man were investigated in respect to their nociceptive information content. Electrical constant current stimuli (20 msec, 2 - 8 mA) and mechanical force controlled stimuli (20 msec, 0.8 - 3.2 N) were applied to the tip of the left middle finger. Four intensities of each stimulus quality were given, each intensity appearing 40 times in standardized randomized order. EEG segments (between 5 sec before and 500 msec after stimulus onset) were subjected to computer analysis. ERP wave form was shown to depend upon the amount of alpha waves in the prestimulus EEG. For analysis, only subjects with low power in the alpha band were selected. Principal component analysis was applied to all single trial ERPs measured using the variance-covariance matrix of association. Six principal components (PCs) were extracted accounting for about 90% of total variance. Five of the extracted PCs had well located loading maxima: PC1 (50 - 80 msec), PC4 (140 - 160 msec), PC3 (200 - 250 msec), PC4 (280 - 360 msec), PC5 (400 - 500 msec); PC6 appeared polyphasic. Analysis of variance of the mean PC scores revealed that one PC (PC1) discriminated between quality, and 4 PCs (PC1 - PC4) between quantity of stimulation. Eliminating effects of stimulus intensity resulted in two PCs (PC2, PC4) which distinguished exclusively between non-pain and pain. PCA applied to disjunctive subsets of ERPs, corresponding to the different experimental conditions, yielded practically identical sets of PCs, such that no specific ERP component emerged when pain was reported.

Adult↗

PubMed1989

Effects of delta-9-tetrahydrocannabinol on sensory evoked hippocampal activity in the rat: principal components analysis and sequential dependency.

The effects of delta-9-tetrahydrocannabinol (delta-9-THC) were assessed on identified hippocampal sensory evoked potentials obtained from rats during performance of a two-tone discrimination task. Techniques which analyzed the trial-to-trial sequential and serial dependence underlying the variance in evoked potential amplitude were utilized. Waveforms of averaged tone-evoked potentials (AEPs) recorded from the outer molecular layer of the dentate gyrus (OM) were subjected to principal components analysis which revealed eight principal components accounting for 90.3% of the total variance in the set of OM AEP waveforms. Five of the eight components were altered significantly in comparison to vehicle injection sessions after administration of either a 1.0- or 2.0-mg/kg dose of delta-9-THC. These alterations accounted for the amplitude and latency changes in the OM AEP described in a previous report. In addition, delta-9-THC also disrupted the trial-to-trial sequential dependency of the OM AEPs. An important result showed that delta-9-THC selectively influenced the serial dependence of the OM AEP. These results implicate delta-9-THC as a potent disruptor of temporally specific information as it is processed by the hippocampus and suggest that such disruption may be the basis of delta-9-THC effects on memory processes in humans.

Animals↗

PubMed2003

Using principal component analysis to monitor spatial and temporal changes in water quality.

Chemical, biological and physical data monitored at 12 locations along the Passaic River, New Jersey, during the year 1998 are analyzed. Principal component analysis (PCA) was used: (i) to extract the factors associated with the hydrochemistry variability; (ii) to obtain the spatial and temporal changes in the water quality. Solute content, temperature, nutrients and organics were the main patterns extracted. The spatial analysis isolated two stations showing a possible point or non-point source of pollution. This study shows the importance of environmental monitoring associated with simple but powerful statistics to better understand a complex water system.

Environmental Monitoring↗

PubMed2001

Principal components analysis of protein structure ensembles calculated using NMR data.

One important problem when calculating structures of biomolecules from NMR data is distinguishing converged structures from outlier structures. This paper describes how Principal Components Analysis (PCA) has the potential to classify calculated structures automatically, according to correlated structural variation across the population. PCA analysis has the additional advantage that it highlights regions of proteins which are varying across the population. To apply PCA, protein structures have to be reduced in complexity and this paper describes two different representations of protein structures which achieve this. The calculated structures of a 28 amino acid peptide are used to demonstrate the methods. The two different representations of protein structure are shown to give equivalent results, and correct results are obtained even though the ensemble of structures used as an example contains two different protein conformations. The PCA analysis also correctly identifies the structural differences between the two conformations.

Macromolecular Substances↗