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Results for “Principal Component Analysis”

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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↗

PubMed2006

Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra: classification of normal premalignant and malignant pathological conditions.

Pulsed laser-induced autofluorescence spectroscopic studies of pathologically certified normal, premalignant, and malignant oral tissues were carried out at 325 nm excitation. The spectral analysis and classification for discrimination among normal, premalignant, and malignant conditions were performed using principal component analysis (PCA) and artificial neural network (ANN) separately on the same set of spectral data. In case of PCA, spectral residuals, Mahalanobis distance, and scores of factors were used for discrimination among normal, premalignant, and malignant cases. In ANN, parameters like mean, spectral residual, standard deviation, and total energy were used to train the network. The ANN used in this study is a classical multiplayer feed-forward type with a back-propagation algorithm for the training of the network. The specificity and sensitivity were determined in both classification schemes. In the case of PCA, they are 100 and 92.9%, respectively, whereas for ANN they are 100 and 96.5% for the data set considered.

Aged↗

PubMed2005

Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems.

OBJECTIVES: We present a computer-aided diagnostic (CAD) system with textural features and image retrieval strategies for classifying benign and malignant breast tumors on various ultrasonic systems. Effective applications of CAD have used different types of texture analysis. Nevertheless, most approaches performed in a specific ultrasonic machine do not indicate whether the technique functions satisfactorily for other ultrasonic systems. This study evaluated a series of pathologically proven breast tumors using various ultrasonic systems. METHODS: Altogether, 600 ultrasound images of solid breast nodules comprising 230 malignant and 370 benign tumors were investigated. All ultrasound images were acquired from four diverse ultrasonic systems. The suspicious tumor area in the ultrasound image was manually chosen as the region-of-interest (ROI) subimage. Textural features extracted from the ROI subimage are supported in classifying the breast tumor as benign or malignant. However, the textural feature always behaves as a high-dimensional vector. In practice, high-dimensional vectors are unsatisfactory at differentiating breast tumors. This study applied the principal component analysis (PCA) to project the original textural features into a lower dimensional principal vector that summarized the original textural information. The image retrieval techniques were employed to differentiate breast tumors, according to the similarities of the principal vectors. The query ROI subimages were identified as malignant or benign tumors according to characteristics of retrieved images from the ultrasound image database. RESULTS: Using the proposed CAD system, historical cases could be directly added into the database without a retraining program. The area under the receiver-operating characteristics curve for the system was 0.970+/-0.006. CONCLUSION: The CAD system identified solid breast nodules with comparatively high accuracy in the different ultrasound systems investigated.

Area Under Curve↗

PubMed2005

Location and intensity discrimination in the leech local bend response quantified using optic flow and principal components analysis.

In response to touches to their skin, medicinal leeches shorten their body on the side of the touch. We elicited local bends by delivering precisely controlled pressure stimuli at different locations, intensities, and durations to body-wall preparations. We video-taped the individual responses, quantifying the body-wall displacements over time using a motion-tracking algorithm based on making optic flow estimates between video frames. Using principal components analysis (PCA), we found that one to three principal components fit the behavioral data much better than did previous (cosine) measures. The amplitudes of the principal components (i.e., the principal component scores) nicely discriminated the responses to stimuli both at different locations and of different intensities. Leeches discriminated (i.e., produced distinguishable responses) between touch locations that are approximately a millimeter apart. Their ability to discriminate stimulus intensity depended on stimulus magnitude: discrimination was very acute for weak stimuli and less sensitive for stronger stimuli. In addition, increasing the stimulus duration improved the leech's ability to discriminate between stimulus intensities. Overall, the use of optic flow fields and PCA provide a powerful framework for characterizing the discrimination abilities of the leech local bend response.

Animals↗

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↗

PubMed1990

Distinguishing normal and abnormal tracheal breathing sounds by principal component analysis.

Expired and inspired tracheal breathing sounds (BS) were recorded from 10 normal subjects and 8 patients with respiratory diseases, including bronchial asthma, sarcoidosis, fibrosing lung disease, chronic bronchitis, and radiation pneumonitis. Frequency spectra were generated using Fast Fourier Transform (FFT), and we observed considerable differences between BS spectra of normal subjects and patients. The frequency of peak amplitude and mean frequency of the BS spectra of patients were significantly higher than those of normal subjects. Spectral features were extracted by dividing each spectra into equal frequency bands--each feature being the mean amplitude of each FFT element within a frequency band. We used Principal Component Analysis to compare spectral feature sets and found a clear separation between normal and abnormal tracheal BS for 10, 20, and 40 features/spectra. We conclude that Principal Component Analysis of BS could become a new method of diagnosing respiratory disease in an automated fashion.

Adult↗

PubMed2005

Metabolic fingerprinting of Ephedra species using 1H-NMR spectroscopy and principal component analysis.

The metabolomic analysis of Ephedra species was performed using 1H-NMR spectroscopy and multivariate data analysis. A broad range of metabolites could be detected by 1H-NMR spectroscopy without any chromatographic separation. The principal component analysis used to reduce the huge data set obtained from the 1H-NMR spectra of the plant extracts clearly discriminated three different Ephedra species. The major differences in Ephedra sinica, Ephedra intermedia and Ephedra distachya var. distachya were found to be due to benzoic acid analogues in the aqueous fraction and ephedrine-type alkaloids in the organic fraction. Based on this metabolomic recognition, one of nine commercial Ephedra materials evaluated was shown to be a mixture of Ephedra species. This method will be a useful tool for chemotaxonomic analysis and authentification of Ephedra species including quality control of plant materials.

Ephedra↗

PubMed2004

Normalization of single-channel DNA array data by principal component analysis.

MOTIVATION: Detailed comparison and analysis of the output of DNA gene expression arrays from multiple samples require global normalization of the measured individual gene intensities from the different hybridizations. This is needed for accounting for variations in array preparation and sample hybridization conditions. RESULTS: Here, we present a simple, robust and accurate procedure for the global normalization of datasets generated with single-channel DNA arrays based on principal component analysis. The procedure makes minimal assumptions about the data and performs well in cases where other standard procedures produced biased estimates. It is also insensitive to data transformation, filtering (thresholding) and pre-screening.

Algorithms↗

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↗

PubMed2003

Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks.

Detection and correct classification of gasoline is important for both arson and fuel spill investigation. Principal component analysis (PCA) was used to classify premium and regular gasolines from gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Depending upon the dataset used for training and tests, around 80-93% of the samples were correctly classified as either premium or regular gasoline using the Mahalanobis distances calculated from the principal components scores. Only 48-62% of the samples were correctly classified when the premium and regular gasoline samples were divided further into their winter/summer sub-groups. Artificial neural networks (ANNs) were trained to recognise premium and regular gasolines from the same GC-MS data. The best-performing ANN correctly identified all samples as either a premium or regular grade. Approximately 97% of the premium and regular samples were correctly classified according to their winter or summer sub-group.

Journal Article↗

PubMed2005

Efficient training of multilayer perceptrons using principal component analysis.

A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior.

Journal Article↗

PubMed2003

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↗

PubMed2006

Interpretation of the repetitive nerve stimulation test results using principal component analysis.

OBJECTIVE: Assessment of the repetitive nerve stimulation (RNS) test parameters has some inherent difficulties, as too many co-dependent variables are involved. To circumvent these problems, we have employed the principal component analysis (PCA) for evaluating the RNS test. METHODS: We performed the RNS test on the abductor digiti quinti (ADQ), flexor carpi ulnaris (FCU) and orbicularis oculi (OO) muscles of 23 myasthenia gravis (MG) patients and 50 controls. For each group, following parameters were chosen for PCA: decremental response of amplitude and area on 2, 3 and 5Hz stimulation rate, including 5Hz stimulation, 4min following tetanus; decremental and incremental response of amplitude and area on 50Hz stimulation. RESULTS: Two principal components (PC1 and PC2) for ADQ and FCU muscles and 1 principal component (PC1) for OO muscle were extracted. The mean values of PC1 were significantly increased for all three muscles in the MG group compared to controls (p<0.01). No significant difference between PC2 values of the MG and control groups was observed (p>0.05). PC1 was the most sensitive test in detecting an abnormality on low rates of stimulation. CONCLUSIONS: PCA, which has the advantage of studying a small number of independent parameters on RNS test, seems to be useful for detecting neuromuscular transmission defects. SIGNIFICANCE: By markedly decreasing the number of assessed variables, PCA can give insight to the direction of data distribution abnormalities in the RNS test, which can prove particularly useful in research studies.

Action Potentials↗

PubMed2001

Application of principal component analysis to the elucidation of perioral soft tissue movements during mastication.

We considered that elucidation of the movements of perioral soft tissue during mastication would be useful in evaluating masticatory movements. However, the evaluation of these movements is difficult because multivariable analysis is needed. In this study, we considered whether principal component analysis (PCA), a form of the multivariate analysis, can reduce the number of degrees of freedom (d.f.) of perioral skin movements (i.e. the time-series data with 45 d.f.). The subjects were 15 healthy persons with complete natural dentition. The chosen experimental food for this study was sufficiently softened chewing gum. Over 95% of the perioral soft tissue movements of healthy subjects with complete natural dentition during mastication could be expressed by PCA using the first three principal components (PCs). Therefore, perioral soft tissue movements in these subjects during mastication were considered to be spatially smooth. Moreover, time analysis of these movements was made possible by the application of proportion diagrams. The results of this study showed that the spatiostructural and temporal analyses of the movements of perioral soft tissue during mastication made possible by the application of PCA.

Adult↗

PubMed2001

Exploratory studies of PM10 receptor and source profiling by GC/MS and principal component analysis of temporally and spatially resolved ambient samples.

For a recent exploratory study of particulate matter (PM) compositions, origins, and impacts in the El Paso/Juarez (Paso del Norte) airshed, the authors relied on solvent extraction (SX)-gas chromatography/mass spectrometry (GC/MS) procedures to characterize 24-hr quartz fiber (QF) filter samples obtained from nine spatially distributed high-volume (Hi-Vol) PM10 samplers as well as on thermal desorption (TD)-GC/MS methods to characterize 45 time-resolved (2-hr) filter samples obtained with modified 1-m3/hr PM10 samplers. Principal component analysis and related chemometric techniques were used for data reduction and data fusion as well as for multiway data correlation. A high degree of correspondence (R2 = 0.821) was found between the rapid TD-GC/MS method (which can be carried out on 2-hr filter slices containing only microgram amounts of sample) and conventional SX-GC/MS procedures. The four main source patterns of organic PM components observed in GC/MS profiles of both temporally and spatially resolved receptor samples obtained in the El Paso/Juarez border airshed during the study period are interpreted to represent (1) vehicular emissions plus resuspended urban dust; (2) biomass combustion; (3) native vegetation detritus and resuspended agricultural dust; and (4) waste burning. Moreover, principal component analysis of combined, variance-weighted, temporally resolved TD-GC/MS data and spatially resolved SX-GC/MS data was used to determine approximate source locations for specific PM components identified in time-resolved receptor sample profiles. The same approach can be used to determine approximate circadian concentration profiles of specific PM components identified in spatially resolved receptor sample profiles.

Agriculture↗

PubMed1994

Evaluation of acid-base disorders in dairy cows using principal component analysis and empiric equations.

Acid-base disorders were studied in two groups of dairy cows. According to conventional interpretation in the 1st group of dairy cows (n = 10) the results of the acid-base parameters indicated respiratory alkalosis and in the 2nd group (n = 10) metabolic acidosis. The data of the two groups were examined using principal component analysis. So called "reduced variables"--principal components for each animal were calculated. Each experimental animal was projected in the coordinates of 3 principal components K1, K2 and K3. The components K1, K2 and K3 comprised a 90% data variability. The importance of variables for a dividing of groups (1 and 2) was quantified. The most important were pCO2, (HCO3)' and BE. It was proved according to mathematico-empiric equations that dairy cows of 1st group suffered from acute respiratory alkalosis. In the 2nd group of dairy cows the combined acid-base disorder was confirmed using the equations for metabolic acidosis and chronic respiratory acidosis. The use of PCA method and empiric equations for acid-base imbalances shows the possibilities for gaining new knowledge how to make the diagnostic process more exact.

Acid-Base Imbalance↗

PubMed1994

Principal component analysis of dynamic positron emission tomography images.

Multivariate image analysis can be used to analyse multivariate medical images. The purpose could be to visualize or classify structures in the image. One common multivariate image analysis technique which can be used for visualization purposes is principal component analysis (PCA). The present work concerns visualization of organs and structures with different kinetics in a dynamic sequence utilizing PCA. When applying PCA on positron emission tomography (PET) images, the result is initially not satisfactory. It is illustrated that one major explanation for the behaviour of PCA when applied to PET images is that it is a data-driven technique which cannot separate signals from high noise levels. With a better understanding of the PCA, gained with a strategy of examining the image data set, the transformations, and the results using visualization tools, a surprisingly easily understood methodology can be derived. The proposed methodology can enhance clinically interesting information in a dynamic PET imaging sequence in the first few principal component images and thus should be able to aid in the identification of structures for further analysis.

Artifacts↗