Principal Component Variables Research Articles

Application of an electronic nose for the diagnosis of ketosis in dairy cows

Ketosis in dairy cows is a nutritional and metabolic disease that severely affects the milk industry and dairy cow health. An electronic nose can identify volatile organic compounds (VOCs). This study applied the electronic nose to detect VOCs in dairy cow milk, feces, and blood. Blood ketone bodies (BHBA) were measured, and 10 ketosis cows, 71 subclinical ketosis cows, and 75 healthy cows were considered the research subjects. Cow milk, feces, and blood samples were collected for electronic nose testing. The measured VOC data were processed through multivariate analysis and machine learning algorithms. The electronic nose analysis results were validated through the GC-MS analysis of the milk samples. The results unveiled spatial differences in the electronic nose VOC values of milk, feces, and blood, and the cumulative contribution rates of both principal component variables (PC) 1 and PC2 reached more than 95%. The machine learning algorithm results revealed that the accuracy of the milk probabilistic neural network (PNN) was 87.5%, the stool support vector machine was 100%, and the blood PNN was 66.7%. This result revealed that electronic nose detection can effectively differentiate healthy, subclinical ketosis, and clinical ketosis cows. According to the milk GC-MS results, significant differences were noted in the VOC content of milk samples between the different groups.The aforementioned studies revealed that electronic nose can aid in the effective prediction and diagnosis of ketosis in dairy cows. This lays the foundation for establishing a new method for diagnosing ketosis in dairy cows.

Morphometric characteristics of spiny-cheek crayfish Faxonius limosus (Rafinesque, 1817) from the Danube River on the territory of Serbia

Twenty-three morphometric characteristics of spiny-cheek crayfish Faxonus limosus from Danube, Serbia, were analyzed to describe the general body shape. Forty-eight specimens were caught in May 2022 and January 2023 and measured for morphometric variation. Also, the values of two crayfish condition indices, Fulton?s condition factor (FCF) and crayfish constant (CC), were determined. Most of the analyzed characteristics were moderately variable (CV 10-30%). Principal component analysis (PCA) was applied to the morphological measurements. All the variables make similar contributions to the variance of the first principal component. The highest contributions are from variables CEF and ARL. ARW, CPH, ABH, CPW, and ROW significantly contributed to the second principal component variance. The population of spiny-cheek crayfish at the investigated site has a relatively stable age structure.

Multivariable System Prediction Based on TCN-LSTM Networks with Self-Attention Mechanism and LASSO Variable Selection.

Intelligent prediction of key output variables that are difficult to measure online in complex systems has important research significance. In this paper, by using the least absolute shrinkage and selection operator (LASSO) algorithm to analyze the principal elements of input variables, a temporal convolutional network fused with long short-term memory (TCN-LSTM) network and self-attention mechanism (SAM) is designed to realize dynamic modeling of multivariate feature sequences. For complex processes with multiple input variables, each variable has different effects on the output, so it is necessary to use the LASSO algorithm to perform regression analysis on the input and output data for selecting the principal component variables and reducing the redundancy and computation burden of the network. The TCN network is used to extract the features of the input variables efficiently. The long-term memory performance of time series is enhanced by applying an LSTM network. The multihead SAM is used to optimize the network, and the role of key features is enhanced by assigning weights with probability to further improve the accuracy of sequence prediction. Finally, by comparison with the existing network model, the offline data generated by the high and low converters in the synthetic ammonia industry is used to predict the CO content so as to verify the superiority and applicability of the proposed network model.

Euclidean and Shape-Based Analysis of the Dynamic Mitral Annulus in Children using a Novel Open-Source Framework

The dynamic shape of the normal adult mitral annulus has been shown to be important to mitral valve function. However, annular dynamics of the healthy mitral valve in children have yet to be explored. The aim of this study was to model and quantify the shape and major modes of variation of pediatric mitral valve annuli in four phases of the cardiac cycle using transthoracic echocardiography. The mitral valve annuli of 100 children and young adults with normal findings on three-dimensional echocardiography were modeled in four different cardiac phases using the SlicerHeart extension for 3D Slicer. Annular metrics were quantified using SlicerHeart, and optimal normalization to body surface area was explored. Mean annular shapes and the principal components of variation were computed using custom code implemented in a new SlicerHeart module (Annulus Shape Analyzer). Shape was regressed over metrics of age and body surface area, and mean shapes for five age-stratified groups were generated. The ratio of annular height to commissural width of the mitral valve ("saddle shape") changed significantly throughout age for systolic phases (P<.001) but within a narrow range (median range, 0.20-0.25). Annular metrics changed statistically significantly between the diastolic and systolic phases of the cardiac cycle. Visually, the annular shape was maintained with respect to age and body surface area. Principal-component analysis revealed that the pediatric mitral annulus varies primarily in size (mode 1), ratio of annular height to commissural width (mode 2), and sphericity (mode 3). The saddle-shaped mitral annulus is maintained throughout childhood but varies significantly throughout the cardiac cycle. The major modes of variation in the pediatric mitral annulus are due to size, ratio of annular height to commissural width, and sphericity. The generation of age- and size-specific mitral annular shapes may inform the development of appropriately scaled absorbable or expandable mitral annuloplasty rings for children.

True sparse PCA for reducing the number of essential sensors in virtual metrology

In the semiconductor industry, virtual metrology (VM) is a cost-effective and efficient technique for monitoring the processes from one wafer to another. This technique is implemented by generating a predictive model that uses real-time data from equipment sensors in conjunction with measured wafer quality characteristics. Before establishing a prediction model for the VM system, appropriate selection of relevant input variables should be performed to maintain the efficiency of subsequent analyses considering the large dimensionality of the sensor data inputs. However, wafer production processes usually employ multiple sensors, which leads to cost escalations. Herein, we propose a variant of the sparse principal component analysis (PCA) called true sparse PCA (TSPCA). The proposed method uses a small number of input variables in the first few principal components. The main contribution of the proposed TSPCA is reducing the number of essential sensors. Our experimental results demonstrate that compared to the existing sparse PCA methods, the proposed approach can reduce the number of sensors required while explaining an approximately equivalent amount of variance.

Assessing the Contributions of Urban Green Space Indices and Spatial Structure in Mitigating Urban Thermal Environment

Urban green space takes a dominant role in alleviating the urban heat island (UHI) effect. Most investigations into the effects of cooling factors from urban green spaces on the UHI have evaluated the correlation between each factor and land surface temperature (LST) separately, and the contribution weights of various typical cooling factors in mitigating the thermal environment have rarely been analyzed. For this research, three periods of Landsat 8 data captured between 2014 and 2018 of Xuzhou during the summer and autumn seasons were selected along with corresponding meteorological and flux measurements. The mono-window method was employed to retrieve LST. Based on the characteristics of the vegetation and spatial features of the green space, eight factors related to green space were selected and computed, consisting of three indices that measure vegetation and five metrics that evaluate landscape patterns: vegetation density (VD), evapotranspiration (ET), green space shading degree (GSSD), patch area ratio (PLAND), largest patch index (LPI), patch natural connectivity (COHESION), patch aggregation (AI), and patch mean shape index distribution (SHPAE_MN). Linear regression and bivariate spatial autocorrelation analyses between each green space factor and LST showed that there were significant negative linear and spatial correlations between all factors and LST, which proved that the eight factors were all cooling factors. In addition, LST was strongly correlated with all factors (|r| &gt; 0.5) except for SHPAE_MN, which was moderately correlated (0.3 &lt; |r| &lt; 0.5). Based on this, two principal components were extracted by applying principal component analysis with all standardized green space factors as the original variables. To determine the contribution weight of each green space factor in mitigating the urban heat island (UHI) effect, we multiplied the influence coefficient matrix of the initial variables with the standardized multiple linear regression coefficients between the two principal component variables and LST. The final results indicated that the vegetation indices of green space contribute more to the alleviation of the UHI than its landscape pattern metrics, and the contribution weights are ranked as VD ≥ ET &gt; GSSD &gt; PLAND ≈ LPI &gt; COHESION &gt; AI &gt; SHAPE_MN. Our study suggests that increasing vegetation density is preferred in urban planning to mitigate urban thermal environment, and increasing broadleaf forests with high evapotranspiration and shade levels in urban greening is also an effective way to reduce ambient temperature. For urban green space planning, a priority is to multiply the regional green space proportion or the area of largest patches. Second, improving the connectivity or aggregation among patches of green space can enhance their ability to cool the surrounding environment. Altering the green space spatial shape is likely the least significant factor to consider.

Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system

Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month’s samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch.Graphical abstract

The role of soil and plant cover as drivers of soil macrofauna of the Dnipro River floodplain ecosystems

Abstract Floodplain ecosystems are hotspots of biological diversity and perform important ecosystem functions in the landscape. The key to understanding the sustainability of ecosystem function is knowledge of the relationships between ecosystem components. The article reveals the role of morphological and physical properties of soil, as well as phytoindication of environmental factors as drivers of biological diversity of soil macrofauna of protected ecosystems of the Dnipro River floodplain. The studies were conducted in the forest floodplain ecosystems of the “Dnipro-Orilskiy” Nature Reserve. The studies of morphological properties of soils allowed us to identify the representatives of two reference groups: Fluvisol and Gleysol. The soil physical property data were subjected to principal component analysis, which extracted four principal components whose eigenvalues exceeded unity and described 79.9% of the variation in traits. The principal components of variation in soil physical properties and phytoindication assessments of environmental factors were used as predictors of the community structure of soil macrofauna. These predictors were able to explain 29.6% of the community variation. Physical soil properties are most important as a driver of soil macrofauna. The morphological properties of the soil and phytoindicator assessments are able to explain a much smaller part of the community variation. The pure influence of the predictors is small, indicating that they interact significantly in influencing soil animals. The results obtained have implications for the development of optimal strategies for floodplain ecosystem management and biodiversity conservation.

Light competition is the key factor determining spatio-temporal variability in legume proportion within Marandu palisadegrass–forage peanut mixed pastures

Context Understanding ecology in grass–legume pastures can help support strategies aimed at maintaining canopy stability in terms of botanical composition. Aims This 2-year study evaluated spatial variability, focusing on plant structural characteristics in a Marandu palisadegrass [Urochloa brizantha (Hochst. Ex A. Rich.) R.D. Webster cv. Marandu]–forage peanut (Arachis pintoi Krapov. and W.C. Greg. cv. BRS Mandobi) mixed canopy under grazing in continuous stocking management. Methods The pasture was managed with canopy height of 20–25 cm. The experimental area had a permanent sample grid containing 50 pre-established and georeferenced plots. Principal component analysis was performed, and spatial dependence structure of the first principal component and structural variables were separately characterised by geostatistical analysis. Key results There was spatial dependence of the structural characteristics, with marked spatial heterogeneity in the distribution of all variables. Taller grass canopies caused competition for light between species, reducing legume contribution and inducing more erect forage peanut growth habit. Conversely, in areas with lower grass canopy height, forage peanut botanical composition increased to up to 70%, as it propagated more effectively by stoloniferous propagation. Conclusions The canopy structure spatial variability of a Marandu palisadegrass–forage peanut mixed pasture determines the overall average pasture grass/legume proportion. In regions with taller canopies, Marandu palisadegrass was favoured, while in areas with shorter canopy, forage peanut proportion was promoted. Satisfactory legume proportions can be reached in the canopy despite areas with reduced legume contribution. Implications Our findings using geostatistical techniques facilitate the development of useful and innovative tools allowing better comprehension for the management of mixed pastures.

A Preliminary Study on the Correlation Factor Analysis of Language Cognitive Assessment System Based on Scale Construction

This paper explores the 12 dimensions of language cognition assessment system using factor analysis. Principal component analysis and correlation analysis of factors within the system were conducted on the language cognition assessment system. Factor analysis and principal component analysis: KMO: 0.934, weak bias correlation, suitable for factor analysis, and the contribution of principal component variance was 79.837%, respectively. The correlations between the 12 factors ranged from 0.611-0.903. The correlation values between the remaining 11 factor assignments and the total score of the system were higher than the correlations between the subscales. The system is constructed with good structural validity, but the correlations between the factors are strong. It is suggested that the indicators be combined or the form of the questions be modified to provide guidance for the modification of the language cognition assessment system.

Comparative serum proteomic analysis of a selected protein panel in individuals with schizophrenia and bipolar disorder and the impact of genetic risk burden on serum proteomic profiles

The diagnostic criteria for schizophrenia (SCZ) and bipolar disorder (BD) are based on clinical assessments of symptoms. In this pilot study, we applied high-throughput antibody-based protein profiling to serum samples of healthy controls and individuals with SCZ and BD with the aim of identifying differentially expressed proteins in these disorders. Moreover, we explored the influence of polygenic burden for SCZ and BD on the serum levels of these proteins. Serum samples from 113 individuals with SCZ and 125 with BD from the PsyCourse Study and from 44 healthy controls were analyzed by using a set of 155 antibodies in an antibody-based assay targeting a selected panel of 95 proteins. For the cases, genotyping and imputation were conducted for DNA samples and SCZ and BD polygenic risk scores (PRS) were calculated. Univariate linear and logistic models were used for association analyses. The comparison between SCZ and BD revealed two serum proteins that were significantly elevated in BD after multiple testing adjustment: “complement C9” and “Interleukin 1 Receptor Accessory Protein”. Moreover, the first principal component of variance in the proteomics dataset differed significantly between SCZ and BD. After multiple testing correction, SCZ-PRS, BD-PRS, and SCZ-vs-BD–PRS were not significantly associated with the levels of the individual proteins or the values of the proteome principal components indicating no detectable genetic effects. Overall, our findings contribute to the evidence suggesting that the analysis of circulating proteins could lead to the identification of distinctive biomarkers for SCZ and BD. Our investigation warrants replication in large-scale studies to confirm these findings.

Identification of critical parameters influencing resistance performance of amphibious vehicles based on a SM-SA method

The design of high-speed amphibious vehicles needs to consider more factors compared to ships. The efficiency of design and optimization of parameters will not be realized in the absence of a feasible parametric model and design criterion. This paper provided a recognition method for the critical factors that significantly affect amphibious vehicles' resistance. Firstly, an initial parametric model of amphibious vehicles was established, and the resistance coefficients were acquired through numerical simulations. The principal component variables of initial data were extracted by principal component analysis (PCA). Then the functional relations between resistance and principal component variables were obtained respectively through artificial neural network (ANN) and nonlinear polynomial fitting (NPF). Next, two surrogate models were employed to analyze the sensitivity of the resistance to initial parameters. The identified sensitive parameters include the trim angle, loss of waterplane area, and some principal dimensions coefficients. The variation of parameters’ sensitivity and their interactions were recognized when parameters are located in different regions. Ultimately, the resistance surrogate model was constructed with critical parameters, enabling the rapid optimization of parameter scheme. Compared with the initial scheme, the optimized scheme achieved significantly reduction of resistance. The extraction and optimization method for critical parameters in this paper provides reference for the design of amphibious vehicles.

Thyristor State Evaluation Method Based on Kernel Principal Component Analysis

The reliability of the thyristor is directly related to the safe operation of the DC transmission system. A method for evaluating the state of thyristors based on kernel principal component analysis (KPCA) is proposed, which firstly considers the thyristor test data, operation records, maintenance history, appearance inspection information, states of other components and operating environment. A basic index system for evaluating the aging state of thyristor with 42 parameters is established. Next, a mathematical model was developed by Fisher Discriminant Analysis (FDA). The kernel function of the kernel principal components is then optimized by an improved particle swarm optimization (IPSO) algorithm. The improved KPCA is applied to extract key parameters from the base index system to obtain the reduced dimensional evaluation indicators. The obtained principal component factors are used to determine the weights of the fuzzy composite factors, which are applied for fuzzy evaluation of the thyristor. Finally, 20 thyristors are selected for experimental and theoretical calculations. The results show that the cumulative contribution of the first three principal component variables after dimensionality reduction reaches 93.76&#x0025;, which is consistent with the state of the thyristor. Compared to the four existing evaluation methods, the results of the method proposed in this paper are more reasonable, which removes the influence of redundant indicators, reduces the amount of data, and provides a reference for the related research on thyristor state evaluation.

A neural network regularization method to address variance inflation in autoencoders

There exist various machine learning techniques which can be used to reduce the dimensionality of original data while minimizing the information loss. Principal component analysis (PCA) is one of the most well known such techniques, which transforms the original correlated variables into uncorrelated variables called principal components. Although PCA is known to preserve the total variance of the original data during the transformation, there are some cases with a potential of variance inflation, where the total variance of principal components becomes much larger than that of original variables. It is important to prevent variance inflation, as it can negatively affect the performance of other application systems (e.g. process monitoring systems) which are designed on the basis of principal component with inflated variances. Variance inflation also has a high potential to occur during the training of autoencoder, a special type of neural network performing nonlinear version of PCA. Although there are several neural network regularization methods available to alleviate the problem of variance inflation, none of them is tailored to do such task. To this end, in this work, an alternative neural network regularization method is proposed, which can strongly regulate the total variance in the feature space. Using the Tennessee Eastman process as an illustrative example, the proposed regularization method is compared with the existing ones in terms of neural network overfitting, variance inflation, and training time.

DETECTION OF THE NUTRITIONAL STATUS OF PHOSPHORUS IN LETTUCE USING THZ TIME-DOMAIN SPECTROSCOPY

Phosphorus deficiency can lead to serious reductions in crop yield and quality. Rapid and accurate determination of phosphorus stress status in vegetables is essential for ensuring the appropriate application of water and fertiliser, improving the yield and quality of vegetables, avoiding the waste of resources and serious non-point pollution source caused by the abuse of chemical fertilisers, and promoting green production and sustainability of facility agriculture. Chemical analysis and spectral or visual imaging methods for detection of phosphorus nutritional status are not conducive in facility production owing to their low accuracy and damage to plants. Owing to its penetration and fingerprint characteristics, terahertz time-domain spectroscopy (THz-TDS) can distinguish differences in internal components caused by excess phosphorus nutrients and changes in nucleic acids, nuclear proteins, phospholipids, and other macromolecules; thus, it could potentially be used to evaluate the nutritional status of crops. In this study, an innovative THz-TDS-based method was used to detect the nutritional status of phosphorus in lettuce. Lettuce was grown with different phosphorus levels using soilless cultivation and different nutrient solutions. Based on the standard formula of Yamasaki nutrient solution, the phosphorus content in the nutrient solutions was reduced or increased by 20%, 60%, 100%, and 150%, and lettuce samples exposed to each of these phosphorus concentrations were collected. Terahertz spectra are highly sensitive to water; thus, the lettuce samples were freeze-dried to minimise the effect of water and maintain their original quality and bioactivity. The spectra of lettuce were recorded using a TS7400 THz-TDS system; noise and interference were eliminated via normalisation based on Savitzky-Golay smoothing. The correction and validation sets were divided using sample set partitioning based on the joint x-y distance (SPXY). The stability competitive adaptive reweighted algorithm, iterative retention information variable algorithm, and interval combination optimisation algorithm were used to select the terahertz characteristic wavelength, and the successive projection algorithm was then used for secondary optimisation. Finally, a THz-TDS model of lettuce phosphorus was established using the partial least squares method with five principal component variables. The coefficient of determination of the model reached 0.7005, and the root-mean-square error of the predictions was 0.003273, indicating that this method has a high prediction accuracy.

CPCA++: An efficient method for contrastive feature learning

In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data. This technique, referred to as cPCA++, is motivated by the fact that the interesting features of a “target” dataset may be obscured by high variance components during traditional PCA. By analyzing what is referred to as a “background” dataset (i.e., one that exhibits the high variance principal components but not the interesting structures), our technique is capable of efficiently highlighting the structures that are unique to the “target” dataset. Similar to another recently proposed algorithm called “contrastive PCA” (cPCA), the proposed cPCA++ method identifies important dataset-specific patterns that are not detected by traditional PCA in a wide variety of settings. However, unlike cPCA, the proposed cPCA++ method does not require a parameter sweep, and as a result, it is significantly more efficient. Several experiments were conducted in order to compare the proposed method to state-of-the-art methods. These experiments show that the proposed method achieves performance that is similar to or better than that of the other methods, while being more efficient.

Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example

Aiming at the problems of low accuracy, low efficiency, and many parameters required in the current calculation of rock slope stability, a prediction model of rock slope stability is proposed, which combines principal component analysis (PCA) and relevance vector machine (RVM). In this model, PCA is used to reduce the dimension of several influencing factors, and four independent principal component variables are selected. With the help of RVM mapping the nonlinear relationship between the safety factor of slope stability and the principal component variables, the prediction model of rock slope stability based on PCA-RVM is established. The results show that under the same sample, the maximum relative error of the PCA-RVM model is only 1.26%, the average relative error is 0.95%, and the mean square error is 0.011, which is far lower than that of the RVM model and the GEP model. By comparing the results of traditional calculation method and PCA-RVM model, it can be concluded that the PCA-RVM model has the characteristics of high prediction accuracy, small discreteness, and high reliability, which provides reference value for accurately predicting the stability of rock slope.

On the Application of Principal Component Analysis to Classification Problems

Principal Component Analysis (PCA) is a commonly used technique that uses the correlation structure of the original variables to reduce the dimensionality of the data. This reduction is achieved by considering only the first few principal components for a subsequent analysis. The usual inclusion criterion is defined by the proportion of the total variance of the principal components exceeding a predetermined threshold. We show that in certain classification problems, even extremely high inclusion threshold can negatively impact the classification accuracy. The omission of small variance principal components can severely diminish the performance of the models. We noticed this phenomenon in classification analyses using high dimension ECG data where the most common classification methods lost between 1 and 6% of accuracy even when using 99% inclusion threshold. However, this issue can even occur in low dimension data with simple correlation structure as our numerical example shows. We conclude that the exclusion of any principal components should be carefully investigated.

Plant identification of Beijing Hanshiqiao wetland based on hyperspectral data

Hyperspectral data play an important role in monitoring and protecting wetlands because it not only offers high resolution and wide observation range but also reveal plant information. To explore the hyperspectral characteristics of wetland plants and their applicability in identification, in situ hyperspectral data for 15 species of wetland plants were collected from the Hanshiqiao Wetland Nature Reserve in Beijing, a constructed wetland. Then, first derivative, second derivative, and logarithmic transformation of the reciprocal of the hyperspectral reflectance data were performed using three data conversion methods (principal component analysis, vegetation index, and full-band spectrum) to identify characteristic hyperspectral variables. Four classifiers were compared, namely, support vector machine, decision tree, back-propagation neural network, and random forest. The results demonstrated that: (1) for a comprehensive comparison of the four spectral features data, logarithmic treatment of the reciprocal significantly improved the identification accuracy; (2) for the three data conversion methods for identifying spectral characteristics, the identification accuracy based on the full-band spectrum was better than the other two data forms, and the identification effectiveness of principal component analysis was better than that of the vegetation index; (3) because of differences in plant types and data formats, none of the four classifiers had obvious advantages or disadvantages over the others; and (4) the identification results for all plant statistics further verified the applicability of full-band spectrum data for plant identification. The results showed that the vegetation index and principal component variables reduced the calculation time but did not improve the identification accuracy. Overall, the spectral variables and classifiers examined in this study could meet the needs of wetland plant identification to a certain extent.

Environmental Contaminant Body Burdens and the Relationship with Blood Pressure Measures Among Indigenous Adults.

Using data from the Environment-and-Health Study, the sample was restricted to adults with valid BP measures and detectable body burden mixtures. In total, n = 759 participants were eligible, of which, n = 447(59%) were females. Principal Component Analysis (PCA) was used to reduce the complexity of the contaminant data. Orthogonal principal component (PC) variables, and sum (∑) of contaminant compounds were used as independent predictors in modified Poisson regression models with robust variance estimation, deriving prevalence ratios (PR) for hypertension, that is, systolic BP (SBP) ≥140 ml of mercury (mmHg), or diastolic BP ≥90 mm Hg. Additionally, using multivariable generalized linear regression, sex-stratified continuous SBP measure was regressed on ∑ contaminant compounds. Two PCs were extracted from the PCA analysis. PCBs and OCs positively highly loaded on the first axis (PC-1). Lead loaded positively on the second (PC-2) axis. Hypertension was consistently associated with PC-1 across models, PR = 1.08 (95% confidence intervals = 1.003 to 1.172) fully adjusted model. Examining ∑ of contaminants, the strength of association with hypertension was strongest for ∑OCs compared with ∑PCBs. The measure of effect for continuous SBP measures with ∑ dichlorodiphenyltrichloroethane(p,p'-DDT) and dichlorodiphenyldichloroethylene(p,p'-DDE) concentrations although small was similar for both females and males, β = 0.04 (95% confidence intervals = 0.005 to 0.075) among females; however, for males the estimate is imprecise after adjusting for body mass index. This cross-sectional analysis found that PCBs and OCs were associated with associated prevalent hypertension; and exposure to OC pesticides, particularity DDT/DDE were found to be associated with prevalent SBP measures among females and not males.