Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the ...electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG‐ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes‐closed and eyes‐open resting conditions, followed by an equiprobable go/no‐go task. Frequency PCA of the EEG, including the nontask resting and within‐task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA‐derived go and no‐go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data‐driven components from both the ERP and EEG.
Despite several decades of study, ambiguities persist in terms used to express environmental and biotic occurrences of polychlorinated alkanes (PCAs), the main ingredient of chlorinated paraffins ...(CPs). This can lead to misinterpretation of data between analytical chemists, toxicologists, risk assessors/managers and regulators. The terms recommended here to harmonise reporting and reduce ambiguity use the conventional definition of PCAs linear chlorinated alkanes (typically, C->= 10) with one chlorine per carbon, although some evidence of multiple chlorination exists. Other recommendations include.reporting the "Sum of measured PCAs" because "Total PCAs" is currently unquantifiable.reporting individual chain lengths, e.g., Sigma PCAs-C-11, Sigma PCAs-C-13, allows easier comparability and allows toxicology and risk assessment to consider different PCA combinations.maintain studies on individual PCAs in order to better characterise chemical, environmental and health risk behaviour.The terms could be extended in future to assimilate new findings on individual PCAs, multiple chlorination and chirality.
•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.
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.
For industrial chemical process, preliminary-summation-based principal component analysis (PS-PCA), an amended PCA method was recently provided for coping with both Gaussian and non-Gaussian ...characteristics. By summing the training and monitoring data respectively, PS-PCA is capable of resolving the issue of non-Gaussian processes and achieves higher fault detection rate than the traditional PCA. However, in the PS-PCA summation operation, all data samples are regarded as the same weight, which results in the fault information of newly-samples may be diluted, leading to significant detection delays. To address this challenge, in this paper, we propose a novel weighted PS-PCA (WPS-PCA) method that employs an exponential weighting scheme to put more emphasis on recent information. Subsequently, a mathematical argument demonstrates that when the number of variables is enough plentiful, the obtained summation combined with the generalized central limit theorem conforms to approximately a Gaussian distribution. The kurtosis relationships indicate this conversion will bring out well-pleasing feasibility for conventional PCA. Ultimately, the proposed technique verifies detection performance using the Tennessee Eastman process, which is compared with the existing PCA and PS-PCA schemes, in terms of the fault detection time and fault detection rate. The simulation studies reveal that the proposed method is efficient and superior.
Prostate cancer (PCa) kills one man in forty five and represents the most frequently diagnosed cancer in American men. As is true with cancer, early detection through digital rectal exam and serum ...testing for prostate specific antigen have greatly reduced the number of fatalities yet these same methods sometimes lead to overdiagnosis and overtreatment. Previous studies have highlighted the association between single nucleotide polymorphisms (SNPs) and cancer. In our studies, we sought to find correlations between SNPs on chromosome 8 and PCa. Normal and PCa patient sequencing data was obtained from the Sequence Read Archive and then analysis pipelines were constructed to map the sequences against chromosome 8 followed by indexing and variant calling. Our studies were able to identify four SNPs specific to DNA sequences isolated from prostate cancer tissues but that were absent in normal tissues. We hope that our findings might help improve personalized medicine and possibly reduce the incidences of overdiagnosis and overtreatment of PCa.
Facing with rapidly increasing demands for analyzing high-order data or multiway data, feature-extracting methods become imperative for analysis and processing. The traditional feature-extracting ...methods, however, either need to overly vectorize the data and smash the original structure hidden in data, such as PCA and PCA-like methods, which is unfavorable to the data recovery, or cannot eliminate the redundant information very well, such as tucker decomposition (TD) and TD-like methods. To overcome these limitations, we propose a more flexible and more powerful tool, called the multiview principal components analysis (Multiview-PCA) in this article. By segmenting a random tensor into equal-sized subarrays called sections and maximizing variations caused by orthogonal projections of these sections , the Multiview-PCA finds principal components in a parsimonious and flexible way. In so doing, two new operations on tensors, the <inline-formula> <tex-math notation="LaTeX">S </tex-math></inline-formula>- direction inner/outer product , are introduced to formulate tensor projection and recovery. With different segmentation ways characterized by section depth and direction , the Multiview-PCA can be implemented many times in different ways, which defines the sequential and global Multiview-PCA, respectively. These multiple Multiview-PCA take the PCA and PCA-like, and TD and TD-like as the special cases, which correspond to the deepest section depth and the shallowest section depth, respectively. We propose an adaptive depth and direction selection algorithm for the implementation of Multiview-PCA. The Multiview-PCA is then tested in terms of subspace recovery ability, compression ability, and feature extraction performance when applied to a set of artificial data, surveillance videos, and hyperspectral imaging data. All numerical results support the flexibility, effectiveness, and usefulness of Multiview-PCA.
•Laboratory experiment: varied soil samples mixed with light/heavy oils.•Soils contaminated with oils can be detected by their emissivity spectra.•Feature depth: versatile identifier, correlates with ...API° and oil concentration.•Spectra show features related to time exposure.•Results relevant for monitoring refineries, pipelines, and naturalcontaminations.
The high risk of environmental contamination posed by the use of petroleum hydrocarbons (HCs) necessitates technologies for detecting leaks. The effectiveness of sensors operating in near infrared (NIR) and short wave (SWIR) have already been demonstrated for this purpose. However, the applicability of thermal data (TIR) remains largely unexplored. This study aims to evaluate the applicability of TIR data for the detection and monitoring of soils contaminated with HCs, by tracking changes in the emissivity of samples in the post-contamination period. A controlled laboratory experiment was conducted in which samples of different mineral substrates were mixed with oils of different APIs° and at different concentrations. The samples were measured using a FTIR spectroradiometer over six months. Emissivity features characteristic of each substrate and HCs were selected and parameterized to analyze their geometric variations. PCA and GLMM were the statistical models used to quantify the variations of these features as a function of i) contamination time; ii) temperature; iii) API°; and iv) oil concentration. Based on the findings, it was demonstrated that TIR is useful for detecting contamination by HCs. However, the patterns are different for each type of substrate. It was identified that the depth of the oil feature is directly proportional to the increase in HC concentration in mixtures with clayey substrates and inversely proportional to sandy substrates. As for API°, in sandy and dolomite soils, the oil feature depth is greater in mixtures with lighter oils and, in the case of clay soils, is greater in mixtures with heavier oils. As for temporal analysis, the oil feature depth in mixtures with dolomitic substrate increases over time. On the other hand, variations in sandy and dolomite soil features showed that the soil feature depth increases as the weeks pass. In relation to temperature, the characteristic of oil and soil feature depth decreases with increasing temperature in all mixtures. Despite the optimistic results, the influence of all these variables and the presence of water, along with the variation of soil and HC features, make the quantification of oil contamination a challenge in the TIR range.
•This paper studies the fault detection of nonlinear system using kernel method.•An online monitoring method for extracting the reduced number of measurements from the training data was proposed.•To ...evaluate the performance of the proposed method is applied for monitoring a Tennessee Eastman Process.
Kernel principal component analysis (KPCA), which is a nonlinear extension of principal component analysis (PCA), has gained significant attention as a monitoring method for nonlinear processes. However, KPCA cannot perform well for dynamic systems and when the training data set is large. Therefore, in this paper, an online reduced KPCA algorithm for process monitoring is proposed. The process monitoring performances are studied using two examples: a numerical example and Tennessee Eastman Process (TEP). The simulation results demonstrate the effectiveness of the proposed method when compared to the online KPCA method.
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•PCA-based quantitation of T1 relaxation data outperforms integration in general.•The actual benefit of using PCA depends on the signal shape and quantitation domain.•PCA-based ...quantitation in the time domain can be performed on full-length FIDs.•Data correction for frequency or phase variations, if any, is a prerequisite for PCA.
Principal component analysis (PCA) has proved to be a powerful technique for processing NMR data. It is particularly useful in signal quantitation where it often provides better results compared to a direct integration of individual signals. In the present work, we recapitulate the principles and theoretical framework underlying PCA-based quantitation with a special focus on T1 relaxometry. We show that under commonly encountered conditions, this approach can provide up to ~4-fold improvement in scatter of points in magnetization build-up curves compared to direct integration. Best practices to optimize the PCA performance in measuring the total magnetization are discussed, including minimization of the number of signal-related principal components and a proper selection of FT parameters and data quantitation intervals. For signals consisting of distinct relaxation components, formulas are provided for resolving the components relaxation and illustrated on a real-data example. In addition to the problem of quantitation, the use of PCA in denoising of partially relaxed spectra is discussed in connection with such applications as line shape analysis and monitoring relaxation of individual spectral components.
To visualise microplastics and nanoplastics via Raman imaging, we need to scan the sample surface over a pixel array to collect Raman spectra as a matrix. The challenge is how to decode this spectrum ...matrix to map accurate and meaningful Raman images. This study compares two decoding approaches. The first approach is used when the sample contains several known types of microplastics whose standard spectra are available. We can map the Raman intensity at selected characteristic peaks as images. In order to increase the image certainty, we employ a logic-based algorithm to merge several images that are simultaneously mapped at several characteristic peaks to one image. However, the rest of the signals other than the selected peaks are ignored, meaning a low signal-noise ratio. The second approach for decoding is used when samples are complicated and standard spectra are not available. We employ principal component analysis (PCA) to decode the spectrum matrix. By selecting principal components (PC) and generating PC score curves to mimic the Raman spectrum, we can justify and assign the suspected items to microplastics and other materials. By mapping the PC loadings as images, microplastics and other materials can be simultaneously visualised. We analyse a sample containing two known microplastics to validate the effectiveness of the PCA-based algorithm. We then apply this method to analyse “unknown” microplastics printed on paper to extract Raman spectra from the complicated background and individually assign the images to paper fabric/additive, black carbon and microplastics, etc. Overall, the PCA-based algorithm shows some advantages and suggests a further step to decode Raman spectrum matrices towards machine learning.
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•Raman imaging enables the direct visualisation and identification of microplastics.•Logic-based and PCA-based algorithm are compared to map image.•Logic-based algorithm can merge several images mapped at different characteristic peaks into one to increase the signal-noise ratio.•PCA-based algorithm can decode the Raman spectrum matrix in the absence of the standard Raman spectrum.
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