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Results for “principal components”

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At least 19 recordsLinked to original sources

PubMed1989

Multicomponent analysis of near-infrared spectra of anesthetized rat head: (I). Estimation of component spectra by principal component analysis.

By measuring precise NIR spectra of anesthetized rat head, we determined principal components included in changes of the NIR spectra caused by acute hypoxia. There was apparent delay in reduction of cyt. aa3 as compared with Hb deoxygenation in the hypoxic period. With regard to the eigenvalues determined by principal component analysis, principal components in the NIR spectral changes caused by acute hypoxia were Hb and cyt. aa3, and contribution of the remaining biological materials to the changes in NIR spectra caused by acute hypoxia was considered to be negligible in quantitative multivariate analysis of Hb and cyt. aa3 in situ.

Animals↗

PubMed1987

Relationships between physico-chemical parameters and microbial groups in Manchego and Burgos cheeses studied by principal component analysis.

Principal component analysis was used to examine the correlations between two sets of variables, one representing physicochemical characteristics (pH, aw and NaCl, moisture and fat content) of Manchego (36 samples) and Burgos (36 samples) cheeses, and the other representing counts of several microbial groups (mesophiles, psychrotrophs, lactic acid bacteria, coliforms, enterococci, staphylococci and molds and yeasts). Thermonuclease content was also included. In addition to the expected relationships (NaCl content, moisture, aw, etc.), significant correlations between some compositional characteristics and levels of certain microorganisms were found. These correlations were dependent on the type of cheese. Thermonuclease content was positively related to enterococci and ripening (only in Manchego cheese). In contrast to former observations, no relationships were observed between coliforms and enterococci counts.

Cheese↗

PubMed1992

[Classification of congenital superior oblique palsy based on principal component analysis].

An attempt was to classify unilateral congenital superior oblique palsy principal component analysis. Each principal component was calculated by taking a linear combination of an eigenvector of the correlation matrix with a standardized original variable. The variables selected for the analysis were vertical deviation in the nine diagnostic positions of 51 cases measured by a synoptometer. The cumulative contributive percent of principal components showed that 88.5% of the variation were accounted for by the first three principal components. The first principal component accounted for 56.7% of the variation in samples indicating the extent of superior oblique palsy in which vertical deviation increases or decreases proportionately. The second principal component accounted for 20.6% of the variation of samples indicating the extent of the incomitance of vertical deviation with a vertical change of gaze. The third principal component accounted for 11.1% of the variation in the sample indicating the extent of the vertical deviation with a horizontal change of gaze.

Adolescent↗

PubMed1992

Measurement processes and spatial principal components analysis.

Spatial principal components analysis (SPCA) applied to the ongoing EEG yields factor loadings which, when mapped, consistently reveal symmetrical patterns resembling the spherical harmonics. In this paper, we consider the mechanisms responsible for these characteristic patterns. In doing so, we demonstrate that volume conduction is one of a family of processes capable of generating such patterns with SPCA. It is shown that any series of measurements on a sphere in which the covariance is only a function of measurement site angular separation (shift invariant processes) will yield the spherical harmonics as the eigenvectors or factor loadings of the covariance matrix. Simulations further indicate that this effect is robust and not determined by the geometry of the measurement sites. In situations where shift invariant signals coexist with those generated at specific sites (anatomically specific processes), such as evoked potentials and some artifacts, it is shown that the anatomically specific signals do not influence the eigenvectors of the covariance matrix in a uniform or random fashion. The factors most influenced are those whose symmetry is similar to that of the site specific signal.

Brain↗

PubMed1990

Comparing principal components analyses of evoked potentials recorded from heterogeneous groups of subjects.

Principal components analysis of evoked potentials differing between groups presents an interpretive problem, particularly in psychiatric research. Two sets of principal components and associated factor scores may appear to differ. The issue is to determine the extent to which visually differing principal components and resultant factor scores span the same factor space. Sets of evoked potentials from controls and from schizophrenics were each subjected to principal components analysis, from which factor score coefficients were computed for all subjects. This allowed determination of the extent to which (1) the two sets of basis waves were similar and (2) the factor scores resulting from the set of basis waves derived from principal components analysis of the control subjects' evoked potential data adequately represented those of the schizophrenics and vice-versa. Canonical correlation analyses indicated substantial similarities between the principal component structures (sets of basis waves). Multiple correlational analyses confirmed that the basis waves from either group spanned the other group's factor space. Factor scores from either set of basis waves were highly correlated. These results suggest that principal component structures derived from evoked potentials of a control group may be used in computing evoked potential factor scores of psychiatrically diverse populations even though the average evoked potentials of the groups may differ in several ways.

Evoked Potentials↗

PubMed1990

Multivariate data reduction by principal components, with application to neurological scoring instruments.

Principal components analysis is widely used as a practical tool for the analysis of multivariate data. The aim of this analysis is to reduce the dimensionality of a multivariate data set to the smallest number of meaningful and independent dimensions. The analysis can also provide interpretable linear functions of the original measured variables that may serve as valuable indices of variation. A brief introduction to principal components analysis is given herein, followed by an examination of a particular set of multivariate data accruing from a study of acute brain injuries in a pediatric population, in which severity of brain injury had been assessed with the Glasgow Coma Scale (CGS). Principal components analysis reveals that the GCS sum score is a particularly inefficient summarizer of information in this cohort. The determination of an objective weighting of measured variables, as provided through principal components analysis, is essential in the construction of meaningful neurological scoring instruments.

Coma↗

PubMed1992

Composite index of skeletal mass: principal components analysis of regional bone mineral densities.

Principal components analysis is a statistical method that is used to reduce and explore data to facilitate further analyses. This method was applied to bone mineral densities measured at seven sites in 109 black and 44 white women, ages 22-80, at an internal medicine clinic in urban Detroit. We excluded subjects with a history of diseases or drugs known to affect bone metabolism. Principal components analysis was used to summarize the interrelationship of the densities and yielded two major results. First, the seven site measurements were reduced to a single, composite index (PC1) of skeletal mass that accounted for 73% of the variation in density among subjects. PC1 had roughly equal weights among the sites. A second combination of the seven sites indicated that the contrast between axial and appendicular regional densities accounted for another 10% of the variation among subjects. In investigating the relationship of density to age, body mass index, and ethnic group, we found that the principal components composite index had a stronger correlation with age (r = -0.58) and with body mass index (r = 0.34) than almost all of the regional densities. Black-white differences were larger for the composite index than for any single site density. A multiple regression of the composite index on ethnicity, body mass index, and age yielded a larger R2 (0.46) than any of the individual site densities. The second principal component, although of theoretical interest, showed a minimal ability to discriminate among subjects using the three independent variables of this study.

Absorptiometry, Photon↗

PubMed1975

Selecting key parameters in pharmaceutical formulations by principal component analysis.

The role of principal component analysis in the selection of pharmaceutical formulations is presented. The objective and the procedure of the analysis are discussed in detail. The technique was successfully applied to a system consisting of 10 response variables (tablet properties). Analysis of the results showed that the first component (dissolution) and components one and two together (dissolution and disintegration) contributed 95.4 and 99.3%, respectively, to the overall information about the formulations and that eight of 10 response parameters contributed nothing further to the overall information. The results obtained from this method of analysis may be found useful for achieving economy in both cost and time of measuring response. Principal component analysis also provides a basis for understanding the underlying mechanism of the system under consideration.

Computers↗

PubMed1991

Evaluation of a principal-components tactile aid for the hearing-impaired.

Principal component analysis, a statistical data reduction technique which can be used to eliminate redundant information, has shown promising results as a speech coding strategy in auditory perceptual studies. The present study describes the development, modification, and evaluation of a principal components-based tactile aid for speech perception by the hearing-impaired. In this device, the first two principal components of an input speech signal were displayed on two-dimensional arrays of vibrators contacting either the fingertip or the forearm. Initial testing of the device with closed-set recorded speech tokens showed fair recognition performance, reaching 57% for three consonants and 56% for four vowels. Modifications to the processor algorithm designed to improve vowel recognizability resulted in higher levels of performance (66% for eight vowels). A real-time prototype was constructed implementing the revised algorithm. Live-voice testing was conducted with six normal-hearing subjects, three of whom had previous training with the Queen's University vocoder, a multichannel tactile vocoder that has shown promising results. Performance of these "trained" subjects for both single-item and connected speech tasks was excellent, equalling levels obtained with the Queen's vocoder. These results suggest that a principal components design may be a promising alternative to a vocoder strategy for a tactile aid. Results for the "naive" subjects did not reach the levels attained by the trained subjects, a finding partially attributed to the short training period available to the naive subjects. The higher level of performance for the trained subjects, together with the similarity of performance for the principal components aid and the Queen's vocoder for these subjects, suggests that they were able to transfer previous learning with the Queen's vocoder to the principal components device.

Adult↗

PubMed1989

[Principal component analysis of the masticatory motion path during gum chewing].

This study examined the characteristics of masticatory motion path using multivariate analysis. Principal component analysis was selected as the method and various important results were revealed as follows; Approximately 70% of the information describing gum chewing motion was explained by three principal components. From factor loading, the first principal component explains back and forth movement from the end of the opening phase to the closed position, the second principal component explains left to right movement at the onset of the mouth opening phase, and the third principal component explains left and right movement at about the position of maximum opening. Using scatter diagrams combining the first and second principal components, as well as the first and third principal components, we were able to recognize delicate differents among the subjects, but to the different strokes of any subject, characteristic patterns were emerged.

Chewing Gum↗

PubMed1992

Morphology of the normal visual field in a population-based random sample: principal components analysis.

I applied principal components analysis to Humphrey central 24-2 threshold values from both eyes of 304 clinically normal persons selected by simple random sample from Barbados, WI. The first component, accounting for 62 per cent of the variation, is equivalent to the average threshold value within persons. The first eigenvector, when represented by grey scale maps depicting a pair of eyes, reveals that, as average threshold increases, the visual field rises and flattens, like an umbrella that, initially closed, is simultaneously opened and thrust upwards. I verify three numerical predictions based upon this umbrella description. Much less important sources of variation involve disparity between fellow eyes, and hemimeridional and other symmetric differences within eyes. I discuss briefly possible physiologic explanatory mechanisms.

Barbados↗

PubMed1990

Characterization of premotor interneurones by their input patterns--application of principal component analysis to cat cervical interneurones.

Principal component analysis of input patterns of cat C6-C8 interneurones (300 cells) revealed that identified premotor interneurones (11 cells) activated from skin afferents and projecting to T1 motoneurones possessed a special input pattern, characterized by restricted distribution on the plane of the first (Prin 1) versus second (Prin 2) principal component (high positive values of both components). These premotor neurones were located mostly in laminae V-VI. Among other laminae V-VI cells descending in the lateral funiculus to T1 similar to such premotor neurones, there were cells distributed similarly on the Prin 1-2 plane. Further, a majority of interneurones antidromically activated from the T1 motor nucleus at low thresholds also showed a distribution on the plane similar to the premotor neurones. We suggest that premotor neurones of this input pattern constitute a major group among laminae V-VI premotor neurones projecting to T1.

Animals↗

PubMedDate not supplied

Chemometric differentiation of raw and commercial milk by trace elements using principal component analysis.

Nine trace elements (Cr, Mn, Fe, Ni, Cu, Zn, Mo, Cd, and Pb) were determined in the dissolved ash of 36 samples of raw milk. The distribution of the concentration of each element was first investigated by means of a test of normality. The matrix of the correlation between the concentrations of the elements was then used as a starting matrix for principal component analysis. Nine variables were reduced to four principal components, accounting for 75% of the total variance. The biophilic elements Mn-Fe and Cu-Mo were positively associated with the first two principal components, while Cr was correlated to the third and Ni and Cd with the fourth principal component. Pb and Zn are both negatively correlated to the first principal component. Comparison with 42 samples of a commercial milk, by using a two-dimensional plot of the principal component scores, rendered possible the differentiation between raw and commercial milk.

Animals↗

PubMed1988

Assessment of biological age by principal component analysis.

A method of assessing biological age by the application of principal component analysis is reported. Healthy individuals (462) randomly selected from about 6000 men who had taken a 2-day health examination were studied. Out of the 30 physiological variables examined in routine check-ups, 11 variables were selected as suitable for the assessment of biological age based on the results of factor analysis and the physiological meaning of each test. This variable set was then submitted to principal component analysis, and the 1st principal component obtained from this analysis was used as an equation for assessing one's biological age. However, the biological age calculated from this equation is expressed as a score, so the estimated score was transformed to years (biological age) using the T-score idea. The biological age estimated by this method is practically useful and theoretically valid in contrast with the multiple regression model, because this approach eliminates and overcomes the following 2 big problems of the multiple regression model: (1) the distortion of the individual biological age at the regression edges; and (2) a theoretical contradiction in that a perfect model will merely be predicting the subject's chronological age, not his biological age.

Adult↗

PubMed1988

Multiple regression and principal components analysis of puberty and growth in cattle.

Multiple regression and principal components analyses were employed to examine relationships among pubertal and growth characters. Records used were from 424 bulls and 475 heifers produced by a diallel mating of Angus, Brahman, Hereford, Holstein and Jersey breeds. Characters studied were age, weight and height at puberty and measurements of weight and hip height from 9 to 21 mo of age; pelvic measurements of heifers also were included. Measurements of weight and height near 1 yr of age were related most highly to pubertal age, weight adn height. Larger size near 1 yr of age was associated with younger, larger animals at puberty. Growth rate was associated with pubertal characters before, but not after, adjustment for effects of breed-type. Principal components of the variation of pubertal and growth characters among animals were strongly related to both weight and height. The majority of the variation among breed-types was due to height. Characteristic vectors of principal components describing the variation of bulls and heifers were strikingly similar. The variance-covariance structure of pubertal characters was essentially the same for both sexes even though the mean values of the characters differed.

Animals↗

PubMed1979

Craniofacial morphology: a principal component analysis.

A series of 56 measurements was derived from lateral cephalometric radiographs of a large sample of subjects. These measurements were subjected to a principal-component analysis which resulted in a series of six components (factors). These factors, represented in general terms and in rank order of their percentage sample variability were as follows: Factor 1. Vertical facial characteristics Factor 2. Anteroposterior aspects of facial morphology Factor 3. Midfacial and dental protrusion Factor 4. Relationship of the mandible and dentition to the profile Factor 5. Horizontal base-line relationships (internal or deep) Factor 6. Maxillary incisor relationships These principal components and the variables contained within them were shown to have sex and age interactions. A longitudinal study of the principal component changes with age was then undertaken. Demonstrable age changes were verified for Factors 1, 2, and 3, and Factors 1 and 3 were observed to show patterns of change which were statistically different from each other and the remaining principal components. An orthodontically treated sample of patients was also assessed for factor changes. Factors 1 and 2 were found to show statistically reliable changes resulting from treatment and/or growth. The remaining four factors showed no statistically supportable alteration. The data-reduction method involving a principal-component analysis would seem to have potential research and clinical applications.

Adolescent↗

PubMed1992

Principal components and multidimensional scaling of auditory and visual event-related potential topography.

Principal components analysis and metric multidimensional scaling were used to assess auditory and visual event-related potential topography in healthy late-middle-aged and elderly adults (n = 20). Binaurally elicited auditory evoked potentials and full-field checkerboard pattern reversal visual event-related potentials were recorded from 28 scalp sites. The zero-lag, cross-correlations of all waveforms for 300 msec post-stimulus epochs were obtained and separate analyses of the auditory and visual data were performed. Ultimately, three of the four dimensions identified in the principal components and multidimensional scaling solutions were similar and represented electrode site differences in (1) the anterior-posterior plane; (2) laterality; and, (3) the proximal-distal relation to midline. The remaining multidimensional scaling axis appeared to reflect effects specific to the modality of stimulation. Under auditory stimulation, the temporal and central-parietal sites were distinct from other scalp regions, whereas under visual stimulation, the occipital and frontal sites were distinctive. Although the results of the principal component analyses were conceptually similar to the multidimensional scaling outcomes, there were consistent differences between them. The findings provide empirical support for the validity of these multivariate methods in topographic analysis.

Acoustic Stimulation↗

PubMed1989

Assessment of air pollution sources in an industrial atmosphere using principal component and multilinear regression analysis.

Aerosol samples collected in the industrial area of Estarreja, Portugal, were used to assess the source classes responsible for the particulate levels observed in the local atmosphere. Principal Component Analysis was applied separately to the concentrations of aerosol constituents and meteorological variables to obtain the number of Principal Components and to verify the influence of weather conditions on ambient air quality. The technique led to the conclusion that soil and transport emissions represent important aerosol sources even in this industrial environment. The quantitative contribution of each source class was calculated using Multilinear Regression Analysis; 37% of the aerosol mass had a soil origin, 8% was from sea spray, 18% resulted from transport emissions and 24% contained ammonium salts. Twelve percent of total suspended particle (TSP) mass could not be explained by any of the six Principal Components retained. Ammonium salts and two other minor Principal Components seem to result mainly from industrial emissions. More specific information about the contribution of each particular source was not possible with this technique.

Air Pollutants, Occupational↗