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Results for “Principal Component Analysis”

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At least 19 recordsLinked to original sources

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↗

PubMedDate not supplied

Attenuation coefficients of body tissues using principal-components analysis.

Principal-components analysis is used to obtain a set of parameters for dual-energy radiography that completely describes the attenuation coefficient of any tissue over a given energy range. These parameters are the weighted averages of the densities of the elements present in a substance. Principal-components (PC) parameters are calculated for several soft tissues from measured attenuation coefficients published by Phelps et al. The calculated PC parameters are compared to the more conventional dual-energy representations of the attenuation coefficient: (1) the electron density/effective atomic number (or Compton/photoelectric) representation and (2) the equivalent water/equivalent aluminum thickness representation. The principal-components parameters represent the attenuation coefficients more accurately, and are more stable than the currently used parameters. In addition, these new parameters are sensitive to differences in the chemical composition and density, whereas the previous representations are primarily sensitive to changes in the density. It is concluded that the principal-components method provides a more sensitive and accurate indicator of changes in tissue composition than previous characterizations of the linear attenuation coefficient. The principal-components method provides a means for improving the accuracy of the characterization of tissues in dual-energy computed tomographic imaging and in dual-energy digital radiography.

Body Composition↗

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↗

PubMedDate not supplied

Principal component analysis of shape variables in adult individuals.

Human body shape variables were obtained by adjusting 34 distances between trunk/limbs, and head/face, landmarks, for an overall appropriate body size measurement. The adjustment was based on regression analysis. Principal component analysis was applied to thus defined shape variables to obtain shape dimensions in 99 normal adult males and 103 females. The first principal component either for trunk/limbs shape variables, or for head/face variables (considered separately in the analyses) is similar in both sexes in that it represents relative proportions between trunk and limb lengths and widths, and between midfacial lengths and widths, respectively. However there are appreciable differences in the succeeding components. The problem of interpretation of body shape dimensions, especially those accounting for less than 20% of the sample variance, as well as difficulties in assessing the biological meaning of dependence structures determined by principal component analysis in humans, are discussed.

Adolescent↗

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↗

PubMed1990

[Principal component analysis for microalbuminuria in patients with noninsulin-dependent, maturity-onset diabetes mellitus].

To determine causal mechanism(s) of microalbuminuria seen in patients with noninsulin-dependent diabetes mellitus (NIDDM), multivariate analysis (principal component analysis) was applied, using patient's age, disease length, fasting blood sugar level (FBS), hemoglobin A1c (HbA1c %), and presence of hypertension as variables. Albumin concentration in the first morning urine was determined by the Latex Photometric Immunoassay (LPIA), and was expressed as albumin index (AI, albumin excretion per gram creatinine). Sixty five cases who had been continuously negative or equivocal (+/-) for urinary protein by an usual paper test method were analysed. The result indicated these patients could be separated into following three groups. Group A (12 cases) showed the highest AI value, was characterized by longer disease length (greater than 10 yrs), and was thought to be in transitional phase into clinical proteinuric stage. Group B (7 cases) was characterized by poor diabetic control and normalization of the microalbuminuria might be possible by strict control measures. In Group C (14 cases), patients were in relatively early stage of the disease, and were under good diabetic control, but presence of hypertension was thought to be a provocative factor.

Adult↗

PubMed1989

[Clinical study on sweep-frequency tympanometry--data analysis using principal component analysis].

A sweep-frequency tympanometer has been developed to diagnose auditory ossicular lesions more precisely. This device measures acoustic middle ear features by changing the probe tone frequency continuously. The oscillator sweeps frequency of the probe tone from 200 to 2,500Hz 1.5 seconds. A microphone picks up the changes in the acoustic pressure and the phase in the external auditory meatus during the frequency change of the probe tone. These measurements are performed under pressures at -200 and 0 dapa, and differences of both the acoustic pressure and the phase between these two pressures are graphically displayed using a microcomputer. This device can also measure and digitally display the frequency (BHz) and the sound pressure (BdB) at the minimum point (B) of the sound pressure curve, the frequency (ZHz) at 0 crossing point (Z) of the sound pressure curve, and the frequency (PHz) and the phase difference (Pdeg) at the maximum point (P) of the phase curve. Normal ranges of these five values were obtained to establish diagnostic criteria from 200 normal ears. The normal values were as follows: BHz, 480 to 1,070Hz; BdB, -6.0 to -1.8dB; ZHz, 1,100 to 1,860Hz; PHz, 910 to 1,820Hz; and Pdeg, 16.3 to 47.9 degrees. Eight cases of ossicular dislocation and 14 cases of ossicular fixation, all of which had normal external auditory meatus and tympanic membrane, were evaluated based on these values. All the cases with ossicular dislocation and 12 cases out of 14 with ossicular fixation were correctly diagnosed.(ABSTRACT TRUNCATED AT 250 WORDS)

Acoustic Impedance Tests↗

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↗

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↗

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↗

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↗

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↗

PubMed1985

Qualitative organic analysis. I. Identification of drugs by principal components analysis of standardized thin-layer chromatographic data in four eluent systems.

Principal component analysis of standardized RF values in four eluent systems [ethyl acetate-methanol-30% ammonia (85:10:15), cyclohexane-toluene-diethylamine (65:25:10), ethyl acetate-chloroform (50:50) and acetone, with the plate dipped in potassium hydroxide solution] provided a two-component model which accounts for 73% of the total variance. The "scores" plot allowed the restriction of the range of inquiry to a few candidates. This result is of great practical significance in analytical toxicology, especially when account is taken of the cost, the time, the analytical instrumentation and the simplicity of the calculations required by the method.

Chromatography, Thin Layer↗

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↗

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↗

PubMed1986

New indices for thyroid functional status, hormone binding, and peripheral hormone metabolism. Principal component analysis of 24,000 clinical data.

Principal component analysis of three thyroid function tests, thyroxine (T4), 3,5,3'-triiodothyronine (T3), and T3 uptake (T3U), was done using 24,000 data obtained from patients with a wide range of pathophysiologic conditions related to the thyroid. The three component scores were obtained as follows: Z1 = 2.62 square root T4 + 0.63 square root T3 + 3.18 square root T3U - 32.43; Z2 = 0.91 square root T4 + 0.24 square root T3 - 4.68 square root T3U + 20.14; and Z3 = 3.94 square root T4 + 0.95 square root T3 + 0.18 square root T3U - 1.53 (T4 micrograms/dL, T3 ng/dL, T3U%). The first component (Z1) represents an apparent axis to the direction of thyroid functional status. It provides a new metabolic index putting conventional free T4 and free T3 indices together. The second component (Z2) was found to be a sensitive indicator of abnormal hormone binding. It showed a close correlation with serum concentration of thyroxine-binding globulin. The third component (Z3) represents the degree of T3 predominance over T4. Computation of these scores will facilitate the diagnosis of atypical cases in which hyper-or hypothyroidism is complicated by abnormal peripheral hormone binding and/or metabolism.

Humans↗

PubMed1990

[Multivariate analysis for the study of craniofacial structure. Selection of parameters on using the principal component analysis].

The present study was undertaken to discuss the effects of parameters on using the principal component analysis for the evaluation of the craniofacial structures. Materials consisted of lateral roentgenocephalograms of 100 adult Japanese females. They were divided into three groups by ANB angle. Linear and angular measurements were selected as parameters to apply the principal component analysis, and scattergrams made by the first and second principal component were compared. The results of the analysis were varied by the parameters. Especially there was a great difference between the real size and the value corrected by isometric method on scattergrams made by the first and second components. Results indicated the importance of using appropriate parameters for the research purpose.

Adult↗