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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↗

PubMedDate not supplied

Interpretation of laboratory results using multidimensional scaling and principal component analysis.

Principal component analysis (PCA) and multidimensional scaling (MDS) are a set of mathematical techniques which uncover the underlying structure of data by examining the relationships between variables. Both MDS and PCA use proximity measures such as correlation coefficients or Euclidean distances to generate a spatial configuration (map) of points where distances between points reflect the relationship between individuals with their underlying set of data. Multidimensional scaling, when compared to PCA, gives more readily interpretable solutions of lower dimensionality and does not depend on the assumption of a linear relationship between variables. Both MDS and PCA were applied to electrolyte profiles of patients with acute renal failure and patients without apparent disease. The MDS was superior to PCA in separating renal patients from normal patients. The one-dimensional and two-dimensional solutions of MDS and PCA were compared.

Acute Kidney Injury↗

PubMed1987

Comparison of principal components computed with principal factor analysis on the basis of averaged and single-trial ERPs using the Fischer-Roppert procedure.

Averaged and single-trial event-related potentials (ERPs) were analysed using the Principal Factor Analysis (PFA) with following varimax rotation, and the results were compared. The correspondence between the matrices of factor loadings was tested by means of the Fischer-Roppert-procedure. For the application of PFA, a preceding averaging to improve the signal to noise ratio is not necessary. If the group of subjects is homogeneous enough, the analysis of the single-trial ERPs provides results sufficient to investigate the component structure of the ERP. The ERPs from all subjects can be described with one common component structure.

Arousal↗

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↗

PubMed1987

Principal-component amplitude compression for the hearing impaired.

Principal-component amplitude compression, a means for matching speech to the reduced dynamic range in sensorineural hearing impairments, is a multiband approach aimed at preserving details of spectral shape while reducing overall level variation. The effect of compression has been studied for the first and second principal components (PC1 an PC2) of the short-term speech spectrum, which are roughly representative of overall level and spectral tilt, respectively. Compression of PC1 roughly equalizes consonant and vowel levels while compression of PC2 provides time-varying high-frequency emphasis. The effect on speech intelligibility of sensorineural hearing-impaired listeners of two principal-component compression system implementations, compression of PC1 and compression of both PC1 and PC2, was compared to that of linear amplification (LA), independent compression of multiple bands (MBC), and wideband compression (WC). Results indicate that compression of overall level as provided by compression of PC1 and WC improved intelligibility relative to LA over a 10- to 15-dB range of input levels. While MBC was beneficial in some cases, it did not provide higher intelligibility than WC. Compression of PC2 did not benefit but rather degraded performance relative to LA. Error analyses and band-level measurements indicate that the highest intelligibility is obtained when audibility is improved and the relative spectral shapes of different speech sounds are preserved.

Auditory Threshold↗

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↗

PubMed1987

Individualized principal component analysis of endocrine circannual variability.

The technique of principal component (PC) analysis (PCA) of multivariate observations is a method that allows dimension reduction of multivariate data for further analysis. It is here introduced as a means of selecting chronobiologically important variables that can be further studied by an analysis of variance. The use of PCA is illustrated for a study of major temporal sources of human endocrine variability. Contributions to temporal variability by seven steroidal and six nonsteroidal hormones are compared in samples available at 100-min intervals for 24 hr in three seasons for each of three clinically healthy individuals: an adolescent woman, a menstrually cycling woman, and a postmenopausal woman. On an individualized basis, it is ascertained that the first principal component, a new variable, is primarily determined by steroids and that PCA can single out variables displaying interseasonal (circannual) differences validated as statistically significant by a subsequent analysis of variance. The variables here scrutinized and identified as contributing to the PC, however, need not all differ with statistical significance along the scale of the seasons. The steroids contributing the first principal component are DHEA-S and an estrogen in all three individuals studied, cortisol and aldosterone in two of them, and 17-OH progesterone in one case.

Adolescent↗

PubMed1987

Principal component analysis for the identification of pollution sources in mussel survey by trace metals.

Principal component analysis has been applied to analyze the correlation matrix obtained from a 8 X 43 data matrix. The 8 trace metals are Mn, Co, Ni, Cu, Zn, Cd, Hg, Pb, which are contained in the soft part of mussels (Mytilus galloprovincialis Lamarck). Mussels were sampled from two sites in the Gulf of Trieste. In both samples, 76-78% of the total variance is explained by the four principal components. The orthogonally rotated factor matrix indicates that Co and Ni are bonded to the first principal component and Cd and Pb to the first (site 2) or second principal component (site 1). The origin of trace metals in the soft part of mussels from the Gulf of Trieste is discussed.

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↗