scholarly journals Geochemical classification of groundwater using multivariate statistical analysis in Latvia

2016 ◽  
Vol 47 (4) ◽  
pp. 799-813 ◽  
Author(s):  
Inga Retike ◽  
Andis Kalvans ◽  
Konrads Popovs ◽  
Janis Bikse ◽  
Alise Babre ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Methods ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Ion Chemistry ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Gypsum Dissolution ◽  
Major Ion

Multivariate statistical methods – principal component analysis (PCA) and hierarchical cluster analysis (HCA) – are applied to identify geochemically distinct groundwater groups in the territory of Latvia. The main processes observed to be responsible for groundwater chemical composition are carbonate and gypsum dissolution, fresh and saltwater mixing and ion exchange. On the basis of major ion concentrations, eight clusters (C1–C8) are identified. C6 is interpreted as recharge water not in equilibrium with most sediment forming minerals. Water table aquifers affected by diffuse agricultural influences are found in C3. Groundwater in C4 reflects brine or seawater admixture and gypsum dissolution in C5. C7 and C2 belong to typical bicarbonate groundwater resulting from calcite and dolomite weathering. Extremely low Cl− and SO42− are observed in C8 and described as pre-industrial groundwater or a solely carbonate weathering result. Finally, C1 seems to be a poorly defined subgroup resulting from mixing between other groups. This research demonstrates the validity of applying multivariate statistical methods (PCA and HCA) on major ion chemistry to distribute characteristic trace elements in each cluster even when incomplete records of trace elements are present.

Evaluation of colon cancer elements contents in serum using statistical methods

2007 ◽  
Vol 61 (5) ◽  
Author(s):  
D. Milde ◽  
J. Macháček ◽  
V. Stužka
Keyword(s):  
Statistical Methods ◽  
Tnm Classification ◽  
Principal Component ◽  
Absorption Spectrometry ◽  
Acid Mixture ◽  
Multivariate Statistical Methods ◽  
Serum Samples ◽  
Multivariate Statistical ◽  
Colorectal Cancer Patients

AbstractClassification of normal and different cancer groups (TNM classification) with univariate and multivariate statistical methods according to the contents of Cu, Fe, Mn, Se, and Zn in blood serum is discussed. All serum samples were digested by acid mixture in a microwave mineralization unit prior to the analysis by atomic absorption spectrometry. Results show that univariate methods can distinguish normal and cancer groups. Level of selenium evaluated as arithmetic mean with its standard deviation in colorectal cancer patients was (42.61 ± 23.76) µg L−1. Retransformed mean was used to evaluate levels of managanese (11.99 ± 1.71) µg L−1, copper (1.05 ± 0.06) mg L−1, zinc (2.14 ± 0.21) mg L−1, and iron (1.82 ± 0.22) mg L−1. Conclusions of multivariate statistical procedures (principal component analysis, hierarchical, and k-means clustering) do not correlate very well with the division of serum samples according to the TNM classification.

Evaluation of graphical and multivariate statistical methods for classification of water chemistry data

2002 ◽  
Vol 10 (4) ◽  
pp. 455-474 ◽  
Author(s):  
Cüneyt Güler ◽  
Geoffrey D. Thyne ◽  
John E. McCray ◽  
Keith A. Turner
Keyword(s):  
Water Chemistry ◽  
Statistical Methods ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Chemistry Data

scholarly journals Metabolomics Approach for Discrimination and Quality Control of Natural and Commercial Fallopia multiflora Products in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Nguyen Thi Thoa ◽  
Nguyen Hai Dang ◽  
Do Hoang Giang ◽  
Nguyen Thi Thu Minh ◽  
Nguyen Tien Dat
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Fda Guidance ◽  
Promising Tool ◽  
Precision And Accuracy ◽  
And Control

A precise HPLC-DAD-based quantification together with the metabolomics statistical method was developed to distinguish and control the quality of Fallopia multiflora, a popular medicinal material in Vietnam. Multivariate statistical methods such as hierarchical clustering analysis and principal component analysis were utilized to compare and discriminate six natural and twelve commercial samples. 2,3,4′,5-Tetrahydroxystilbene 2-O-β-D-glucopyranoside (THSG) (1), emodin (4), and the new compound 6-hydroxymusizin 8-O-α-D-apiofuranosyl-(1⟶6)-β-D-glucopyranoside (5) could be considered as important markers for classification of F. multiflora. Furthermore, seven phenolics were quantified that the variation in the contents of selected metabolites revealed the differences in the quality of natural and commercial samples. Recovery of the compounds from the analytes was more than 98%, while the limits of detection (LOD) and the limits of quantitation (LOQ) ranged from 0.5 to 6.6 μg/ml and 1.5 to 19.8 μg/ml, respectively. The linearity, LOD, LOQ, precision, and accuracy satisfied the criteria FDA guidance on bioanalytical methods. Overall, this method is a promising tool for discrimination and quality assurance of F. multiflora products.

Multivariate Techniques of Use in Biological Research

1969 ◽  
Vol 5 (1) ◽  
pp. 67-77 ◽  
Author(s):  
S. C. Pearce
Keyword(s):  
Principal Component Analysis ◽  
Statistical Methods ◽  
Matrix Algebra ◽  
Principal Component ◽  
Component Analysis ◽  
Canonical Analysis ◽  
Biological Research ◽  
Multivariate Techniques ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical

SUMMARYMultivariate statistical methods are used increasingly in biological research to investigate the responses of organisms considered as a whole, whereas established statistical methods are usually concerned with measured characteristics considered one at a time. Multivariate techniques are mostly explained in terms of matrix algebra, which is a way of dealing with groups of numbers rather than individual ones. A brief description is given of some elementary results of matrix algebra and a method is presented whereby hypotheses can be generated about interrelations within an organism. Two techniques, principal component analysis and canonical analysis, are described in greater detail. It is emphasized that hypotheses need to be tested even though they have been generated by objective statistical means.

scholarly journals Classification of Specialized Farms Applying Multivariate Statistical Methods

2017 ◽  
Vol 65 (3) ◽  
pp. 1007-1014
Author(s):  
Zuzana Hloušková ◽  
Marie Prášilová
Keyword(s):  
Statistical Methods ◽  
Classification Model ◽  
Medium Size ◽  
Small Farms ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Discriminatory Analysis ◽  
Data Source ◽  
Economic Size

Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly), and the Large and Very Large enterprises (100 % filed correctly). The Medium Size farms have been correctly filed at 58.11 % only. Partial shortages of the process presented have been found when discriminating Medium and Small farms.

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