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At least 379 records · Page 21Linked to original sources
PubMed2001

Analysis of patterns of food intake in nutritional epidemiology: food classification in principal components analysis and the subsequent impact on estimates for endometrial cancer.

OBJECTIVE: To assess the effect of different methods of classifying food use on principal components analysis (PCA)-derived dietary patterns, and the subsequent impact on estimation of cancer risk associated with the different patterns. METHODS: Dietary data were obtained from 232 endometrial cancer cases and 639 controls (Western New York Diet Study) using a 190-item semi-quantitative food-frequency questionnaire. Dietary patterns were generated using PCA and three methods of classifying food use: 168 single foods and beverages; 56 detailed food groups, foods and beverages; and 36 less-detailed groups and single food items. RESULTS: Classification method affected neither the number nor character of the patterns identified. However, total variance explained in food use increased as the detail included in the PCA decreased (approximately 8%, 168 items to approximately 17%, 36 items). Conversely, reduced detail in PCA tended to attenuate the odds ratio (OR) associated with the healthy patterns (OR 0.55, 95% confidence interval (CI) 0.35-0.84 and OR 0.77, 95% CI 0.49-1.20, 168 and 36 items, respectively) but not the high-fat patterns (OR 0.95, 95% CI 0.57-1.58 and OR 0.85, 0.51-1.40, 168 and 36 items, respectively). CONCLUSIONS: Greater detail in food-use information may be desirable in determination of dietary patterns for more precise estimates of disease risk.

Adult↗
PubMed2004

Identification of Tibicen cicada species by a Principal Components Analysis of their songs.

Specific identification of three Tibicen cicadas, T. japonicus, T. flammatus and T. bihamatus, by their chirping sounds was carried out using Principal Components Analysis (PCA). High quality recordings of each species were used as the standards. The peak and mean frequencies and the pulse rate were used as the variables. Out of 12 samples recorded in the fields one fell in the vicinity of T. japonicus and all other were positioned near T. bihamatus. Then the cluster analysis of the PCA scores clearly separated each species and allocated the samples in the same way.

Acoustic Stimulation↗
PubMed2000

Three-mode principal components analysis: choosing the numbers of components and sensitivity to local optima.

A method that indicates the numbers of components to use in fitting the three-mode principal components analysis (3MPCA) model is proposed. This method, called DIFFIT, aims to find an optimal balance between the fit of solutions for the 3MPCA model and the numbers of components. The achievement of DIFFIT is compared with that of two other methods, both based on two-way PCAs, by means of a simulation study. It was found that DIFFIT performed considerably better than the other methods in indicating the numbers of components. The 3MPCA model can be estimated by the TUCKALS3 algorithm, which is an alternating least squares algorithm. In a study of how sensitive TUCKALS3 is at hitting local optima, it was found that, if the numbers of components are specified correctly, TUCKALS3 never hits a local optimum. The occurrence of local optima increased as the difference between the numbers of underlying components and the numbers of components as estimated by TUCKALS3 increased. Rationally initiated TUCKALS3 runs hit local optima less often than randomly initiated runs.

Algorithms↗
PubMed1994

A principal components analysis of the Dissociative Experiences Scale in a substance abuse population.

A principal components analysis with a varimax rotation was performed on the Dissociative Experiences Scale using a substance abuse population. Results based on a combination of the eigenvalue greater than 1 criterion and factor interpretability indicated that a four-factor solution best fit the data. Implications for interpreting the Dissociative Experiences Scale with substance abusers were discussed.

Adult↗
PubMed1987

The utility of principal component analysis for the image display of brain lesions. A preliminary, comparative study.

Principal component analysis (PCA), a common tool from multivariate statistical analysis, has been implemented into the computer display system of a MR imaging device. PCA allows the calculation of images in which the information in a defined region of interest inherent in the basic acquired images is condensed. PCA image calculation has been applied to acquired MR studies of 13 patients with brain lesions. The appearance of the brain lesions on the resultant PCA images was scored in comparison to the acquired images before and after administration of Gd-DTPA as well as to other calculated images including T1, T2, hydrogen density, and contrast-optimized images. The conspicuity of a lesion and the number of distinguishable components within a lesion were slightly superior on PCA than on the acquired images. PCA is an analytical tool for MR imaging that should be helpful in revealing information that is inherent in, but not readily visible on, standard acquired MR images.

Brain Neoplasms↗
PubMed1992

Unrestricted principal components analysis of brain electrical activity: issues of data dimensionality, artifact, and utility.

Principal components analysis (PCA) was performed on the 1536 spectral and 2944 evoked potential (EP) variables generated by neurophysiologic paradigms including flash VER, click AER, and eyes open and closed spectral EEG from 202 healthy subjects aged 30 to 80. In each case data dimensionality of 1500 to 3000 was substantially reduced using PCA by magnitudes of 20 to over 200. Just 20 PCA factors accounted for 70% to 85% of the variance. Visual inspection of the topographic distribution of factor loading scores revealed complex loadings across multiple data dimensions (time-space and frequency-space). Forty-two non-artifactual factors were successful in classifying age, gender, and a separate group of 60 demented patients by linear discriminant analysis. Discrimination of age and gender primarily involved EP derived factors, whereas dementia primarily involved EEG derived factors. Thirty-eight artifactual factors were identified which, alone, could not discriminate age but were relatively successful in discriminating gender and dementia. The need to parsimoniously develop real neurophysiologic measures and to objectively exclude artifact are discussed. Unrestricted PCA is suggested as a step in this direction.

Adult↗
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↗
PubMed1999

A loss function approach to model selection in nonlinear principal components.

The nonlinear transformation of the input variables that characterises the first nonlinear principal component is modelled as a linear sum of radially-symmetric kernel functions. It is shown that the parameters of the variance maximising transformation may be obtained through the minimisation of a loss function measuring departure from homogeneity. An alternating least squares algorithm is given. This is used as the basis of a cross-validation routine for model selection.

Journal Article↗
PubMedDate not supplied

Principal components representation of the two-dimensional coronal tongue surface.

This paper uses principal components (PC) analysis to represent coronal tongue contours for the 11 vowels of English in two consonant contexts (/s/, /l/), based upon five replicated measurements in three sessions for each of 6 subjects. Curves from multiple sessions and speakers were overlaid before analysis onto a common (x, y) coordinate system by extensive preprocessing of the curves including: extension (padding) or truncation within session, translation, and truncation to a common x range. Four PCs plus a mean level allow accurate representation of coronal tongue curves, but PC shapes depend strongly on the degree of padding or truncation. The PCs successfully reduced the dimensionality of the curves and reflected vowel height, consonant context, and physiological features.

Adult↗
PubMed1996

Principal components analysis of the Physical Self-Efficacy Scale for a black sample.

Principal components analysis of an intercorrelation matrix for the Physical Self-efficacy Scale in an all black sample of 320 mostly confirmed the original validation study of Ryckman, Robbins, Thornton, and Cantrell who used a wholly white sample; however, the analysis identified items with factor loadings at criterion on more than one factor, one item that loaded on a different subscale, and additional factors. It is unknown whether differences in this sample are attributable to race or other influences. Further investigation is suggested.

Adolescent↗
PubMed2006

[Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model].

A new method for the discrimination of varieties of apple by means of near infrared spectroscopy (NIRS) was developed. First, principal component analysis (PCA) was used to compress thousands of spectral data into several variables and describe the body of spectra, the analysis suggested that the cumulate reliabilities of PC1 and PC2 (the first two principle components) were more than 98%, and the 2-dimentional plot was drawn with the scores of PC1 and PC2. It appeared to provide the best clustering of the varieties of apple. The loading plot was drawn with PC1 and PC2 through the whole wavelength region. The fingerprint spectra, which were sensitive to the variety of apple, were obtained from the loading plot. The fingerprint spectra were applied as ANN-BP inputs. Seventy five samples from three varieties were selected randomly, then they were used to build discrimination model. This model was used to predict the varieties of 15 unknown samples; the distinguishing rate of 100% was achieved. This model is reliable and practicable. So the present paper could offer a new approach to the fast discrimination of varieties of apple.

Malus↗
PubMed2005

Quince (Cydonia oblonga miller) fruit characterization using principal component analysis.

This paper presents a large amount of data on the composition of quince fruit with regard to phenolic compounds, organic acids, and free amino acids. Subsequently, principal component analysis (PCA) is carried out to characterize this fruit. The main purposes of this study were (i) the clarification of the interactions among three factors-quince fruit part, geographical origin of the fruits, and harvesting year-and the phenolic, organic acid, and free amino acid profiles; (ii) the classification of the possible differences; and (iii) the possible correlation among the contents of phenolics, organic acids, and free amino acids in quince fruit. With these aims, quince pulp and peel from nine geographical origins of Portugal, harvested in three consecutive years, for a total of 48 samples, were studied. PCA was performed to assess the relationship among the different components of quince fruit phenolics, organic acids, and free amino acids. Phenolics determination was the most interesting. The difference between pulp and peel phenolic profiles was more apparent during PCA. Two PCs accounted for 81.29% of the total variability, PC1 (74.14%) and PC2 (7.15%). PC1 described the difference between the contents of caffeoylquinic acids (3-O-, 4-O-, and 5-O-caffeoylquinic acids and 3,5-O-dicaffeoylquinic acid) and flavonoids (quercetin 3-galactoside, rutin, kaempferol glycoside, kaempferol 3-glucoside, kaempferol 3-rutinoside, quercetin glycosides acylated with p-coumaric acid, and kaempferol glycosides acylated with p-coumaric acid). PC2 related the content of 4-O-caffeoylquinic acid with the contents of 5-O-caffeoylquinic and 3,5-O-dicaffeoylquinic acids. PCA of phenolic compounds enables a clear distinction between the two parts of the fruit. The data presented herein may serve as a database for the detection of adulteration in quince derivatives.

Amino Acids↗
PubMed1999

Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework.

Principal component analysis (PCA) is one of several structure-seeking multivariate statistical techniques, exploratory as well as inferential, that have been proposed recently for the characterization and detection of activation in both PET and fMRI time series data. In particular, PCA is data driven and does not assume that the neural or hemodynamic response reaches some steady state, nor does it involve correlation with any pre-defined or exogenous experimental design template. In this paper, we present a generalized linear systems framework for PCA based on the singular value decomposition (SVD) model for representation of spatio-temporal fMRI data sets. Statistical inference procedures for PCA, including point and interval estimation will be introduced without the constraint of explicit hypotheses about specific task-dependent effects. The principal eigenvectors capture both the spatial and temporal aspects of fMRI data in a progressive fashion; they are inherently matched to unique and uncorrelated features and are ranked in order of the amount of variance explained. PCA also acts as a variation reduction technique, relegating most of the random noise to the trailing components while collecting systematic structure into the leading ones. Features summarizing variability may not directly be those that are the most useful. Further analysis is facilitated through linear subspace methods involving PC rotation and strategies of projection pursuit utilizing a reduced, lower-dimensional natural basis representation that retains most of the information. These properties will be illustrated in the setting of dynamic time-series response data from fMRI experiments involving pharmacological stimulation of the dopaminergic nigro-striatal system in primates.

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine↗
PubMed2004

Principal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples.

Principal component analysis (PCA) was used to identify the main sources of variation in the Fourier transform infrared (FT-IR) spectra of 329 wines of various styles. The FT-IR spectra were gathered using a specialized WineScan instrument. The main sources of variation included the reducing sugar and alcohol content of the samples, as well as the stage of fermentation and the maturation period of the wines. The implications of the variation between the different wine styles for the design of calibration models with accurate predictive abilities were investigated using glycerol calibration in wine as a model system. PCA enabled the identification and interpretation of samples that were poorly predicted by the calibration models, as well as the detection of individual samples in the sample set that had atypical spectra (i.e., outlier samples). The Soft Independent Modeling of Class Analogy (SIMCA) approach was used to establish a model for the classification of the outlier samples. A glycerol calibration for wine was developed (reducing sugar content < 30 g/L, alcohol > 8% v/v) with satisfactory predictive ability (SEP = 0.40 g/L). The RPD value (ratio of the standard deviation of the data to the standard error of prediction) was 5.6, indicating that the calibration is suitable for quantification purposes. A calibration for glycerol in special late harvest and noble late harvest wines (RS 31-147 g/L, alcohol > 11.6% v/v) with a prediction error SECV = 0.65 g/L, was also established. This study yielded an analytical strategy that combined the careful design of calibration sets with measures that facilitated the early detection and interpretation of poorly predicted samples and outlier samples in a sample set. The strategy provided a powerful means of quality control, which is necessary for the generation of accurate prediction data and therefore for the successful implementation of FT-IR in the routine analytical laboratory.

Calibration↗
PubMed2005

Energy landscape of a small peptide revealed by dihedral angle principal component analysis.

A 100 ns molecular dynamics simulation of penta-alanine in explicit water is performed to study the reversible folding and unfolding of the peptide. Employing a standard principal component analysis (PCA) using Cartesian coordinates, the resulting free-energy landscape is found to have a single minimum, thus suggesting a simple, relatively smooth free-energy landscape. Introducing a novel PCA based on a transformation of the peptide dihedral angles, it is found, however, that there are numerous free energy minima of comparable energy (less than or approximately 1 kcal/mol), which correspond to well-defined structures with characteristic hydrogen-bonding patterns. That is, the true free-energy landscape is actually quite rugged and its smooth appearance in the Cartesian PCA represents an artifact of the mixing of internal and overall motion. Well-separated minima corresponding to specific conformational structures are also found in the unfolded part of the free energy landscape, revealing that the unfolded state of penta-alanine is structured rather than random. Performing a connectivity analysis, it is shown that neighboring states are connected by low barriers of similar height and that each state typically makes transitions to three or four neighbor states. Several principal pathways for helix nucleation are identified and discussed in some detail.

Energy Transfer↗
PubMed2002

Classification of narcotics in solid mixtures using principal component analysis and Raman spectroscopy.

Eighty-five solid samples consisting of illegal narcotics diluted with several different materials were analyzed by near-infrared (785 nm excitation) Raman spectroscopy. Principal Component Analysis (PCA) was employed to classify the samples according to narcotic type. The best sample discrimination was obtained by using the first derivative of the Raman spectra. Furthermore, restricting the spectral variables for PCA to 2 or 3% of the original spectral data according to the most intense peaks in the Raman spectrum of the pure narcotic resulted in a rapid discrimination method for classifying samples according to narcotic type. This method allows for the easy discrimination between cocaine, heroin, and MDMA mixtures even when the Raman spectra are complex or very similar. This approach of restricting the spectral variables also decreases the computational time by a factor of 30 (compared to the complete spectrum), making the methodology attractive for rapid automatic classification and identification of suspect materials.

Cocaine↗
PubMed2003

Principal components analysis as an evaluation and classification tool for lower torso sEMG data.

The use of univariate statistical techniques on multivariate electromyography data can fail to uncover important relationships between variables. Principal components analysis (PCA) is a multivariate statistical technique that can be used as a data exploration tool, both by classifying participants and simplifying data structures. Past research using this technique has focused on discriminating between "patients" and "normals". This investigation explored the use of PCA on electromyography data from healthy participants, with the objective of elucidating any between-participant differences in the multivariate patterns of muscle coactivation. Results indicated that, even between healthy participants, quantitative and qualitative differences in muscle coactivation patterns exist and that, in the context of the lower torso, a large portion (>70%) of the empirically determined muscle activation could be synthesized in a theoretical three-parameter control model.

Abdominal Muscles↗
PubMed2005

Optimizing principal components analysis of event-related potentials: matrix type, factor loading weighting, extraction, and rotations.

OBJECTIVE: Given conflicting recommendations in the literature, this report seeks to present a standard protocol for applying principal components analysis (PCA) to event-related potential (ERP) datasets. METHODS: The effects of a covariance versus a correlation matrix, Kaiser normalization vs. covariance loadings, truncated versus unrestricted solutions, and Varimax versus Promax rotations were tested on 100 simulation datasets. Also, whether the effects of these parameters are mediated by component size was examined. RESULTS: Parameters were evaluated according to time course reconstruction, source localization results, and misallocation of ANOVA effects. Correlation matrices resulted in dramatic misallocation of variance. The Promax rotation yielded much more accurate results than Varimax rotation. Covariance loadings were inferior to Kaiser Normalization and unweighted loadings. CONCLUSIONS: Based on the current simulation of two components, the evidence supports the use of a covariance matrix, Kaiser normalization, and Promax rotation. When these parameters are used, unrestricted solutions did not materially improve the results. We argue against their use. Results also suggest that optimized PCA procedures can measurably improve source localization results. SIGNIFICANCE: Continued development of PCA procedures can improve the results when PCA is applied to ERP datasets.

Electroencephalography↗