scholarly journals Multivariate Statistical Analysis on a SEM/EDS Phase Map of Rare Earth Minerals

Scanning ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chaoyi Teng ◽  
Raynald Gauvin
Keyword(s):  
Rare Earth ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Energy Dispersive Spectrometer ◽  
Nonnegative Matrix ◽  
Component Analysis ◽  
Phase Identification ◽  
Multivariate Statistical ◽  
Rare Earth Minerals ◽  
Phase Map

The scanning electron microscope/X-ray energy dispersive spectrometer (SEM/EDS) system is widely applied to rare earth minerals (REMs) to qualitatively describe their mineralogy and quantitatively determine their composition. The performance of multivariate statistical analysis on the EDS raw dataset can enhance the efficiency and the accuracy of phase identification. In this work, the principal component analysis (PCA) and the blind source separation (BSS) algorithms were performed on an EDS map of a REM sample, assisting to achieve an efficient phase map analysis. The PCA significantly denoised the phase map and was used as a preprocessing step for the following BSS. The BSS separated the mixed EDS signals into a set of physically interpretable components, bringing convenience to the phase separation and identification. Through the comparison between the independent component analysis (ICA) and the nonnegative matrix factorization (NMF) algorithms, the NMF was confirmed to be more suitable for the EDS mapping analysis.

scholarly journals The distribution of the trace element contents in lignite and ash from Drama lignite deposit, using multivariate statistical analysis

2001 ◽  
Vol 34 (3) ◽  
pp. 1255
Author(s):  
S. PANILAS ◽  
G. HATZIYANNIS
Keyword(s):  
Trace Elements ◽  
Rare Earth ◽  
Statistical Analysis ◽  
Rare Earth Elements ◽  
Multivariate Statistical Analysis ◽  
Major And Trace Elements ◽  
Major Elements ◽  
Geochemical Data ◽  
Multivariate Statistical ◽  
Inorganic Matter

Multivariate statistical analysis was used on existing geochemical data of the Drama lignite deposit, eastern Macedonia, Greece. Factor analysis with varimax rotation technique was applied to study the distribution of major, trace and rare earth elements in the lignite and 850°C lignitic ash, to find a small set of factors that could explain most of the geochemical variability. The study showed that major elements AI, Na, Κ, contained in the lignite samples, presented high correlation with most of the trace and rare earth elements. In 850°C lignitic ashes major and trace elements present different redistribution. Only Al remained correlated with the trace elements Co, Cr, Rb, Ta, Th, Ti, Sc and rare earths related with inorganic matter in the lignite beds. Trace elements Fe, Mo, U, V, W, and Lu were associated with organic matter of lignite and had also been affected by the depositional environment.

scholarly journals Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

2019 ◽  
Vol 11 (12) ◽  
pp. 3345 ◽  
Author(s):  
Guowei Liu ◽  
Fengshan Ma ◽  
Gang Liu ◽  
Haijun Zhao ◽  
Jie Guo ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Water Samples ◽  
Multivariate Statistical Analysis ◽  
Water Inrush ◽  
Principal Component ◽  
Component Analysis ◽  
Gold Mine ◽  
Mining Operations ◽  
Multivariate Statistical

Submarine mine water inrush has become a problem that must be urgently solved in coastal gold mining operations in Shandong, China. Research on water in subway systems introduced classifications for the types of mine groundwater and then established the functions used to identify each type of water sample. We analyzed 31 water samples from −375 m underground using multivariate statistical analysis methods. Cluster analysis combined with principle component analysis and factor analysis divided water samples into two types, with one type being near the F3 fault. Principal component analysis identified four principle components accounting for 91.79% of the total variation. These four principle components represented almost all the information about the water samples, which were then used as clustering variables. A Bayes model created by discriminant analysis demonstrated that water samples could also be divided into two types, which was consistent with the cluster analysis result. The type of water samples could be determined by placing Na+ and CHO3− concentrations of water samples into Bayes functions. The results demonstrated that F3, which is a regional fault and runs across the whole Xishan gold mine, may be the potential channel for water inrush, providing valuable information for predicting the possibility of water inrush and thus reducing the costs of the mining operation.

scholarly journals Economic Causes of Extremism Intellectual (Thoughts) Views of Graduate Students Using Multivariate Statistical Techniques

2016 ◽  
Vol 9 (7) ◽  
pp. 160
Author(s):  
Hasan Abdullah Al-Dajah
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Graduate Students ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
The Impact

The present study investigated the impact of the economic reasons on the intellectual (thoughts) extremism, and the statement of the most important indicators in the economic factor that lead to extremism from the views of graduate students. The study problem based on the following question: What are economic factors leading to the extremism of the intellectual(Thoughts)? Correlation coefficient, Principal component analysis (PCA), varimax (F) rotated factor analysis, and dendrogram cluster analysis (DCA) were assessed for the economic impacts that leads to extremism(Thoughts). Multivariate statistical analysis of the dataset and correlation analysis suggested that the strong positive correlations are commonly associated in the poverty and lack of interest in remote areas for major cities Center. Multivariate statistical analysis such as principal component analysis, varimax rotated factor analysis, and dendrogram cluster analysis allowed the identification of three main factors controlling that lead to extremism from the views of graduate students. The extracted factors are as follows: low living expenses, poverty and substantial deprivation, and unequal opportunities and unemployment associations related to prevalence of corruption phase.

Multivariate statistical analysis of elements content in snow cover in Blagoveshchensk

2018 ◽  
Vol 52 (2) ◽  
pp. 15
Author(s):  
V. I. Radomskaya ◽  
D. V. Yusupov ◽  
L. М. Pavlova ◽  
А. G. Sеrgееvа ◽  
N. А. Bоrоdinа ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Snow Cover ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical

Multivariate Statistical Analysis Regarding the Formulation of Oxicam-Based Pharmaceutical Hydrogels

2017 ◽  
Vol 68 (4) ◽  
pp. 726-731
Author(s):  
Lenuta Maria Suta ◽  
Anca Tudor ◽  
Colette Roxana Sandulovici ◽  
Lavinia Stelea ◽  
Daniel Hadaruga ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Pharmaceutical Formulations ◽  
Analysis Data ◽  
Multivariate Statistical ◽  
Predictive Variables ◽  
Rapid Release ◽  
Computational Data ◽  
Experimental Protocols ◽  
Formulation Factors

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.

scholarly journals Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4146
Author(s):  
José Enrique Herbert-Pucheta ◽  
José Daniel Lozada-Ramírez ◽  
Ana E. Ortega-Regules ◽  
Luis Ricardo Hernández ◽  
Cecilia Anaya de Parrodi
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Field Gradient ◽  
Multivariate Statistical Analysis ◽  
Data Matrix ◽  
Cabernet Sauvignon ◽  
Gradient Echo ◽  
Multivariate Statistical ◽  
Orthogonal Projections ◽  
Pulsed Field

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad 1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.

scholarly journals Metabolomic-Based Comparison of Traditional and Industrial Doenjang Samples with Antioxidative Activities

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1377
Author(s):  
Song-Hui Soung ◽  
Sunmin Lee ◽  
Seung-Hwa Lee ◽  
Hae-Jin Kim ◽  
Na-Rae Lee ◽  
...  
Keyword(s):  
Amino Acids ◽  
Statistical Analysis ◽  
Organic Acids ◽  
Biogenic Amines ◽  
Multivariate Statistical Analysis ◽  
Metabolite Profiling ◽  
New Products ◽  
Multivariate Statistical ◽  
Fermentation Processes

Numerous varieties of doenjang are manufactured by many food companies using different ingredients and fermentation processes, and thus, the qualities such as taste and flavor are very different. Therefore, in this study, we compared many products, specifically, 19 traditional doenjang (TD) and 17 industrial doenjang (ID). Subsequently, we performed non-targeted metabolite profiling, and multivariate statistical analysis to discover distinct metabolites in two types of doenjang. Amino acids, organic acids, isoflavone aglycones, non-DDMP (2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4- one) soyasaponins, hydroxyisoflavones, and biogenic amines were relatively abundant in TD. On the contrary, contents of dipeptides, lysophospholipids, isoflavone glucosides and DDMP-conjugated soyasaponin, precursors of the above-mentioned metabolites, were comparatively higher in ID. We also observed relatively higher antioxidant, protease, and β-glucosidase activities in TD. Our results may provide valuable information on doenjang to consumers and manufacturers, which can be used while selecting and developing new products.

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