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.

scholarly journals Application of Multivariate Statistical Methods in the Analysis of Czech Population Life Quality with Attention to Regional Differentiation

2015 ◽  
Vol 63 (5) ◽  
pp. 1671-1678
Author(s):  
Andrea Jindrová
Keyword(s):  
Life Quality ◽  
Regional Disparities ◽  
Regional Differentiation ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
The Social ◽  
Interregional Disparities ◽  
Key Indicators

Quality of life in the regions is affected by many mutually interlinked factors. The paper is aimed at the research of regional disparities in CR population life quality as assessed from the viewpoint of economic efficiency of the region and the social and environmental conditions. The interregional disparities research started from statistical modeling based on identification of key indicators affecting life quality in the CR districts and the outcomes reached have been exploited further for multidimensional classification of districts as to the indicators analyzed. Attention has been paid also to the ways of application of cartographic map facilitating a clear visualization of regional disparities.

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.

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.

scholarly journals Pattern recognition methods as supplementary techniques for identification of salicylamide — cyclodextrins inclusion complexes

2012 ◽  
Vol 10 (5) ◽  
pp. 1534-1546 ◽  
Author(s):  
Ewa Kosecka-Judin ◽  
Marek Wesolowski ◽  
Dominik Paukszta
Keyword(s):  
Pattern Recognition ◽  
Inclusion Complexes ◽  
Solid Phase ◽  
Principal Component ◽  
Phase Solubility ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Pattern Recognition Methods ◽  
X Ray Scattering

AbstractThe objective of this study was to learn whether or not the pattern recognition methods, such as agglomerative cluster analysis (CA) and principal component analysis (PCA), can be used as supplementary techniques for identification of salicylamide (SAA) inclusion complexes with β-cyclodextrin (β-CD) and 2-hydroxypropyl-β-cyclodextrin (HP-β-CD). To do this, phase-solubility of SAA in the presence of the cyclodextrins was studied by the Higuchi-Connors method, which showed that the cyclodextrins enhanced the solubility of SAA in water as compared to that of the drug. Next, the solid phase complexes of the drug with β-CD and HP-β-CD were prepared by using the coprecipitation, precipitation-evaporation, and kneading methods. Identification of the inclusion complexes was performed by using thermal analysis, infrared spectroscopy, and wide angle X-ray scattering. Two multivariate statistical methods, CA and PCA, were used as the supplementary techniques for identification of the inclusion complexes. The results of the statistical analysis have shown that CA and PCA are helpful for interpretation of the thermoanalytical and spectral data. Moreover, these methods enabled proper classification of the products in all doubtful cases. They can be used as supplementary techniques to verify the conclusions of the above-mentioned standard methods.

scholarly journals ANALYSIS OF THE GENETIC DIVERSITY IN MAIZE LANDRACE CULTIVARS FROM NORTHERN RIO GRANDE DO SUL, BRAZIL

2020 ◽  
Vol 19 ◽  
pp. 12
Author(s):  
JÉSSICA ARGENTA ◽  
JEFFERSON GONÇALVES ACUNHA ◽  
BIANCA OLIVEIRA MACHADO ◽  
ARIEL RIZZARDO ◽  
NORYAM BERVIAN BISPO
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Multivariate Statistical Methods ◽  
Maize Breeding ◽  
Multivariate Statistical ◽  
Maize Landrace ◽  
Maize Landraces ◽  
Graphical Integration ◽  
Northern Rio Grande ◽  
Landrace Accessions

Maize landraces are important genetic resources for maize breeding. Many of these landrace varieties have not yet been properly studied in order to be distinguished from the others.  In this study, multivariate statistical methods were used, beyond the analysis of variance, for estimating genetic dissimilarity among 27 maize landrace accessions. Principal component analysis and clustering analysis were performed using 16 evaluated quantitative characters. The ANOVA results reported the existence of significant differences among the tested accessions for 14 evaluated characters. Two principal components almost explained 49% of found experimental variance. Four different clusters were formed by the used clustering analysis, whose results were plotted into a dendrogram. The graphical integration of this dendrogram with the PCA allowed to conclude that the variation found may be due to the genotypic distinctions existing among the four groups of accesses determined in this study.

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 Application of Multidimensional Statistical Analysis in Tribotechnical Diagnostics of Hydraulic Fluids in Woodworking Equipment

Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4628
Author(s):  
Michaela Hnilicová ◽  
Ján Turis ◽  
Richard Hnilica
Keyword(s):  
Data Matrix ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Machine Maintenance ◽  
Hydraulic Fluids ◽  
Quantitative Characteristics ◽  
Friction Surfaces ◽  
Qualitative And Quantitative Characteristics

The article dealt with the assessment of the quality of hydraulic oil and determination of the mode of wear of the friction surfaces of Baljer & Zembrod manipulating lines through the information traces in the oils by applying tribotechnical diagnostics. We presented the assessment of the level of degradation of the oils. In addition, we presented the mode of wear of the friction surfaces washed in oil through evaluation of the qualitative and quantitative characteristics of the particles found in the oil. In detail, we focused on the application of suitable multivariate statistical methods on the data matrix. The article also presents predictive models that can sort oils into groups based on the assessment of quality of the oil and the state of the friction couples. The models can be used in research and in solving practical tasks in tribotechnical diagnostics of hydraulic fluids in woodworking equipment. Our results showed that the manipulation lines were greatly thermically stressed due to inadequate oil and machine maintenance. By correlative integration of all methods used, we could determine the real mode of the wear of the tribologic nodes of the machine. The experiment enabled the early detection of an undesirable process in the tribological node and implementation of corrective measures before the machine would break down.

scholarly journals Effects of wheat cultivar and harvest year on technological quality studied by univariate and multivariate analyses

2008 ◽  
Vol 25 (No. 5) ◽  
pp. 249-258
Author(s):  
I. Švec ◽  
M. Hrušková ◽  
O. Jirsa
Keyword(s):  
Wheat Cultivar ◽  
Principal Component ◽  
Test Results ◽  
Multivariate Statistical ◽  
Wheat Varieties ◽  
Milling Quality ◽  
Technological Quality ◽  
Harvest Year ◽  
Parameter Values

The effects of wheat cultivar and harvest year on the wheat technological quality were studied by univariate and multivariate statistical methods. Two wheat varieties sown in the harvest years 2003–2005 were used, the first one of European (cultivar Bezostaja, RUS), the second one of American origin (cultivar Jagger, USA). The evaluated parameter values indicated otherness of technological quality of the varieties studied, mostly in the milling effectivity and in proteins contents and quality. Principal component analysis (PCA) results suggested these differences, but their verifiability based on ANOVA testing was not proved. The harvest year mostly affected also the milling quality and alveograph parameters. The baking test results were not affected by either of both effects studied. The crop of 2003 had higher proximity to the crop of 2004 than to that of 2005. Multivariate analysis (cluster analysis; CA), was used to evaluate the interaction between the wheat cultivar and harvest year effects. In comparison of these effects rate, the technological quality of American cultivar Jagger was strongly influenced by the cultivar (with exception of Falling Number and gases volume). In contrast, the quality of the European wheat cultivar Bezostaja depended significantly on the harvest year.

Sign in / Sign up

Export Citation Format

Share Document