Journal articles: 'Multivariate analysis – Data processing'

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Guccione, Pietro, Mattia Lopresti, Marco Milanesio, and Rocco Caliandro. "Multivariate Analysis Applications in X-ray Diffraction." Crystals 11, no. 1 (December 25, 2020): 12. http://dx.doi.org/10.3390/cryst11010012.

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Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden in data, disclosing the possibility of automatic data processing even in absence of a priori structural knowledge. MA methods recently used in the field of X-ray diffraction are here reviewed and described, giving hints about theoretical background and possible applications. The use of MA in the framework of the modulated enhanced diffraction technique is described in detail.

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Li, Xiu Min. "Multivariate Regression Analysis Using Statistics with R." Advanced Materials Research 765-767 (September 2013): 1572–75. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1572.

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Multiple regression analysis is a useful model in econometrics. It can be applied in many fields. Statistics software plays an important role in processing data. This paper gives a method to use R, constructs regression model, and explains the result.

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Bartolacci, Gianni, and Ahmed Bouajila. "Application of multivariate tools to mineral processing data analysis and modeling: Flotation case." IFAC Proceedings Volumes 33, no. 22 (August 2000): 179–84. http://dx.doi.org/10.1016/s1474-6670(17)36988-4.

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Kümmel, Anne, Paul Selzer, Martin Beibel, Hanspeter Gubler, Christian N. Parker, and Daniela Gabriel. "Comparison of Multivariate Data Analysis Strategies for High-Content Screening." Journal of Biomolecular Screening 16, no. 3 (February 18, 2011): 338–47. http://dx.doi.org/10.1177/1087057110395390.

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High-content screening (HCS) is increasingly used in biomedical research generating multivariate, single-cell data sets. Before scoring a treatment, the complex data sets are processed (e.g., normalized, reduced to a lower dimensionality) to help extract valuable information. However, there has been no published comparison of the performance of these methods. This study comparatively evaluates unbiased approaches to reduce dimensionality as well as to summarize cell populations. To evaluate these different data-processing strategies, the prediction accuracies and the Z′ factors of control compounds of a HCS cell cycle data set were monitored. As expected, dimension reduction led to a lower degree of discrimination between control samples. A high degree of classification accuracy was achieved when the cell population was summarized on well level using percentile values. As a conclusion, the generic data analysis pipeline described here enables a systematic review of alternative strategies to analyze multiparametric results from biological systems.

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Wang, Lijun, Yu Lei, Ying Zeng, Li Tong, and Bin Yan. "Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data." Computational and Mathematical Methods in Medicine 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/645921.

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Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

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Parachalil, Drishya Rajan, Brenda Brankin, Jennifer McIntyre, and Hugh J. Byrne. "Raman spectroscopic analysis of high molecular weight proteins in solution – considerations for sample analysis and data pre-processing." Analyst 143, no. 24 (2018): 5987–98. http://dx.doi.org/10.1039/c8an01701h.

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This study explores the potential of Raman spectroscopy, coupled with multivariate regression techniques and ion exchange chromatography, to quantitatively monitor diagnostically relevant changes in high molecular weight proteins in liquid plasma.

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Apruzzese, Francesca, Ramin Reshadat, and Stephen T. Balke. "In-Line Monitoring of Polymer Processing. II: Spectral Data Analysis." Applied Spectroscopy 56, no. 10 (October 2002): 1268–74. http://dx.doi.org/10.1366/000370202760354713.

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The objective of this work was to examine the application of various multivariate methods to determine the composition of a flowing, molten, immiscible, polyethylene–polypropylene blend from near-infrared spectra. These spectra were acquired during processing by monitoring the melt with a fiber-optic-assisted in-line spectrometer. Undesired differences in spectra obtained from identical compositions were attributed to additive and multiplicative light scattering effects. Duplicate blend composition data were obtained over a range of 0 to 100% polyethylene. On the basis of previously published approaches, three data preprocessing methods were investigated: second derivative of absorbance with respect to wavelength (d2), multiplicative scatter correction (MSC), and a combination consisting of MSC followed by d2. The latter method was shown to substantially improve superposition of spectra and principal component analysis (PCA) scores. Also, fewer latent variables were required. The continuum regression (CR) approach, a method that encompasses ordinary least squares (OLS), partial least squares (PLS), and principle component regression (PCR) models, was then implemented and provided the best prediction model as one based on characteristics between those of PLS and OLS models.

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Yajie, Li, Lv Zhengdong, and Wang Maonan. "Visualization Investigation on the Marine Data with Multivariate Statistical Analysis Methods." Polish Maritime Research 24, s2 (August 28, 2017): 89–94. http://dx.doi.org/10.1515/pomr-2017-0069.

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Abstract Marine information is an important way for us to know and study more about the ocean. Marine data makes the basic of marine information. Because of the huge quantity and diversity of marine data, and at the same time marine data is polyatomic variable, we start with statistical analysis methods to search for the regularity of the marine data. On one hand, we get the aggregate variation functions of the marine data by factor analyzing in aspect of the spatiality. Then we visually describe the marine status of the studied sea area with pre variogram function and post variogram function. On the other hand, we used cluster analysis method to get the verifying rule in time and make visible graphs of the marine data. In this way, we can also supply with the suggestions in classifying the sea seawater quality. The data processing result shows that the suggested methods in this article are both operable and effective. At the same time some reasonable suggestions are given in the article.

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Riani, Marco, Anthony C. Atkinson, Andrea Cerioli, and Aldo Corbellini. "Efficient robust methods via monitoring for clustering and multivariate data analysis." Pattern Recognition 88 (April 2019): 246–60. http://dx.doi.org/10.1016/j.patcog.2018.11.016.

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Rodríguez-Ruiz, Julieta G., Carlos Eric Galván-Tejada, Sodel Vázquez-Reyes, Jorge Issac Galván-Tejada, and Hamurabi Gamboa-Rosales. "Classification of Depressive Episodes Using Nighttime Data: Multivariate and Univariate Analysis." Proceedings of the Institute for System Programming of the RAS 33, no. 2 (2021): 115–24. http://dx.doi.org/10.15514/ispras-2021-33(2)-6.

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Mental disorders like depression represent 28% of global disability, it affects around 7.5% percent of global disability. Depression is a common disorder that affects the state of mind, normal activities, emotions, and produces sleep disorders. It is estimated that approximately 50% of depressive patients suffering from sleep disturbances. In this paper, a data mining process to classify depressive and not depressive episodes during nighttime is carried out based on a formal method of data mining called Knowledge Discovery in Databases (KDD). KDD guides the process of data mining with stages well established: Pre-KDD, Selection, Pre-processing, Transformation, Data Mining, Evaluation, and Post-KDD. The dataset used for the classification is the DEPRESJON dataset, which contains the motor activity of 23 unipolar and bipolar depressed patients and 32 healthy controls. The classification is carried out with two different approaches; a multivariate and univariate analysis to classify depressive and non-depressive episodes. For the multivariate analysis, the Random Forest algorithm is implemented with a model construct of 8 features, the results of the classification are specificity equal to 0.9927 and sensitivity equal to 0.9991. The univariate analysis shows that the maximum of the activity is the most descriptive characteristic of the model with 0.908 in accuracy for the classification of depressive episodes.

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Coggan, David, Timothy Andrews, and Daniel Baker. "Investigating the temporal properties of visual object processing using a multivariate analysis of EEG data." Journal of Vision 16, no. 12 (September 1, 2016): 1311. http://dx.doi.org/10.1167/16.12.1311.

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Caliandro, Rocco, and Danilo Benny Belviso. "RootProf: software for multivariate analysis of unidimensional profiles." Journal of Applied Crystallography 47, no. 3 (May 10, 2014): 1087–96. http://dx.doi.org/10.1107/s1600576714005895.

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RootProfis a multi-purpose program which implements multivariate analysis of unidimensional profiles. Series of measurements, performed on related samples or on the same sample by varying some external stimulus, are analysed to find trends in data, classify them and extract quantitative information. Qualitative analysis is performed by using principal component analysis or correlation analysis. In both cases the data set is projected in a latent variable space, where a clustering algorithm classifies data points. Group separation is quantified by statistical tools. Quantitative phase analysis of a series of profiles is implemented by whole-profile fitting or by an unfolding procedure, and relies on a variety of pre-processing methods. Supervised quantitative analysis can be applied, provideda prioriinformation on some samples is provided.RootProfcan be applied to measurements from different techniques, which can be combined by means of a covariance analysis. A specific analysis for powder diffraction data allows estimation of the average size of crystal domains.RootProfborrows its graphics and data analysis capabilities from the Root framework, developed for high-energy physics experiments.

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Singh, Omkar. "Physiological Time Series Processing via Empirical Wavelet Transform." Advanced Science, Engineering and Medicine 12, no. 5 (May 1, 2020): 582–87. http://dx.doi.org/10.1166/asem.2020.2557.

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This paper presents the efficacy of empirical wavelet transform (EWT) for physiological time series processing. At first, EWT is applied to multivariate heterogeneous physiological time series. Secondly, EWT is used for the removal of fast temporal scales in multiscale entropy analysis. Empirical mode decomposition is an adaptive data analysis method in the sense that it does not require prior information about the signal statistics and tend to decompose a signal into various constituent modes. The utility of Standard EMD algorithm is however limited to single channel data as it suffers from the problems of mode alignment and mode mixing when applied channel wise for multivariate data. The standard EMD algorithm was extended to multivariate Empirical mode decomposition (MEMD) that can be used analyze a multivariate data. The MEMD can only be applied to multivariate data in which all the channels have equal data length. EWT is another adaptive technique for mode extraction in a signal using empirical scaling and wavelet functions. The multiscale entropy (MSE) algorithm is generally used to quantify the complexity of a time series. The original MSE approach utilizes a coarse-graining process for the removal of fast temporal scales in a time series which is equivalent to applying a finite impulse response (FIR) moving average filter. In Refined Multiscale entropy (RMSE), the FIR filter was replaced with a low pass Butterworth filter which exhibits a better frequency response than that of a FIR filter. In this paper we have presented a new approach for the removal of fast temporal scales based on empirical wavelet transform. The empirical wavelet transform is also used as an innovative filtering approach in multiscale entropy analysis.

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Shah, Nauman, and Stephen J. Roberts. "Dynamically Measuring Statistical Dependencies in Multivariate Financial Time Series Using Independent Component Analysis." ISRN Signal Processing 2013 (June 2, 2013): 1–14. http://dx.doi.org/10.1155/2013/434832.

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We present a computationally tractable approach to dynamically measure statistical dependencies in multivariate non-Gaussian signals. The approach makes use of extensions of independent component analysis to calculate information coupling, as a proxy measure for mutual information, between multiple signals and can be used to estimate uncertainty associated with the information coupling measure in a straightforward way. We empirically validate relative accuracy of the information coupling measure using a set of synthetic data examples and showcase practical utility of using the measure when analysing multivariate financial time series.

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Setiawan, Irwan, and Suprihanto Suprihanto. "Exploratory data analysis of crime report." Matrix : Jurnal Manajemen Teknologi dan Informatika 11, no. 2 (July 15, 2021): 71–80. http://dx.doi.org/10.31940/matrix.v11i2.2449.

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Visualization of data is the appearance of data in a pictographic or graphical form. This form facilitates top management to understand the data visually and get the messages of difficult concepts or identify new patterns. The approach of the personal understanding to handle data; applying diagrams or graphs to reflect vast volumes of complex data is more comfortable than presenting over tables or statements. In this study, we conduct data processing and data visualization for crime report data that occurred in the city of Los Angeles in the range of 2010 to 2017 using R language. The research methodology follows five steps, namely: variables identification, data pre-processing, univariate analysis, bivariate analysis, and multivariate analysis. This paper analyses data related to crime variables, time of occurrence, victims, type of crime, weapons used, distribution, and trends of crime, and the relationship between these variables. As the result shows, by using those methods, we can gain insights, understandings, new patterns, and do visual analytics from the existing data. The variations of crime variables presented in this paper are only a few of the many variations that can be made. Other variations can be performed to get more insights, understandings, and new patterns from the existing data. The methods can be performed on other types of data as well.

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Gerget, Olga M., Olga V. Marukhina, and Yulia A. Cherkashina. "System for Visualizing and Analyzing Multivariate Data of Medico-Social Research." Key Engineering Materials 685 (February 2016): 957–61. http://dx.doi.org/10.4028/www.scientific.net/kem.685.957.

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The paper presents a system for visualizing and analyzing multivariate data for distinguishing the major aspects of aggregating data without applying the quantitative analysis method and instant diagnosis of a pregnant woman’s health condition. The paper justifies the necessity of processing multivariate data with the use of the “NovoSpark Visualizer” specialized software suite. An illustrating example as well as the evaluation of pregnant women’s health on the basis of real data is presented.

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Honda, Nakaji, and Futoshi Sugimoto. "Multivariate data representation and analysis by face pattern using facial expression characteristics." Pattern Recognition 19, no. 1 (January 1986): 85–94. http://dx.doi.org/10.1016/0031-3203(86)90037-3.

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Mandic, Danilo P., Naveed ur Rehman, Zhaohua Wu, and Norden E. Huang. "Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis." IEEE Signal Processing Magazine 30, no. 6 (November 2013): 74–86. http://dx.doi.org/10.1109/msp.2013.2267931.

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Mallette, Jennifer R., John F. Casale, Valerie L. Colley, David R. Morello, and James Jordan. "Changes in illicit cocaine hydrochloride processing identified and revealed through multivariate analysis of cocaine signature data." Science & Justice 58, no. 2 (March 2018): 90–97. http://dx.doi.org/10.1016/j.scijus.2017.12.003.

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Ding, X. F., A. Vishneva, Ö. Penek, and S. Marcocci. "Speeding up complex multivariate data analysis in Borexino with parallel computing based on Graphics Processing Unit." Journal of Physics: Conference Series 1342 (January 2020): 012115. http://dx.doi.org/10.1088/1742-6596/1342/1/012115.

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Suta, Lenuta Maria, Anca Tudor, Colette Roxana Sandulovici, Lavinia Stelea, Daniel Hadaruga, Constantin Mircioiu, and Germaine Savoiu Balint. "Multivariate Statistical Analysis Regarding the Formulation of Oxicam-Based Pharmaceutical Hydrogels." Revista de Chimie 68, no. 4 (May 15, 2017): 726–31. http://dx.doi.org/10.37358/rc.17.4.5539.

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In this paper, it was analysed the influence of formulation factors over obtaining oxicam hydrogels, using the statistical analysis. Data analysis and predictive modeling by multivariate regression offers a large number of possible explanatory/predictive variables. Therefore, variable selection and dimension reduction is a major task for multivariate statistical analysis, especially for multivariate regressions. The statistical analysis and computational data processing of responses obtained from different pharmaceutical formulations, via different experimental protocols, lead to the optimization of the formulation process. It was found that the most suitable pharmaceutical formulations based on oxicams with the possibility of rapid release contained cyclodextrin, in particular 2-hydroxypropyl-b-cyclodextrin.

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Voronov, Alexey, Atsushi Urakawa, Wouter van Beek, Nikolaos E. Tsakoumis, Hermann Emerich, and Magnus Rønning. "Multivariate curve resolution applied to in situ X-ray absorption spectroscopy data: An efficient tool for data processing and analysis." Analytica Chimica Acta 840 (August 2014): 20–27. http://dx.doi.org/10.1016/j.aca.2014.06.050.

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Prevost, Paoline, Kristel Chanard, Luce Fleitout, Eric Calais, Damian Walwer, Tonie van Dam, and Michael Ghil. "Data-adaptive spatio-temporal filtering of GRACE data." Geophysical Journal International 219, no. 3 (September 19, 2019): 2034–55. http://dx.doi.org/10.1093/gji/ggz409.

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SUMMARY Measurements of the spatio-temporal variations of Earth’s gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission have led to new insights into large spatial mass redistribution at secular, seasonal and subseasonal timescales. GRACE solutions from various processing centres, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial patterns in the latter. In order to dampen the noise and enhance the geophysical signals in the GRACE data, we propose an approach based on a data-driven spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA). M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability. We perform an M-SSA analysis on 13 yr of GRACE spherical harmonics solutions from five different processing centres in a simultaneous setup. We show that the method allows us to extract common modes of variability between solutions, while removing solution-specific spatio-temporal errors that arise from the processing strategies. In particular, the method efficiently filters out the spurious north–south stripes, which are caused in all likelihood by aliasing, due to the imperfect geophysical correction models and low-frequency noise in measurements. Comparison of the M-SSA GRACE solution with mass concentration (mascons) solutions shows that, while the former remains noisier, it does retrieve geophysical signals masked by the mascons regularization procedure.

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Martynova, V. F., and D. V. Solomatin. "MULTIVARIATE ANALYSIS IN PEDAGOGY ON THE EXAMPLE OF RESEARCH OF A CHILDREN’S PUBLIC ORGANIZATION." Review of Omsk State Pedagogical University. Humanitarian research, no. 31 (2021): 169–75. http://dx.doi.org/10.36809/2309-9380-2021-31-169-175.

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The article provides examples of statistical data processing of pedagogical research of children’s public organization using methods of multivariate analysis and identification of a causal relationship.

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Robitaille, Annie, Graciela Muniz, Andrea M. Piccinin, Boo Johansson, and Scott M. Hofer. "Multivariate Longitudinal Modeling of Cognitive Aging." GeroPsych 25, no. 1 (January 2012): 15–24. http://dx.doi.org/10.1024/1662-9647/a000051.

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We illustrate the use of the parallel latent growth curve model using data from OCTO-Twin. We found a significant intercept-intercept and slope-slope association between processing speed and visuospatial ability. Within-person correlations among the occasion-specific residuals were significant, suggesting that the occasion-specific fluctuations around individual’s trajectories, after controlling for intraindividual change, are related between both outcomes. Random and fixed effects for visuospatial ability are reduced when we include structural parameters (directional growth curve model) providing information about changes in visuospatial abilities after controlling for processing speed. We recommend this model to researchers interested in the analysis of multivariate longitudinal change, as it permits decomposition and directly interpretable estimates of association among initial levels, rates of change, and occasion-specific variation.

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Dusold, Laurence R., and John A. G. Roach. "Computer Assistance in Food Analysis." Journal of AOAC INTERNATIONAL 69, no. 5 (September 1, 1986): 754–56. http://dx.doi.org/10.1093/jaoac/69.5.754.

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Abstract Laboratory computer links are a key part of acquisition, movement, and interpretation of certain types of data. Remote information retrieval from databases such as the Chemical Information System provides the analyst with structural and toxicologicai information via a laboratory terminal. Remote processing of laboratory data by large computers permits the application of pattern recognition techniques to the solution of complex multivariate problems such as the detection of food adulteration.

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Dumas, Marc-Emmanuel, Cécile Canlet, Laurent Debrauwer, Pascal Martin, and Alain Paris. "Selection of Biomarkers by a Multivariate Statistical Processing of Composite Metabonomic Data Sets Using Multiple Factor Analysis." Journal of Proteome Research 4, no. 5 (October 2005): 1485–92. http://dx.doi.org/10.1021/pr050056y.

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MacGregor, J. F., T. Kourti, J. Liu, J. Bradley, K. Dunn, and H. Yu. "MULTIVARIATE METHODS FOR THE ANALYSIS OF DATA-BASES, PROCESS MONITORING, AND CONTROL IN THE MATERIAL PROCESSING INDUSTRIES." IFAC Proceedings Volumes 40, no. 11 (2007): 193–98. http://dx.doi.org/10.3182/20070821-3-ca-2919.00028.

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Ginoris, Y. P., A. L. Amaral, A. Nicolau, M. A. Z. Coelho, and E. C. Ferreira. "Raw data pre-processing in the protozoa and metazoa identification by image analysis and multivariate statistical techniques." Journal of Chemometrics 21, no. 3-4 (2007): 156–64. http://dx.doi.org/10.1002/cem.1054.

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Drumm, Charlene A., and Michael D. Morris. "Microscopic Raman Line-Imaging with Principal Component Analysis." Applied Spectroscopy 49, no. 9 (September 1995): 1331–37. http://dx.doi.org/10.1366/0003702953965326.

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Reconstructed Raman images of the distribution of polystyrene and polyethylene were obtained by line-imaging, with the use of univariate and multivariate processing of the spectral data. Multiple sets of microscopic Raman spectral line-images were acquired with line-focused illumination, a motorized translation stage to move the sample perpendicular to the illumination line, and a holographic imaging spectrograph equipped with a 2D detector. The line-imaged raw spectral data were processed with the use of both a simple univariate method (single-band integration) and a more sophisticated multivariate method (principal component analysis with eigenvector rotation) to generate two-dimensional Raman images representing spatial distribution of the polymers. In the spectral range employed (900–1700 cm−1), suitable bands could be found for univariate processing of the polystyrene and polyethylene images. The principal component analysis method gave equivalent separation of the images, but only if the entire spectral window was employed to generate the eigenvectors.

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Solo, Victor, and Syed Ahmed Pasha. "Point-Process Principal Components Analysis via Geometric Optimization." Neural Computation 25, no. 1 (January 2013): 101–22. http://dx.doi.org/10.1162/neco_a_00382.

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There has been a fast-growing demand for analysis tools for multivariate point-process data driven by work in neural coding and, more recently, high-frequency finance. Here we develop a true or exact (as opposed to one based on time binning) principal components analysis for preliminary processing of multivariate point processes. We provide a maximum likelihood estimator, an algorithm for maximization involving steepest ascent on two Stiefel manifolds, and novel constrained asymptotic analysis. The method is illustrated with a simulation and compared with a binning approach.

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Chiang, J., Z. J. Wang, and M. J. McKeown. "A Hidden Markov, Multivariate Autoregressive (HMM-mAR) Network Framework for Analysis of Surface EMG (sEMG) Data." IEEE Transactions on Signal Processing 56, no. 8 (August 2008): 4069–81. http://dx.doi.org/10.1109/tsp.2008.925246.

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COSTA, JOSÉ ALFREDO FERREIRA, and MÁRCIO LUIZ DE ANDRADE NETTO. "Estimating the Number of Clusters in Multivariate Data by Self-Organizing Maps." International Journal of Neural Systems 09, no. 03 (June 1999): 195–202. http://dx.doi.org/10.1142/s0129065799000186.

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Determining the structure of data without prior knowledge of the number of clusters or any information about their composition is a problem of interest in many fields, such as image analysis, astrophysics, biology, etc. Partitioning a set of n patterns in a p-dimensional feature space must be done such that those in a given cluster are more similar to each other than the rest. As there are approximately [Formula: see text] possible ways of partitioning the patterns among K clusters, finding the best solution is very hard when n is large. The search space is increased when we have no a priori number of partitions. Although the self-organizing feature map (SOM) can be used to visualize clusters, the automation of knowledge discovery by SOM is a difficult task. This paper proposes region-based image processing methods to post-processing the U-matrix obtained after the unsupervised learning performed by SOM. Mathematical morphology is applied to identify regions of neurons that are similar. The number of regions and their labels are automatically found and they are related to the number of clusters in a multivariate data set. New data can be classified by labeling it according to the best match neuron. Simulations using data sets drawn from finite mixtures of p-variate normal densities are presented as well as related advantages and drawbacks of the method.

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Escuredo, Olga, María Shantal Rodríguez-Flores, Laura Meno, and María Carmen Seijo. "Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing." Foods 10, no. 2 (February 3, 2021): 317. http://dx.doi.org/10.3390/foods10020317.

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There is an increase in the consumption of natural foods with healthy benefits such as honey. The physicochemical composition contributes to the particularities of honey that differ depending on the botanical origin. Botanical and geographical declaration protects consumers from possible fraud and ensures the quality of the product. The objective of this study was to develop prediction models using a portable near-Infrared (MicroNIR) Spectroscopy to contribute to authenticate honeys from Northwest Spain. Based on reference physicochemical analyses of honey, prediction equations using principal components analysis and partial least square regression were developed. Statistical descriptors were good for moisture, hydroxymethylfurfural (HMF), color (Pfund, L and b* coordinates of CIELab) and flavonoids (RSQ > 0.75; RPD > 2.0), and acceptable for electrical conductivity (EC), pH and phenols (RSQ > 0.61; RDP > 1.5). Linear discriminant analysis correctly classified the 88.1% of honeys based on physicochemical parameters and botanical origin (heather, chestnut, eucalyptus, blackberry, honeydew, multifloral). Estimation of quality and physicochemical properties of honey with NIR-spectra data and chemometrics proves to be a powerful tool to fulfil quality goals of this bee product. Results supported that the portable spectroscopy devices provided an effective tool for the apicultural sector to rapid in-situ classification and authentication of honey.

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Lucchesi, Simone, Simone Furini, Donata Medaglini, and Annalisa Ciabattini. "From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies." Vaccines 8, no. 1 (March 20, 2020): 138. http://dx.doi.org/10.3390/vaccines8010138.

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Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.

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Watada, Junzo, Keisuke Aoki, Masahiro Kawano, and Muhammad Suzuri Hitam. "Dual Scaling in Data Mining from Text Databases." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 4 (July 20, 2006): 451–57. http://dx.doi.org/10.20965/jaciii.2006.p0451.

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The availability of multimedia text document information has disseminated text mining among researchers. Text documents, integrate numerical and linguistic data, making text mining interesting and challenging. We propose text mining based on a fuzzy quantification model and fuzzy thesaurus. In text mining, we focus on: 1) Sentences included in Japanese text that are broken down into words. 2) Fuzzy thesaurus for finding words matching keywords in text. 3) Fuzzy multivariate analysis to analyze semantic meaning in predefined case studies. We use a fuzzy thesaurus to translate words using Chinese and Japanese characters into keywords. This speeds up processing without requiring a dictionary to separate words. Fuzzy multivariate analysis is used to analyze such processed data and to extract latent mutual related structures in text data, i.e., to extract otherwise obscured knowledge. We apply dual scaling to mining library and Web page text information, and propose integrating the result in Kansei engineering for possible application in sales, marketing, and production.

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Li, Guang Hui, Zheng Pei, Ran Chen, and Da Li Hu. "The Principal Component Analysis of Signal Characteristic Parameters." Applied Mechanics and Materials 128-129 (October 2011): 1269–72. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1269.

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With the development of wireless communication, the automatic modulation recognition technology of communicational signals becomes an important theme. In this paper, based on multivariate statistical principal component analysis, we select characteristics of the signal, and use radio monitoring equipment mined measurement data to do some processing and analysis, which provided technical support to automatic modulation recognition of the radio signal.

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Hazarika, Subhashis, Soumya Dutta, Han-Wei Shen, and Jen-Ping Chen. "CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data." IEEE Transactions on Visualization and Computer Graphics 25, no. 1 (January 2019): 1214–24. http://dx.doi.org/10.1109/tvcg.2018.2864801.

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Hemmer, Selina, Sascha K. Manier, Svenja Fischmann, Folker Westphal, Lea Wagmann, and Markus R. Meyer. "Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data." Metabolites 10, no. 9 (September 21, 2020): 378. http://dx.doi.org/10.3390/metabo10090378.

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The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.

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Fernández-Getino, A. P., Z. Hernández, A. Piedra Buena, and G. Almendros. "Exploratory analysis of the structural variability of forest soil humic acids based on multivariate processing of infrared spectral data." European Journal of Soil Science 64, no. 1 (January 31, 2013): 66–79. http://dx.doi.org/10.1111/ejss.12016.

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González-Mohino, Alberto, Trinidad Pérez-Palacios, Teresa Antequera, Jorge Ruiz-Carrascal, Lary Souza Olegario, and Silvia Grassi. "Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device." Foods 9, no. 9 (September 14, 2020): 1294. http://dx.doi.org/10.3390/foods9091294.

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This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950–1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square–discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.

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Titchmarsh, J. M. "Extraction of Information From Stem-Edx Segregation Profiles Using Multivariate Statistical Analysis." Microscopy and Microanalysis 3, S2 (August 1997): 937–38. http://dx.doi.org/10.1017/s1431927600011570.

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Multivariate Statistical Analysis (MSA) has been applied to spectroscopic data acquired by various methods but only recently to EDX spectra acquired in the transmission electron microscope where segregation sensitivity was improved by a factor of x2-3. Equilibrium segregation data were ideally suited for MSA analysis. Each spectrum was composed of a linear sum of contributions from the segregation layer and the two adjacent grains or phases, in proportions determined by the probe current distribution and location. MSA successfully identified an eigenvalue explicitly associated with segregation. Smaller eigenvalues related to self-absorption and coherent bremsstrahlung (CB) were also revealed. The use of an orthogonal MSA constrained the information sources to be independent. This constraint, which was reasonable for equilibrium segregation, has now been examined for a more complicated case: thermally and radiation-induced diffusion near an interface where profiles derived by conventional processing have varying features (Fig.l) from which it is difficult to extract real concentration profiles or even qualitative correlations between elements.

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Mansouri, Edris, Faranak Feizi, Alireza Jafari Rad, and Mehran Arian. "Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran." Solid Earth 9, no. 2 (March 28, 2018): 373–84. http://dx.doi.org/10.5194/se-9-373-2018.

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Abstract. This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

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Clavijo, Nayher, Afrânio Melo, Maurício M. Câmara, Thiago Feital, Thiago K. Anzai, Fabio C. Diehl, Pedro H. Thompson, and José Carlos Pinto. "Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant." Processes 7, no. 7 (July 10, 2019): 436. http://dx.doi.org/10.3390/pr7070436.

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Predictive analytics is usually cited as one of the most important pillars of the digital transformation. For the oil industry, specifically, it is a common belief that issues like integrity and maintenance could benefit from predictive analytics. This paper presents the development and the application of a process-monitoring tool in a real process facility. The PMA (Predictive Maintenance Application) system is a data-driven application that uses a multivariate analysis in order to predict the system behavior. Results show that the use of a multivariate approach for process monitoring could not only detect an early failure at a metering system days before the operation crew, but could also successfully identify, among hundreds of variables, the root cause of the abnormal situation. By applying such an approach, a better performance of the monitored equipment is expected, decreasing its downtime.

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Luty, Lidia. "ORGANIC FARMING IN POLAND IN THE LIGHT OF MULTIVARIATE COMPARATIVE ANALYSIS." Acta Scientiarum Polonorum. Oeconomia 16, no. 4 (December 30, 2017): 113–21. http://dx.doi.org/10.22630/aspe.2017.16.4.50.

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Organic farming is an environmentally-friendly production system, which has been dynamically developing since 2004. The study attempts to conduct a spatial assessment of the development of this management method. The analysis covered data at the level of voivodships, originating from GIJHARS1 and from the Central Statistical Office (GUS) from 2014, concerning producers respecting the production in the environmentally-friendly system. They include characteristics such as: average surface area, proportion of the area of arable lands, number of processing plants, production volume of: milk, cereals, vegetables, and fruit. The analysis uses the method of linear ordering of a set of objects, based on the created synthetic variable. The results of the study suggest that Polish voivodships are generally characterised by an average or low level of development of organic farming. A positive phenomenon is observed in the fact that organic farming develops in voivodships with a more fragmented agrarian structure.

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Bale, Kim, Paul Chapman, Nick Barraclough, Jon Purdy, Nizamettin Aydin, and Paul Dark. "Kaleidomaps: A New Technique for the Visualization of Multivariate Time-Series Data." Information Visualization 6, no. 2 (January 2007): 155–67. http://dx.doi.org/10.1057/palgrave.ivs.9500154.

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In this paper, we describe a new visualization technique that can facilitate our understanding and interpretation of large complex multivariate time-series data sets. ‘Kaleidomaps’ have been carefully developed taking into account research into how we perceive form and structure within Glass patterns. We have enhanced the classic cascade plot using the curvature of a line to alter the detection of possible periodic patterns within multivariate dual periodicity data sets. Similar to Glass patterns, the concentric nature of the Kaleidomap may induce a motion signal within the brain of the observer facilitating the perception of patterns within the data. Kaleidomaps and our associated visualization tools alter the rapid identification of periodic patterns not only within their own variants but also across many different sets of variants. By linking this technique with traditional line graphs and signal processing techniques, we are able to provide the user with a set of visualization tools that permit the combination of multivariate time-series data sets in their raw form and also with the results of mathematical analysis. In this paper, we provide two case study examples of how Kaleidomaps can be used to improve our understanding of large complex multivariate time dependent data.

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Goceri, Evgin. "Future Healthcare: Will Digital Data Lead to Better Care?" New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 8 (December 9, 2017): 07–11. http://dx.doi.org/10.18844/gjapas.v0i8.2781.

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Currently, datasets used in bioinformatics and computational biology are high-dimensional, complex and multivariate. Analysis and processing of data is vital in medicine; however, manual analysis and pattern recognition with big data is difficult, and processing of large and weakly connected datasets is challenging. The increasing complexity of healthcare systems causes high health cost. To provide better healthcare services at reduced prices, computer-aided tools using smart approaches and context-aware computations are of great importance. Advancements in wireless network technology, mobile devices and pattern recognition applications help solve the cost problem of healthcare systems. In the future, patients will be able to participate in healthcare as their own health manager and observe important parameters like body fat amount and blood pressure. However, open issues related to this topic exist. In this paper, we present a survey of smart healthcare environments and smart hospitals and discuss some questions and challenges in this area. Keywords: Future healthcare, healthcare system, smart hospitals, smart environments.

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Ghizdavet, Zeno, Iuliana Madalina Stanciu, Alina Melinescu, and Adelina Ianculescu. "Correlations Composition - Processing - Microstructure - Properties for Ceria - Based Solid Electrolytes." Revista de Chimie 68, no. 5 (June 15, 2017): 1044–50. http://dx.doi.org/10.37358/rc.17.5.5608.

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The main objective of this work is to study the correlation between composition, microstructure, processing and properties of doped ceria. Doped ceria with heterovalent cations can be used as solid electrolyte for solid oxide fuel cells working at intermediate temperatures (SOFC-IT). This study is made in order to obtain information, which can be useful for further improvement of this type of materials. Multivariate analysis techniques, such as Principal Component Analysis (PCA) and Self-Organizing Maps (SOMs) were used on a database consisting of 18 samples which differ by composition and by processing methods. Experimental data regarding these samples consisted in information about composition, SEM images and the property of choice, the electrical conductivity. SEM images were digitally processed to isolate grain boundaries; on the 18 resulting images it was computed the Fractal Dimension, as a parameter containing both information about size and shape of the grains, therefore characterising the microstructure. All results were, then, subjected to Multivariate analysis taking into account, also, the processing methods. The aim is to extract useful information from the available experimental data. Results encourage us to assume that even identification and diagnose can become possible based on the present work, given an adequate database. The overall procedure can be applied to any crystalline material.

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Huzsvai, László, Péter Fejér, Árpád Illés, Csaba Bojtor, Csilla Bojté, Éva Horváth, and Cintia Demeter. "Analysis of sweet corn nutritional values using multivariate statistical methods." Acta Agraria Debreceniensis, no. 1 (June 1, 2021): 103–8. http://dx.doi.org/10.34101/actaagrar/1/8587.

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Processing large amounts of data provided by automated analytical equipment requires carefulness. Most mathematical and statistical methods have strict application conditions. Most of these methods are based on eigenvalue calculations and require variables to be correlated in groups. If this condition is not met, the most popular multivariate methods cannot be used. The best procedure for such testing is the Kaiser-Meyer-Olkin test for Sampling Adequacy. Two databases were examined using the KMO test. One of them resulted from the sweet corn measured in the scone of the study, while the other from the 1979 book of János Sváb. For both databases, MSA (measures sampling adequacy) was well below the critical value, thus they are not suitable e.g. for principal component analysis. In both databases, the values of the partial correlation coefficients were much higher than Pearson’s correlation coefficients. Often the signs of partial coefficients did not match the signs of linear correlation coefficients. One of the main reasons for this is that the correlation between the variables is non-linear. Another reason is that control variables have a non-linear effect on a given variable. In such cases, classical methods should be disregarded and expert models better suited to the problem should be chosen in order to analyse the correlation system.

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Xu, Yangyang, Hao Cai, Gang Cao, Yu Duan, Ke Pei, Jia Zhou, Li Xie, et al. "Discrimination of volatiles in herbal formula Baizhu Shaoyao San before and after processing using needle trap device with multivariate data analysis." Royal Society Open Science 5, no. 6 (June 2018): 171987. http://dx.doi.org/10.1098/rsos.171987.

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To characterize the chemical differences of volatile components between crude and processed Baizhu Shaoyao San (BSS), a classical Chinese herbal formula that is widely applied in the treatment of gastrointestinal diseases, we developed a gas chromatography–mass spectrometry-based needle trap device combined with multivariate data analysis to globally profile volatile components and rapidly identify differentiating chemical markers. Using a triple-bed needle packed with Carbopack X, DVB and Carboxen 1000 sorbents, we identified 121 and 123 compounds, respectively, in crude and processed BSS. According to the results of principal component analysis and orthogonal partial least-squares discriminant analysis, crude and processed BSS were successfully distinguished into two groups with good fitting and predicting parameters. Furthermore, 21 compounds were identified and adopted as potential markers that could be employed to quickly differentiate these two types of samples using S-PLOT and variable importance in projection analyses. The established method can be applied to explain the chemical transformation of Chinese medicine processing in BSS and further control the quality and understand the processing mechanism of Chinese herbal formulae. Besides, the triple-bed needle selected and optimized in this study can provide a valuable reference for other plant researches with similar components. Furthermore, the systematic research on compound identification and marker discrimination of the complex components in crude and processed BSS could work as an example for other similar studies, such as composition changes in one plant during different growth periods, botanical characters of different medicinal parts in same kind of medicinal herbs and quality identification of one species of medicinal herb from different regions.

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Journal articles on the topic 'Multivariate analysis – Data processing'

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