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Principal components analysis of the photoresponse nonuniformity of a matrix detector.

The principal component analysis is used to identify and quantify spatial distributions of relative photoresponse as a function of the exposure time for a visible CCD array. The analysis shows a simple way to define an invariant photoresponse nonuniformity and compare it with the definition of this invariant pattern as the one obtained for long exposure times. Experimental data of radiant exposure from levels of irradiance obtained in a stable and well-controlled environment are used.

Journal Article↗

Principal components analysis as a tool for the optimization of experimental conditions.

Principal Components Analysis is used to display the variation in a data set consisting of the free amino-acid patterns in serum from dogs as a function of fasting time. Inhomogeneities found in the data set resulted in an optimization of the experimental conditions. Moreover important observations concerning sampling time and biological variability could be made.

Amino Acids↗

Dynamic electromyography. I. Numerical representation using principal component analysis.

A complete description of human gait requires consideration of linear and temporal gait parameters such as velocity, cadence, and stride length, as well as graphic waveforms such as limb rotations, forces, and moments at the joints and phasic activity of muscles. This results in a large number of interactive parameters, making interpretation of gait data extremely difficult. Statistical pattern recognition techniques can simplify this problem. For this approach to be successful, first it is necessary to reduce the number of interactive parameters to a manageable set. In this study, we present an application of principal component analysis as a means for representing graphic waveforms in a parsimonious manner. In particular, we concentrate on representing the phasic muscle activity recorded using surface electrodes from ten major muscles of the lower extremity of 35 normal subjects during level walking. A 32 point vector is created in which each point of the vector represents the normalized area under the curve of a portion of rectified and smoothed electromyographic signal, expressed as a function of gait cycle. Principal components are computed and the first few weighting coefficients are retained as features to represent the original EMG data. We show that the corresponding basis vectors span parts of the gait cycle where the most variability between individual subjects exists. We also show that the basis vectors can be used to represent the EMG data of subjects not originally used to generate the basis vectors.

Adolescent↗

Principal component similarity analysis of Raman spectra to study the effects of pH, heating, and kappa-carrageenan on whey protein structure.

Raman spectroscopy was used to elucidate structural changes of beta-lactoglobulin (BLG), whey protein isolate (WPI), and bovine serum albumin (BSA), at 15% concentration, as a function of pH (5.0, 7.0, and 9.0), heating (80 degrees C, 30 min), and presence of 0.24% kappa-carrageenan. Three data-processing techniques were used to assist in identifying significant changes in Raman spectral data. Analysis of variance showed that of 12 characteristics examined in the Raman spectra, only a few were significantly affected by pH, heating, kappa-carrageenan, and their interactions. These included amide I (1658 cm(-1)) for WPI and BLG, alpha-helix for BLG and BSA, beta-sheet for BSA, CH stretching (2880 cm(-1)) for BLG and BSA, and CH stretching (2930 cm(-1)) for BSA. Principal component analysis reduced dimensionality of the characteristics. Heating and its interaction with kappa-carrageenan were identified as the most influential in overall structure of the whey proteins, using principal component similarity analysis.

Carrageenan↗

Application of principal components analysis to 1H-NMR data obtained from propolis samples of different geographical origin.

Propolis is a widely used natural remedy and a range of biological activities have been attributed to it. The chemical composition of propolis is highly variable and its quality is often controlled on the basis of one or two marker compounds. In order to progress towards a method for the quality control of this complex material, HPLC and 1H-NMR approaches as methods of quality control have been compared. HPLC analyses of 43 samples of propolis were carried out and six marker compounds were quantified in each sample. The same samples were analysed using 1H-NMR and the spectra were then converted into their first derivative forms and digitised using the software application MestRe-C. The digitised data were subjected to principal component analysis using the software application Simca-P. It was found that the chemical composition of propolis mapped well according to the geographical origins of the samples studied when the first three principal components were used to display them. In addition, each sample was assessed for anti-oxidant activity, and the results were then overlaid onto the sample groupings according to 1H-NMR data. It was observed that anti-oxidant properties also mapped quite well according to geographical origin.

Geography↗

[Application of PCA (Principal Components Analysis) for the estimation of smoking effect on the occurrence of trace elements in women's gallstones].

The PCA (Principal Components Analysis) was used to estimation of the role of smoking in the changes of elements contents in gallstones. The concentration of given elements were determined by ICP-AES method. It was stated that smoking regardless of sex, influence on the occurrence of elements in hydroxyapatites of gallstones, in particular it concerns changes of As, Pb, Zn, Se, Ti contents. PCA method let to describe the role of selected elements in entire chemical composition of gallstones coming from smoking women.

Arsenic↗

Adaptive multiscale principal component analysis for on-line monitoring of a sequencing batch reactor.

In recent years, multiscale monitoring approaches, which combine principal component analysis (PCA) and multi-resolution analysis (MRA), have received considerable attention. These approaches are potentially very efficient for detecting and analyzing diverse ranges of faults and disturbances in chemical and biochemical processes. In this work, multiscale PCA is proposed for fault detection and diagnosis of batch processes. Using MRA, measurement data are decomposed into approximation and details at different scales. Adaptive multiway PCA (MPCA) models are developed to update the covariance structure at each scale to deal with changing process conditions. Process monitoring by a unifying adaptive multiscale MPCA involves combining only those scales where significant disturbances are detected. This multiscale approach facilitates diagnosis of the detected fault as it hints to the time-scale under which the fault affects the process. The proposed adaptive multiscale method is successfully applied to a pilot-scale sequencing batch reactor for biological wastewater treatment.

Algorithms↗

New approach based on fuzzy logic and principal component analysis for the classification of two-dimensional maps in health and disease. Application to lymphomas.

Two-dimensional (2D) electrophoresis is the most wide spread technique for the separation of proteins in biological systems. This technique produces 2D maps of high complexity, which creates difficulties in the comparison of different samples. The method proposed in this paper for the comparison of different 2D maps can be summarised in four steps: (a) digitalisation of the image; (b) fuzzyfication of the digitalised map in order to consider the variability of the two-dimensional electrophoretic separation; (c) decoding by principal component analysis of the previously obtained fuzzy maps, in order to reduce the system dimensionality; (d) classification analysis (linear discriminant analysis), in order to separate the samples contained in the dataset according to the classes present in said dataset. This method was applied to a dataset constituted by eight samples: four belonging to healthy human lymph-nodes and four deriving from non-Hodgkin lymphomas. The amount of fuzzyfication of the original map is governed by the sigma parameter. The larger the value, the more fuzzy theresulting transformed map. The effect of the fuzzyfication parameter was investigated, the optimal results being obtained for sigma = 1.75 and 2.25. Principal component analysis and linear discriminant analysis allowed the separation of the two classes of samples without any misclassification.

Electrophoresis, Gel, Two-Dimensional↗

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↗

[Whole mouth gustatory test (Part 1)--basic considerations and principal component analysis].

The whole mouth method gustatory test is a simple gustatory test. It can be applied easily, and can be used to assess overall taste which a subject is supposed to be feeling. In the present study, a new series of taste solutions for the use in the whole mouth gustatory test was prepared. In order to determine the normal range of gustation and relationships between each taste solution, gustatory threshold tests were conducted on 123 healthy volunteers (17-22 years old). The series of taste solutions represented 4 tastes, i.e., sweet, salty, sour, and bitter, and was prepared using sucrose, salt, tartaric acid and quinine, respectively. Recognition thresholds measured for each taste solution yielded normal ranges i.e., 0.0165 mol/l for the sweet solution, 0.0316 mol/l for the salty solution, 0.000734 mol/l for the sour solution, and 0.0000203 mol/l for the bitter solution which were almost the same as those published. The average thresholds obtained for all solutions were at almost always at the middle concentration level, i.e., level 6. Thus, this method can serve as a standard method of performing the whole mouth taste test. Principal component analysis of the thresholds obtained revealed that the primary component mainly demonstrated taste detection function, and that it could more or less be represented by the simple sum of each detection threshold. The analysis also showed that approximately 90% of taste threshold variation is explained by the 4 principal components. These findings provide further evidence that these four tastes comprise the basic elements of taste.

Adolescent↗

Determination of the residue-specific 15N CSA tensor principal components using multiple alignment media.

The individual components of the backbone (15)N CSA tensor, sigma(11), sigma(22), sigma(33), and the orientation of sigma(11) relative to the NH bond described by the angle beta have been determined for uniformly labeled (15)N, (13)C ubiquitin from partial alignment in phospholipid bicelles, Pf1 phage, and poly(ethylene glycol) by measuring the residue-specific residual dipolar couplings and chemical shift deviations. No strong correlation between any of the CSA tensor components is observed with any single structural feature. However, the experimentally determined tensor components agree with the previously determined average CSA principal components [Cornilescu and Bax (2000) J. Am. Chem. Soc. 122, 10143-10154]. Significant deviations from the averages coincide with residues in beta-strand or extended regions, while alpha-helical residue tensor components cluster close to the average values.

Anisotropy↗

Chemometric treatment of vanillin fingerprint chromatograms. Effect of different signal alignments on principal component analysis plots.

This study describes the chemometric treatment of vanillin fingerprint chromatograms to distinguish vanillin from different sources. Prior to principal component analysis, which is used to discriminate vanillin from different origins, the fingerprints are aligned. Three alignment algorithms are tested, correlation optimized warping (COW), target peak alignment (TPA) and semi-parametric time warping (STW). The performance of the three algorithms is evaluated and the effect of the different alignments on the PCA score plots is investigated. The alignment obtained with STW differs somewhat from that with COW and TPA. However, equivalent score plots were obtained regarding the different vanillin groups.

Algorithms↗

Principal component analysis of evoked responses and the effects of alcohol on the geniculo-striate system of the monkey.

This study was designed to test the effects of alcohol on visual evoked potentials in nonhuman primates performing a cognitive task. Flash evoked potentials were recorded from monkeys involved in a delayed matching-to-sample (DMS) paradigm in which the flash served as an alerting signal before each trial. Event-related potentials were recorded from the lateral geniculate nucleus and homolateral striate cortex before, during, and after intravenous administration of saline or ethanol (0.25, 0.5, 1.0, and 2.0 g/kg). Average evoked potentials (AEPs) were computed. Residual waveforms were obtained by subtracting the predrug AEP from postdrug AEPs. A principal component analysis was employed to define the alcohol alterations on the evoked responses. In the analysis each AEP was represented by 40 time points spaced 12 msec apart. These reduced representations of the AEP were entered in the variance-covariance matrix calculations. The first five eigenvectors were computed and plotted. Alcohol produced the greatest variance in the AEPs at the two highest dose levels. So the data were grouped together into three experimental categories: saline, low-dose (0.25-0.5 g/kg) and high-dose (1.0-2.0 g/kg). A correlation template, representing each category, was computed by correlating individual eigenvectors with each sequential average composed of 10 individual evoked potentials in the 200 trials of an experimental session. Alcohol affected the state vector from the brain by loading the correlation coefficient in the opposite direction following alcohol administration in two principal components. One or two of the eigenvectors significantly (P less than 0.01) shifted in geniculate nucleus, indicating that either the nucleus or a previous station was affected by alcohol. In comparison, three or more eigenvectors from striate cortex were shifted significantly following alcohol injection. This difference may be explained by the effect of alcohol on multisynaptic brain structures, including the brain-stem reticular formation, which in turn influenced the cortex.

Animals↗

Assessing local influence in principal component analysis with application to haematology study data.

In many medical and health studies, high-dimensional data are often encountered. Principal component analysis (PCA) is a commonly used technique to reduce such data to a few components that includes most of the information provided by the original data. However, PCA is known to be very sensitive to some abnormal observations. Therefore, it is essential to assess such sensitivity in PCA. In this paper, the assessments of local influence based on generalized influence function are developed under the case-weights and additive perturbation schemes, along with a discussion of the perturbation scheme and the generalized influence function approach. When perturbing different variables of the data, it is noted that the directions of the largest joint local influence for the eigenvalues are all the same. Moreover, these directions are completely determined by the score values of the observations, to which an approximate cut-off point is given. The proposed methods are applied to analyse a set of haematology study data for illustration. Results add new insights in finding influential observations in the studied data set.

Health Status↗

[Demonstration by the analysis of principal components of the of the equivalence of two models of clonal survival].

The principal component analysis of 21 chlorella cell survival curves, adjusted by one-hit and two-hit target models, lead to quite similar projections on the principal plan: the homologous parameters of these models are linearly correlated; the reason for the statistical equivalence of these two models, in the present state of experimental inaccuracy, is revealed.

Chlorella↗

The relationship between anxiety and depression. A review of some principal component analytic studies.

Forty studies published between 1934 and 1977 which used principal component analysis of symptoms, personality or illness features in patients with affective disorders were examined. Factor clusters or dimensions indicative of anxiety and depression were evident in each study. Visual plots on the first 2 components in each of 6 studies selected as being representative of the 40 are included in this paper. The implications arising from semantic confusion are discussed.

Anxiety Disorders↗

Identification of PM sources by principal component analysis (PCA) coupled with wind direction data.

The effectiveness of combining principal component analysis (PCA) with multi-linear regression (MLRA) and wind direction data was demonstrated in this study. PM data from three grain-size fractions from a highly industrialised area in Northern Spain were analysed. Seven independent PM sources were identified by PCA: steel (Pb, Zn, Cd, Mn) and pigment (Cr, Mo, Ni) manufacture, road dust (Fe, Ba, Cd), traffic exhaust (P, OC + EC), regional-scale transport (, , V), crustal contributions (Al2O3, Sr, K) and sea spray (Na, Cl). The spatial distribution of the sources was obtained by coupling PCA with wind direction data, which helped identify regional drainage flows as the main source of crustal material. The same analysis showed that the contribution of motorway traffic to PM10 levels is 4-5 microg m-3 higher than that of local traffic. The coupling of PCA-MLRA with wind direction data proved thus to be useful in extracting further information on source contributions and locations. Correct identification and characterisation of PM sources is essential for the design and application of effective abatement strategies.

Air Pollutants↗

Headspace-solid-phase microextraction fast GC in combination with principal component analysis as a tool to classify different chemotypes of chamomile flower-heads (Matricaria recutita l.).

Headspace-solid-phase microextraction gas chromatography-principal component analysis (HS-SPME GC-PCA) is proposed as a complementary or alternative method to essential oil (EO) GC-PCA in order to discriminate between flower-heads of chamomile of different chemotypes. Ninety-two EOs and the headspaces sampled by HS-SPME of the corresponding chamomile flower-heads were examined by conventional GC and fast GC (F-GC) and the results submitted to statistical analysis by PCA. HS-SPME F-GC-PCA showed itself to be a rapid technique by which to distinguish chamomile flower-head chemotypes a produced results in agreement with the accepted EO classification. Using this method, the analysis time was reduced from at least 4.5 h with EO conventional GC to less than 1 h with HS-SPME F-GC. This approach can thus successfully be used as an analytical decision maker in order to reduce the number of time-consuming EO conventional GC analyses by limiting them to those samples that cannot unequivocally be classified. The EO conventional GC and HS-SPME F-GC results of PCA were very uniform, but they did not provide quantitative correlations between the components as determined by the two methods. A different statistical approach and a larger number of samples will be needed in order to correlate components in the headspace sampled by SPME and those in the corresponding EO quantitatively through a function.

Chromatography, Gas↗