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.

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 Multivariate statistical treatment of plant extract compositional data: Average mass scan of the total ion chromatogram (AMS) approach

2013 ◽  
Vol 11 (1) ◽  
pp. 85-99
Author(s):  
Polina Blagojevic ◽  
Niko Radulovic
Keyword(s):  
Clustering Analysis ◽  
Compositional Data ◽  
Statistical Treatment ◽  
Principal Component ◽  
Chemical Compositions ◽  
Agglomerative Hierarchical Clustering ◽  
Multivariate Statistical ◽  
Time Saving ◽  
Average Mass ◽  
Peak Areas

It was recently confirmed that relative abundances of m/z values of the average mass scan of the total GC chromatograms (AMS) are suitable variables for multivariate statistical comparison (MVA) of essential oils. These are even more applicable, reliable and faster than the traditionally used variables-percentages (peak areas) of individual oil constituents. Herein, we have explored if AMS-derived variables are appropriate for MVA comparison of plant solvent extract compositional data. To achieve this, average mass scans of the total GC chromatograms and chemical compositions (relative percentages) of eight diethyl ether extracts (six different species; samples were analyzed using GC-FID and GC-MS; data from the literature) were separately compared using two MVA methods: agglomerative hierarchical clustering analysis and principal component analysis. The obtained results strongly suggest that MVA of complex volatile mixtures (GC-MS analyzable fractions of plant solvent extracts), using the corresponding AMS, could be considered as a promising time saving tool for easy and reliable comparison purposes. The AMS approach gives comparable or even better results than the traditional method.

scholarly journals Genetic Variation for Biofortifying The Maize Grain

2016 ◽  
Vol 4 (8) ◽  
pp. 684 ◽  
Author(s):  
Gönül Cömertpay ◽  
Faheem Shehzad Baloch ◽  
Halil Erdem
Keyword(s):  
Starch Content ◽  
Principal Component ◽  
Mineral Elements ◽  
Quality Traits ◽  
Maize Breeding ◽  
Mineral Contents ◽  
Maize Germplasm ◽  
Geographical Regions ◽  
Maize Landraces ◽  
Maize Grain

The maize germplasm variation is valuable for breeders to develop elite hybrids with increased mineral contents in the maize grain to eliminate mineral malnutrition, which is referred as HIDEN HUNGER. Therefore, we aimed to determine mineral element diversity of maize landraces collected from different geographical regions of Turkey. There was huge diversity for all mineral traits and other quality traits. Turkish maize landraces showed high variation for Zn (17-41.34 mg kg-1), Fe (13.52-29.63 mg kg-1), Cu (0.77-3.34 mg kg-1), Mn (5.68-14.78 mg kg-1), Protein (6.6-11.6%), starch content (73.3-80.0%), oil content (3.15-4.7%) and thousand grain weight (177.0-374.9g). There were significant positive and negative associations among mineral elements and quality traits. The principal component analysis differentiated some maize landraces from the rest, and these diverse landraces could be used in the maize breeding program with biofortification purpose.

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.

Monsoon-driven Dynamics of water quality by multivariate statistical methods in Daya Bay, South China Sea

2012 ◽  
Vol 41 (4) ◽  
Author(s):  
Mei-Lin Wu ◽  
You-Shao Wang ◽  
Cui-Ci Sun ◽  
Fu-Lin Sun ◽  
Hao Cheng ◽  
...  
Keyword(s):  
Sustainable Use ◽  
Principal Component ◽  
Southwest Monsoon ◽  
Daya Bay ◽  
Grouped Data ◽  
Northeast Monsoon ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Monsoon Period ◽  
Natural Processes

AbstractEleven physicochemical parameters of data collected from 12 stations in Daya Bay in 2003 were analyzed by multivariate statistical analysis. Cluster analysis (CA) grouped data from 4 seasons into two groups, the northeast and southwest monsoon periods, representing different natural processes. During the northeast monsoon period, principal component analysis (PCA) and CA group the 12 monitoring sites into Cluster DA1 (S1, S2 and S6) and Cluster DA2 (S3-S5 and S7-S12). During the southwest monsoon period, PCA and CA group the 12 monitoring sites into Cluster WB1 (S1, S2, S7, S9 and S11) and Cluster WB2 (S3-S6, S8, S10, S11 and S12). The spatial heterogeneity within the bay was defined by different hydrodynamic conditions and human activities. These results may be valuable for achieving sustainable use of the coastal ecosystems in Daya Bay.

scholarly journals Visualization of Chemical Space using Kernel Based Principal Component Research

2019 ◽  
Vol 8 (11S) ◽  
pp. 590-593
Keyword(s):  
Principal Component Analysis ◽  
Molecular Descriptors ◽  
Chemical Space ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Non Linear ◽  
Hidden Patterns

Principal Component analysis (PCA) is one of the important and popular multivariate statistical methods applied over various data modeling applications. Traditional PCA handles linear variance in molecular descriptors or features. Handling complicated data by standard PCA will not be very helpful. This drawback can be handled by introducing kernel matrix over PCA. Kernel Principal Component Analysis (KPCA) is an extension of conventional PCA which handles non-linear hidden patterns exists in variables. It results in computational efficiency for data analysis and data visualization. In this paper, KPCA has been applied over dug-likeness dataset for visualization of non-linear relations exists in variables.

scholarly journals Use of multivariate statistical methods for classification of olive oil

2021 ◽  
Vol 6 (1) ◽  
pp. 035-043
Author(s):  
Moacyr Cunha Filho ◽  
Renisson Neponuceno Araujo Filho ◽  
Ana Luiza Xavier Cunha ◽  
Victor Casimiro Piscoya ◽  
Guilherme Rocha Moreira ◽  
...  
Keyword(s):  
Olive Oil ◽  
Statistical Methods ◽  
Virgin Olive Oil ◽  
Principal Component ◽  
Extra Virgin Olive Oil ◽  
Fuzzy Cluster ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Fuzzy Clustering Method ◽  
Silhouette Index

Multivariate statistical methods can contribute significantly to classification studies of extra virgin and common olive oil groups. Therefore, nuclear magnetic resonance (NMR) was used to discriminate olive oil samples, multivariate statistical techniques Principal Component Analysis - PCA, Fuzzy Cluster, Silhouette Validation Method to describe and classify. The groups' distinction into organic and common was observed by applying the non-hierarchical Fuzzy grouping with a distinction between the two groups with a 65% confidence interval. The validation was performed by the silhouette index that presented S (i) of 0.73, which showed that the adopted grouping presented adequate strength and distinction criterion. However, PCA only analyzed the behaviors of data from extra virgin olive oil. Thus, the Fuzzy clustering method was the most suitable for classifying extra virgin olive oil.

scholarly journals Human resources as the precondition of the regional development

2007 ◽  
Vol 55 (6) ◽  
pp. 157-162 ◽  
Author(s):  
Libuše Svatošová
Keyword(s):  
Regional Development ◽  
Regional Policy ◽  
Principal Component ◽  
Human Potential ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Potential Development ◽  
Specific Factors ◽  
Development Evaluation ◽  
Regional Condition

The paper provides information of the possibilities of regional condition and development evaluation with use of multivariate statistical methods. Human potential is regional development´s cruicial factor. Analysis of the human potential development is of fundamental significance in decision-making the field of regional policy. Principal component analysis as principal metod is able to appreciate both gene­ral development trends common to all regions and specific factors´development in particular regions too.

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