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At least 253 records · Page 14Linked to original sources
PubMed2004

Principal-component-analysis eigenvalue spectra from data with symmetry-breaking structure.

Principal component analysis (PCA) is a ubiquitous method of multivariate statistics that focuses on the eigenvalues lambda and eigenvectors of the sample covariance matrix of a data set. We consider p, N-dimensional data vectors xi drawn from a distribution with covariance matrix C. We use the replica method to evaluate the expected eigenvalue distribution rho(lambda) as N--> infinity with p=alphaN for some fixed alpha. In contrast to existing studies we consider the case where C contains a number of symmetry-breaking directions, so that the sample data set contains some definite structure. Explicitly we set C=sigma2I+sigma(2)Sigma(S)(m=1)A(m)B(m)B(T)(m), with A(m)>0 for all m. We find that the bulk of the eigenvalues are distributed as for the case when the elements of xi are independent and identically distributed. With increasing alpha a series of phase transitions are observed, at alpha=A(-2)(m), m=1,2,..., S, each time a single delta function, delta(lambda-lambda(u)(A(m))), separates from the upper edge of the bulk distribution, where lambda(u)(A)=sigma(2)[1+A][1+(alphaA)(-1)]. We confirm the results of the replica analysis by studying the Stieltjes transform of rho(lambda). This suggests that the results obtained from the replica analysis are universal, irrespective of the distribution from which xi is drawn, provided the fourth moment of each element of xi exists.

Fourier Analysis↗
PubMedDate not supplied

Identification of fat, protein matrix, and water/starch on microscopy images of sausages by a principal component analysis-based segmentation scheme.

A color-based segmentation scheme applied to microscopy images of cryosectioned sausages is proposed. The segmentation scheme is capable of segmenting three different levels on the microscopy images: the fat particles, the protein matrix, and water/starch. The method is based on principal component analysis. A user-friendly program was developed for the manual segmentation of a selection of image pixels by microscopists. Principal component models based on the manually classified pixels are then used to segment fat, protein matrix, and starch/water on microscopy images. The program can also be used as a training tool for microscopists.

Animals↗
PubMed2003

Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks.

Detection and correct classification of gasoline is important for both arson and fuel spill investigation. Principal component analysis (PCA) was used to classify premium and regular gasolines from gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Depending upon the dataset used for training and tests, around 80-93% of the samples were correctly classified as either premium or regular gasoline using the Mahalanobis distances calculated from the principal components scores. Only 48-62% of the samples were correctly classified when the premium and regular gasoline samples were divided further into their winter/summer sub-groups. Artificial neural networks (ANNs) were trained to recognise premium and regular gasolines from the same GC-MS data. The best-performing ANN correctly identified all samples as either a premium or regular grade. Approximately 97% of the premium and regular samples were correctly classified according to their winter or summer sub-group.

Journal Article↗
PubMed2006

Degradation of malathion by Pseudomonas during activated sludge treatment system using principal component analysis (PCA).

Popular descriptive multivariate statistical method currently employed is the principal component analyses (PCA) method. PCA is used to develop linear combinations that successively maximize the total variance of a sample where there is no known group structure. This study aimed at demonstrating the performance evaluation of pilot activated sludge treatment system by inoculating a strain of Pseudomonas capable of degrading malathion which was isolated by enrichment technique. An intensive analytical program was followed for evaluating the efficiency of biosimulator by maintaining the dissolved oxygen (DO) concentration at 4.0 mg/L. Analyses by high performance liquid chromatographic technique revealed that 90% of malathion removal was achieved within 29 h of treatment whereas COD got reduced considerably during the treatment process and mean removal efficiency was found to be 78%. The mean pH values increased gradually during the treatment process ranging from 7.36-8.54. Similarly the mean ammonia-nitrogen (NH3-N) values were found to be fluctuating between 19.425-28.488 mg/L, mean nitrite-nitrogen (NO3-N) ranging between 1.301-2.940 mg/L and mean nitrate-nitrogen (NO3-N) ranging between 0.0071-0.0711 mg/L. The study revealed that inoculation of bacterial culture under laboratory conditions could be used in bioremediation of environmental pollution caused by xenobiotics. The PCA analyses showed that pH, COD, organic load and total malathion concentration were highly correlated and emerged as the variables controlling the first component, whereas dissolved oxygen, NO3-N and NH3-N governed the second component. The third component repeated the trend exhibited by the first two components.

Biodegradation, Environmental↗
PubMed2006

[Principal component analysis and cluster analysis of inorganic elements in Panax quinque folium. L].

The contents of elements such as Mg, Al, P, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Mo, Cd and Pb in twelve Panax quinque folium. L samples were determined by means of ICP/MS. The results were used for the development of element fingerprint chromatogram. The principal component analysis of SPSS was applied for the study of characteristic elements in Panax quinque folium. L. Five principal components which accounted for over 90% of the total variance were extracted from the original data. The analysis results show that Fe, Al, V, Mn, Mg, Sr, Mo, Ca and Cu may be the characteristic elements in Panax quinque folium. L. The results of Q-type cluster analysis show that the samples could be clustered reasonably into five groups, and the elemental distribution characteristics are related to the breeds of Panax quinque folium. L.

Cluster Analysis↗
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↗
PubMed2004

Differentiation of bovine and porcine gelatins using principal component analysis.

Gelatin is a collagen derivative, which has a large application in the pharmaceutical, food and adhesive industries as well as photography. The large similarity in structure and properties of gelatins from different origins makes their differentiation difficult. Certain chemometric methods, such as principal component analysis (PCA), can help to classify and characterize gelatin components. In this study 14 bovine and 5 porcine gelatins were examined. The analysis procedure involved complete hydrolysis of samples by classic acid hydrolysis in order to release their amino acid residues. Separation and determination of amino acids was achieved by reversed-phase (RP) HPLC following pre-column derivatisation. Orthophtaldialdehyde (OPA) and 4-chloro-7-nitro benzofurazane (NBD-Cl) were used as derivatisation reagents. From the 20 peaks detected by HPLC analysis, one was very typical in bovine gelatin. Peak height, area, area percentage and width were used to make matrixes. Principal component analysis with the MATLAB program was used to differentiate these gelatins. PCA on matrix of height, width and total matrix were resulted in good differentiation between bovine and porcine gelatins.

Animals↗
PubMed1986

Spectral methods for principal components analysis of event-related brain potentials.

Principal components analysis has been a widely used method for the analysis of event-related, electrical brain potentials (ERPs). Recent emphasis has been placed on measuring the topography of ERPs, as derived from the instantaneous measurements from multiple locations, and on defining diagnostic differences in ERPs among various clinical populations. One goal of the present paper is to discuss inherent difficulties in utilizing PCA as an analytical technique in multiple location and multiple group studies. Another goal is to demonstrate the utility of spectral analysis and its equivalency to PCA when the signal imbedded in stationary noise model is used. Spectral analysis readily permits analysis of multiple lead multiple group studies.

Brain↗
PubMed1975

Kinetic studies on the five principal components of normal adult human hemoglobin.

The five principal components of human hemoglobin (Ala, Alb, Alc, Ao, and A2) have been isolated by column chromatography and by preparative isoelectric focusing in gels. The isoelectric points and a number of kinetic parameters have been determined for each hemoglobin. The greatest kinetic differences are found in the binding of CO to the deoxy conformation. At pH 7, A0 and A2 are nearly identical in their overall reaction with CO, whereas the initial lag phase characteristic of crude hemolysate and A0 is greatly reduced in Ala and Alc and is essentially absent in Alb. The general effect of p-mercuribenzoate bindind on CO association is to magnify kinetic differences among the hemoglobins, diminish the initial lag phase, and increase the overall rate of CO binding. Hemoglobin Ala is anomalous in that the overall CO binding rate actually decreases after reaction with the mercurial. In terms of an Adair model with four association constants the rate constant for the binding of the first molecule of CO (1l') showed the greatest variation among the five hemoglobins, with A0 having the smallest constant, and Alb the largest. For the native hemoglobins, 1l' for Alb was more than twice that for A0; for the mercurated hemoglobins, the difference was greater than threefold. Raising the pH form 7 to 8 increases 1l' for all hemoglobins, but Ala is anomalous in having a slower overall rate for CO binding at the higher pH. At pH 9, the time course of CO binding is biphasic for all hemoglobins, with A0, the fastest, and Ala, the slowest, differing by nearly threefold in rate. The equilibrium constant for the tetramer-dimer equilibrium was determined by flash photolysis. The largest dissociation constant occurs for Ala and is 4.4 times that for A0, and 5.6 times that for Alc, the least dissociated of the hemoglobins. The overall oxygen dissociation reaction is biphasic for Ala and Alb, with the two phases differing by a factor of 5; the dissociation reactions for the other three hemoglobins appear essentially monophasic. The kinetics of dissociation of the first oxygen molecule from oxyhemoglobin are very similar for all five hemoglobins, as are the association kinetics for CN-minus and N3-minus binding to the five methemoglobins.

Adult↗
PubMed1999

Head and neck cancer: detection of recurrence with three-dimensional principal components analysis at dynamic FDG PET.

Fully automated principal components analysis (PCA) was applied to dynamic 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) positron emission tomographic (PET) images obtained in 15 patients with previously treated head and neck cancer. PCA with time-activity curves incorporated kinetic information about FDG uptake, which improved tissue characterization on FDG PET images. The combination of standardized uptake value and PCA image sets likely will improve the reliability of tumor detection in head and neck cancers.

Adult↗
PubMed2002

Quantitative analysis of polymorphic mixtures of ranitidine hydrochloride by Raman spectroscopy and principal components analysis.

Ranitidine hydrochloride exists as two polymorphs, forms I and II, both of which are used to manufacture commercial tablets. Raman spectroscopy can be used to differentiate the two forms but univariate methods of quantitative analysis of one polymorph as an impurity in the other lack sensitivity. We have applied principal components analysis (PCA) of Raman spectra to binary mixtures of the two polymorphs and to binary mixtures prepared by adding one polymorph to powdered tablets of the other. Based on absorption measurements of seven spectral regions, it was found that >97% of the spectral variation was accounted for by three principal components. Quantitative calibration models generated by multiple linear regression predicted a detection limit and quantitation limit for either forms I or II in mixtures of the two of 0.6 and 1.8%, respectively. This study demonstrates that PCA of Raman spectroscopic data provides a sensitive method for the quantitative analysis of polymorphic impurities of drugs in commercial tablets with a quantitation limit of less than 2%.

Drug Evaluation, Preclinical↗
PubMed2006

Principal components analysis of obsessive-compulsive disorder symptoms in children and adolescents.

BACKGROUND: Obsessive-compulsive disorder (OCD) has a broadly diverse clinical expression that may reflect etiologic heterogeneity. Several adult studies have identified consistent symptom dimensions of OCD. The purpose of this study was to conduct an exploratory principal components analysis of obsessive-compulsive (OC) symptoms in children and adolescents with OCD to identify improved phenotypes for future studies. METHODS: This study examined lifetime occurrence of OC symptoms included in the 13 symptom categories of the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and the Children's Yale-Brown Obsessive-Compulsive Scale (CY-BOCS). Principal components analysis with promax rotation was performed on 231 children and adolescents with OCD and compared with results of similar adult studies. RESULTS: A four-factor solution emerged explaining 59.8% of symptom variance characterized by 1) symmetry/ordering/repeating/checking; 2) contamination/cleaning/aggressive/somatic; 3) hoarding; and 4) sexual/religious symptoms. All factors included core symptoms that have been consistently observed in adult studies of OCD. CONCLUSIONS: In children and adolescents, OCD is a multidimensional disorder. Symptom dimensions are predominantly congruent with those described in similar studies of adults with OCD, suggesting fairly consistent covariation of OCD symptoms through the developmental course. Future work is required to understand changes in specific symptom dimensions observed across the life span.

Adolescent↗
PubMed2004

Principal component analysis for reduction of ocular artefacts in event-related potentials of normal and dyslexic children.

OBJECTIVE: The aim of this study was to reduce ocular artefacts in single trial event-related potentials (ERPs) recorded in normal and in dyslexic children. METHODS: ERPs were recorded during passive and active reading of centrally presented alphabetic letters and non alphabetic symbols. EEG was recorded from 10 EEG locations using the 10-20 system. Diagonal EOG from the right eye was also recorded. Principal component analysis (PCA) was applied in order to reduce ocular artefacts: the first or the second principal component (PC) was subtracted when the correlation coefficient between the component and EOG was greater or equal to 0.9 or 0.95, respectively. Performance of the method was tested on simulated and real data, on both single and averaged trials, varying EOG amplitude and artefact transmission characteristics. RESULTS: Applying the method to real recordings from normal and dyslexic children, we obtained a significant increase in the number of useful trials. In normal children we retrieved 41.0% of the rejected trials in passive and 39.1% in active reading. In dyslexic children 36.7 and 32.2% of the rejected trials in passive and active reading could be included in the respective averages. CONCLUSIONS: The method allows an increase in the number of trials suitable for averaging, a great improvement in ERP quality and a reduction in the recording time.

Artifacts↗
PubMed1997

Registration of dynamic dopamine D2 receptor images using principal component analysis.

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D2 receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P<0.001) improvements in image registration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other methods failed completely to register, particularly 123I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the chi2 of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences.

Algorithms↗
PubMed2006

Using supervised principal components analysis to assess multiple pollutant effects.

BACKGROUND: Many investigations of the adverse health effects of multiple air pollutants analyze the time series involved by simultaneously entering the multiple pollutants into a Poisson log-linear model. This method can yield unstable parameter estimates when the pollutants involved suffer high intercorrelation; therefore, traditional approaches to dealing with multicollinearity, such as principal component analysis (PCA), have been promoted in this context. OBJECTIVES: A characteristic of PCA is that its construction does not consider the relationship between the covariates and the adverse health outcomes. A refined version of PCA, supervised principal components analysis (SPCA), is proposed that specifically addresses this issue. METHODS: Models controlling for longterm trends and weather effects were used in conjunction with each SPCA and PCA to estimate the association between multiple air pollutants and mortality for U.S. cities. The methods were compared further via a simulation study. RESULTS: Simulation studies demonstrated that SPCA, unlike PCA, was successful in identifying the correct subset of multiple pollutants associated with mortality. Because of this property, SPCA and PCA returned different estimates for the relationship between air pollution and mortality. CONCLUSIONS: Although a number of methods for assessing the effects of multiple pollutants have been proposed, such methods can falter in the presence of high correlation among pollutants. Both PCA and SPCA address this issue. By allowing the exclusion of pollutants that are not associated with the adverse health outcomes from the mixture of pollutants selected, SPCA offers a critical improvement over PCA.

Air Pollutants↗
PubMed2006

Principal component analysis of DNA oligonucleotide structural data.

The microstructure of a DNA helix is characterized by several base pair and base step parameters such as twist, rise, roll, propeller twist, etc., in addition to conformational parameters such as the backbone and the glycosidic torsion angles. Among these only a few, which are independent of all others and of each other, may be used to precisely characterize the helix. The problem however is to identify these independent parameters. We have used principal component analysis to identify a relatively small set of independent parameters, with which to characterize each DNA helix. We show that these principal components clearly discriminate between A and B DNA helical types. The calculations further suggest that the microstructure of a DNA helix is better characterized using dinucleotides.

Base Sequence↗
PubMed2003

Two-dimensional infrared spectroscopy and principal component analysis studies on a new azobenzene derivative supramolecular system based on hydrogen bonds.

Infrared (IR) spectra of a supramolecular assembly with an azobenzene derivative and intermolecular hydrogen bonds have been measured in the temperature range from 30 to 200 degrees C to investigate heat-induced structural changes and thermal stability. Principal component analysis (PCA) and two kinds of two-dimensional (2D) correlation spectroscopy, variable-variable (VV) 2D and sample-sample (SS) 2D spectroscopy, have been employed to analyze the observed temperature-dependent spectral variations. The PCA and SS 2D correlation analyses have demonstrated that the complete decoupling of hydrogen bonds in the supramolecular assembly occurs between 110 and 115 degrees C, which is in good agreement with the results of a differential scanning calorimetry (DSC) study for the heating process. The PCA of the IR spectra in the region of 3600-3100 cm(-1) has illustrated that there are at least four principal components for the different NH2 and CONH species in the present supramolecular system. The VV 2D correlation spectroscopy study has provided information about the structure and strength of hydrogen bonds of NH2 and CONH groups and their temperature-dependent variations. The different species of hydrogen-bonded NH2 and CONH groups in the supramolecular system can be clarified by the VV 2D correlation analysis. The VV 2D correlation analysis has also revealed the specific order of the temperature-induced changes in the hydrogen bonds of NH2 and CONH groups.

Azo Compounds↗
PubMed2005

Evaluation of river water quality monitoring stations by principal component analysis.

The development of a surface water monitoring network is a critical element in the assessment, restoration, and protection of stream water quality. This study applied principal component analysis (PCA) and principal factor analysis (PFA) techniques to evaluate the effectiveness of the surface water quality-monitoring network in a river where the evaluated variables are monitoring stations. The objective was to identify monitoring stations that are important in assessing annual variations of river water quality. Twenty-two stations used for monitoring physical, chemical, and biological parameters, located at the main stem of the lower St. Johns River in Florida, USA, were selected for the purpose of this study. Results show that 3 monitoring stations were identified as less important in explaining the annual variance of the data set, and therefore could be the non-principal stations. In addition, the PFA technique was also employed to identify important water quality parameters. Results reveal that total organic carbon, dissolved organic carbon, total nitrogen, dissolved nitrate and nitrite, orthophosphate, alkalinity, salinity, Mg, and Ca were the parameters that are most important in assessing variations of water quality in the river. This study suggests that PCA and PFA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.

Calcium↗