scholarly journals Chemical Fingerprinting of Cryptic Species and Genetic Lineages of Aneura pinguis (L.) Dumort. (Marchantiophyta, Metzgeriidae)

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1180
Author(s):  
Rafał Wawrzyniak ◽  
Wiesław Wasiak ◽  
Beata Jasiewicz ◽  
Alina Bączkiewicz ◽  
Katarzyna Buczkowska
Keyword(s):  
Cluster Analysis ◽  
Cryptic Species ◽  
Plant Material ◽  
Chemical Constituents ◽  
Principal Component ◽  
Molecular Data ◽  
Rapid Identification ◽  
Multivariate Statistical ◽  
Genetic Lineages ◽  
Marker Compounds

Aneura pinguis (L.) Dumort. is a representative of the simple thalloid liverworts, one of the three main types of liverwort gametophytes. According to classical taxonomy, A. pinguis represents one morphologically variable species; however, genetic data reveal that this species is a complex consisting of 10 cryptic species (named by letters from A to J), of which four are further subdivided into two or three evolutionary lineages. The objective of this work was to develop an efficient method for the characterisation of plant material using marker compounds. The volatile chemical constituents of cryptic species within the liverwort A. pinguis were analysed by GC-MS. The compounds were isolated from plant material using the HS-SPME technique. Of the 66 compounds examined, 40 were identified. Of these 40 compounds, nine were selected for use as marker compounds of individual cryptic species of A. pinguis. A guide was then developed that clarified how these markers could be used for the rapid identification of the genetic lineages of A. pinguis. Multivariate statistical analyses (principal component and cluster analysis) revealed that the chemical compounds in A. pinguis made it possible to distinguish individual cryptic species (including genetic lineages), with the exception of cryptic species G and H. The classification of samples based on the volatile compounds by cluster analysis reflected phylogenetic relationships between cryptic species and genetic lineages of A. pinguis revealed based on molecular data.

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

scholarly journals Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

scholarly journals Analysis of the physical-chemical and sensorial properties of Maria type cookies

2013 ◽  
Vol 6 (2) ◽  
pp. 269-280 ◽  
Author(s):  
Daniela Pereira ◽  
Paula M. R. Correia ◽  
Raquel P. F. Guiné
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Sensory Evaluation ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Volume Density ◽  
Physical Parameters ◽  
Multivariate Statistical ◽  
Main Component

Abstract Given the importance of the cookies of type Maria worldwide, and considering the absence of any scientific study setting out their main features, it becomes important to identify the differentiating characteristics of several commercialized brands, in particular related to the chemical, physical and sensory characteristics. In this way, the aim of this work was to study and compare eight different brands of cookies of type Maria. The elemental chemical analysis (moisture, ash, protein, fat, fibre and carbohydrates contents), determination of physical parameters (volume, density, texture and colour) and sensory evaluation of studied cookies were performed. Multivariate statistical methods (Pearson correlation, principal component analysis and cluster analysis) were applied to estimating relationships in analysed data. The results for the elemental analysis showed that the samples were very similar in terms of some components, like for example ashes, while quite different in terms of other components, such as moisture and fat contents. With respect to texture and colour the samples showed, in general, some important differences. In terms of sensory evaluation, the sample C was the one that in most sensory tests gathered the preference of the panellists. The cluster analysis showed that the sample A was much different from the other samples. The results of principal component analysis showed that the main component explains 32.6 % of the total variance, and is strongly related to variables associated to colour.

scholarly journals Multivariate Analysis and its Application for Screening Mungbean [Vigna radiata (L.) Wilczek] Landraces

2022 ◽  
Author(s):  
Berk Benlioglu ◽  
Ugur Ozkan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Vigna Radiata ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
Breeding Programs ◽  
High Level

Background: Mungbean [Vigna radiata (L.) Wilczek] is known as one of the important crop of the Vigna group. In order to determine morphological traits of mungbean, multivariate analysis will provide important advantages in the selection phase of future breeding programs. Multivariate statistical analysis was used to determine and classify these traits. Multivariate analysis, that includes principal component analysis (PCA) and cluster analysis (CA), is considered the best tool for selecting promising genotypes in the future breeding programs. Methods: Eighteen landraces and two species were used to classify morphological traits in this study. Nine different morphological traits were observed during the research period. These are; days to 50% flowering (DFT), plant height (PH), branches per plant (BPP), clusters per plant (CPP), number of pods per cluster (PPC), seed yield per plot (SYPP), biomass yield per plot (BYPP), harvest index (HI), 1000 seed weight (SW). Result: Principal component analysis (PCA) revealed a high level of variation among the genotypes. Therefore, high variability was observed in DFT (36-59 day), PH (39-76 cm), BPP (3-7), CPP (4-21), SYPP (231-824 g), BYPP (3300-10300 g), HI (6.77-11.25%) and 1000 SW (19.95-50.50 g). According to cluster analysis, landraces with the least genetic diversity distance between them in terms of morphological traits examined were determined as 2 and 3.

scholarly journals Variation in Stress Resistance Patterns amongstxGenotypes and Genetic Lineages of Shiga Toxin-Producing Escherichia coli O157

2012 ◽  
Vol 78 (9) ◽  
pp. 3361-3368 ◽  
Author(s):  
Ken-ichi Lee ◽  
Nigel P. French ◽  
Geoff Jones ◽  
Yukiko Hara-Kudo ◽  
Sunao Iyoda ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Cluster Analysis ◽  
Shiga Toxin ◽  
Stress Resistance ◽  
Principal Component ◽  
Content Type ◽  
Genetic Lineages ◽  
Resistance Patterns ◽  
And Cluster Analysis ◽  
Resistance To Heat

ABSTRACTTo evaluate the relationship between bacterial genotypes and stress resistance patterns, we exposed 57 strains of Shiga toxin-producingEscherichia coli(STEC) O157 to acid, freeze-thaw, heat, osmotic, oxidative, and starvation stresses. Inactivation rates were calculated in each assay and subjected to univariate and multivariate analyses, including principal component analysis (PCA) and cluster analysis. Thestxgenotype was determined for each strain as was the lineage-specific polymorphism assay (LSPA6) genotype. In univariate analyses, strains of thestx1stx2genotype showed greater resistance to heat than strains of thestx1stx2cgenotype; moreover, strains of thestx1stx2genotype showed greater resistance to starvation than strains of thestx2orstx2cgenotypes. LSPA6 lineage I (LI) strains showed greater resistance to heat and starvation than LSPA6 lineage II (LII) strains. PCA revealed a general trend that a strain with greater resistance to one type of stress tended to have greater resistance to other types of stresses. In cluster analysis, STEC O157 strains were grouped into stress-resistant, stress-sensitive, and intermediate clusters. Instxgenotypes, all strains of thestx1stx2genotype were grouped with the stress-resistant cluster, whereas 72.7% (8/11) of strains of thestx1stx2cgenotype grouped with the stress-sensitive cluster. In LI strains, 77.8% (14/18) of the strains were grouped with the stress-resistant cluster, whereas 64.7% (11/17) of LII strains were grouped with the stress-sensitive cluster. These results indicate that the genotypes of STEC O157 that are frequently associated with human illness, i.e., LI or thestx1stx2genotype, have greater multiple stress resistance than do strains of other genotypes.

scholarly journals Water Quality Assessment of Porsuk River, Turkey

2010 ◽  
Vol 7 (2) ◽  
pp. 593-599 ◽  
Author(s):  
Suheyla Yerel
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Water ◽  
Surface Water Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Porsuk River

The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management.

Source identification of groundwater pollution with the aid of multivariate statistical analysis

2005 ◽  
Vol 5 (6) ◽  
pp. 281-288 ◽  
Author(s):  
T.N. Wu ◽  
Y.C. Huang ◽  
M.S. Lee ◽  
C.M. Kao
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Ammonia Concentration ◽  
Mineral Dissolution ◽  
Agricultural Activity ◽  
Multivariate Statistical ◽  
The Impact

With the aid of multivariate statistical analysis, this study attempted to predict possible underlying processes, attribute their influence, and isolate the distribution of sources that might threaten groundwater quality. Tainan County, Taiwan was employed as a case study, and 34 monitoring wells were sampled for routine lab analysis. Lab data of groundwater quality including pH, EC, hardness, chloride, sulfate, ammonia, nitrate, Fe, Mn, As, Zn, TOC and TDS were subjected to factor and cluster analysis. Principal component analysis (PCA) was utilized to reflect those chemical data with the greatest correlation, whereas cluster analysis (CA) was used to evaluate the similarities of water quality in groundwater samples. By utilizing PCA, the identified four major principal components (PCs) representing 78.8% of cumulative variance were able to interpret the most information contained in the data. PC 1 reflects the dominance of salinization, which was characterized by the elevated concentrations of EC, hardness, chloride and sulfate in groundwater. PC 2 with the positive loadings of TOC and pH but negative loading of nitrate is thought to be representative of organic pollution within the aquifer. PC 3 is regarded as mineralization factor on the basis of the loadings of manganese and zinc. PC 4 shows a strong monotonic relationship with ammonia concentration in the groundwater revealing the linkage with agricultural activity. CA results illustrated that coastal area was partially salinized as a result of seawater intrusion and part of salinization zone was also subjected to the impact of mineral dissolution.

scholarly journals Multivariate statistical characterization of groundwater quality in Fesdis, East of Algeria

2018 ◽  
Vol 37 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Safia Khelif ◽  
Abderrahmane Boudoukha
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Statistical Characterization ◽  
And Cluster Analysis

AbstractThis study is a contribution to the knowledge of hydrochemical properties of the groundwater in Fesdis Plain, Algeria, using multivariate statistical techniques including principal component analysis (PCA) and cluster analysis. 28 samples were taken during February and July 2015 (14 samples for each month). The principal component analysis (PCA) applied to the data sets has resulted in four significant factors which explain 75.19%, of the total variance. PCA method has enabled to highlight two big phenomena in acquisition of the mineralization of waters. The main phenomenon of production of ions in water is the contact water-rock. The second phenomenon reflects the signatures of the anthropogenic activities. The hierarchical cluster analysis (CA) in R mode grouped the 10 variables into four clusters and in Q mode, 14 sampling points are grouped into three clusters of similar water quality characteristics.

scholarly journals ESTATÍSTICA MULTIVARIADA APLICADA À ANÁLISE DE QUALIDADE DA ÁGUA EM DIFERENTES AMBIENTES DE MICROBACIAS HIDROGRÁFICAS

Nativa ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 185
Author(s):  
Milena Scaramussa Pastro ◽  
Roberto Avelino Cecílio ◽  
Sidney Sara Zanetti ◽  
Francielle Rodrigues de Oliveira ◽  
Fabrina Teixeira Ferraz
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Oxygen Demand ◽  
Principal Component ◽  
River Basin Management ◽  
Multivariate Statistical ◽  
Total Data ◽  
Calcium Magnesium ◽  
The Difference ◽  
Main Components

Estudos sobre o comportamento da qualidade de água são importantes, desse modo, o objetivo deste trabalho foi agrupar as águas estudadas quanto à similaridade e selecionar as características físico-químicas para explicar a variabilidade da qualidade das águas em quatro microbacias. Para tanto, foram selecionadas quatro microbacias com diferentes usos do solo: pastagem, regeneração florestal, floresta e cafezal; sob diferentes ambientes: ambientes lêntico e lótico, nascentes e águas subterrâneas. As coletas ocorreram entre fevereiro de 2014 e dezembro de 2014, sendo analisados: coliformes totais e termotolerantes; oxigênio dissolvido (OD); nitrogênio total (Nt); PO43-; turbidez; temperatura; pH; Demanda Bioquímica de Oxigênio (DBO); condutividade elétrica (CE); sólidos totais (ST); sólidos dissolvidos (SD); sólidos suspensos (SS); e os metais cálcio, magnésio e ferro. Utilizou-se técnicas de análise estatística multivariada, por meio da análise de agrupamento (AA) e análise de componentes principais (ACP). Na AA, foram formados quatro grupos distintos no período chuvoso e três no período seco. A diferença entre os ambientes foi o principal fator de influência na segregação dos grupos. A partir da ACP foram selecionadas 4 componentes principais que explicaram 73,09% da variância total dos dados. As variáveis selecionadas foram CE, turbidez, magnésio, ferro, SD, Nt, DBO, pH e coliformes termotolerantes.Palavras-chave: recursos hídricos; análise de agrupamento; componentes principais; manejo de bacias hidrográficas. MULTIVARIATE STATISTICS APPLIED TO WATER QUALITY IN DIFFERENT HYDROGRAPHIC MICROBASE ENVIRONMENTS ABSTRACT: Studies on the water quality behavior are important, so the objective of this work was to group the studied waters regarding the similarity and to select the physical-chemical characteristics to explain the variability of water quality in four micro-basins. Four micro-basins with different soil uses were selected: pasture, forest regeneration, forest and coffee; under different environments: lentic and lotic environments, springs and groundwater. The collections occurred between February 2014 and December 2014, being analyzed: total coliforms and thermotolerant; dissolved oxygen (OD); total nitrogen (Nt); PO43-; turbidity; temperature; pH; Biochemical Oxygen Demand (BOD); electrical conductivity (EC); total solids (TS); dissolved solids (SD); suspended solids (SS); and the metals calcium, magnesium and iron. Multivariate statistical analysis techniques were used, through cluster analysis (AA) and principal component analysis (PCA). In AA, four distinct groups were formed in the rainy season and three in the dry season. The difference between the environments was the main factor of influence in the segregation of the groups. From the PCA, 4 main components were selected, which explained 73.09% of the total data variance. The selected variables were CE, turbidity, magnesium, iron, SD, Nt, BOD, pH and thermotolerant coliforms.Keywords: water resources; cluster analysis; principal components; river basin management.

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