Lithofacies Identification and Multivariate Analysis of Groundwater Chemistry in Coastal Aquifers in Koko Area of the Western Niger Delta

Understanding aquifer lithofacies and depth of occurrence, and what factors influence its quality and chemistry are of paramount importance to the management of groundwater resource. Subsurface lithofacies distribution was characterized by resistivity and validated with available subsurface geology. Resistivity values varied from less than 100 Ωm to above 1000 Ωm. Lithofacies identified includes clay, clayey sand, sand and peat. Shallow unconfined and confined aquifers occurred at depths ranging from 0 to 12 m and 18 to 63 m, respectively. Geochemistry and multivariate statistical analysis consisting of principal component analysis (PCA) and cluster analysis (CA) were used for the determination of quality and groundwater evolution. Groundwater types depicted by Piper plots were Ca3+, Cl− and Na+, Cl−, which was characterized by low dissolved ions, slightly acidic and Fe2+. The dominant variables influencing groundwater quality as returned by PCA were organic pollution resulting from swampy depositional environment, anthropogenic effects resulting from septic and leachates from haphazard dumpsites mixing with groundwater from diffuse sources. In addition, the weathering and dissolution of aquifer sediments rich in feldspar and clay minerals have considerable impact on groundwater quality. CA depicted two distinct types of groundwater that are significantly comparable to those obtained from Piper plots.

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Evaluation of the anthropogenic impact in Suat Ugurlu Dam lake using multivariate statistical techniques

Global NEST Journal ◽

10.30955/gnj.002387 ◽

2018 ◽

Vol 20 (1) ◽

pp. 161-168 ◽

Cited By ~ 1

Keyword(s):

Anthropogenic Impact ◽

Sediment Quality ◽

Principal Component ◽

Statistical Techniques ◽

Multivariate Statistical Techniques ◽

Cluster Number ◽

Multivariate Statistical ◽

Oxidation Reduction ◽

Oxidation Reduction Potential ◽

And Cluster Analysis

Sediments play an important role in the quality of aquatic ecosystems in the Dam Lake where they can either be a sink or a source of contaminants, depending on the management. This purpose of this study is to identify the sediment quality in order to find out the causes for the malodor and the eutrophication that is causing a bad scenario. Solutions for improving the dam are proposed. Multivariate statistical techniques, such as a principal component analysis (PCA) and cluster analysis (CA), were applied to the data regarding sediment quality in relation to anthropogenic impact in Suat Ugurlu Dam Lake. This data was generated during 2014-2015, with monitoring at four sites for 11 parameters. A PCA and CA were used in the study of the samples. The total variance of 84.1%, 74.3%, 87.4% and 91.5% suggest 4, 3, 3 and 4 principle components (PCs) in the four locations: LC1, LC2, LC3 and LC4, respectively. Also, a CA was applied to both the variables and the observations. Some variables and observations showed a high similarity based on the results of variables in the CA. Also, the similarity ratio of temperature-mercury (Hg) and oxidation reduction potential (ORP) was high and generally, the cluster number of variables was 5, according to the selected similarity level.

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DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest

Molecules ◽

10.3390/molecules23092136 ◽

2018 ◽

Vol 23 (9) ◽

pp. 2136 ◽

Cited By ~ 6

Author(s):

Patrycja Garbacz ◽

Marek Wesolowski

Keyword(s):

Principal Component ◽

Water Soluble ◽

Crystal Formation ◽

Multivariate Statistical ◽

Molar Ratios ◽

Pharmaceutical Ingredients ◽

Multivariate Statistical Approach ◽

And Cluster Analysis ◽

Dsc Curves ◽

Physical Mixtures

Co-crystals have garnered increasing interest in recent years as a beneficial approach to improving the solubility of poorly water soluble active pharmaceutical ingredients (APIs). However, their preparation is a challenge that requires a simple approach towards co-crystal detection. The objective of this work was, therefore, to verify to what extent a multivariate statistical approach such as principal component analysis (PCA) and cluster analysis (CA) can be used as a supporting tool for detecting co-crystal formation. As model samples, physical mixtures and co-crystals of indomethacin with saccharin and furosemide with p-aminobenzoic acid were prepared at API/co-former molar ratios 1:1, 2:1 and 1:2. Data acquired from DSC curves and FTIR and Raman spectroscopies were used for CA and PCA calculations. The results obtained revealed that the application of physical mixtures as reference samples allows a deeper insight into co-crystallization than is possible with the use of API and co-former or API and co-former with physical mixtures. Thus, multivariate matrix for PCA and CA calculations consisting of physical mixtures and potential co-crystals could be considered as the most profitable and reliable way to reflect changes in samples after co-crystallization. Moreover, complementary interpretation of results obtained using DSC, FTIR and Raman techniques is most beneficial.

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Correlation Between Quality and Geographical Origins of Leonuri Herba Revealed By the Qualitative Fingerprint Profiling and Quantitative Determination of Chemical Components

10.21203/rs.3.rs-1102170/v1 ◽

2021 ◽

Author(s):

Kelly Yinching Lam ◽

Yinghao Wang ◽

Tszking Lam ◽

Chuenfai Ku ◽

Wingping Yeung ◽

...

Keyword(s):

Principal Component ◽

Chinese Herbs ◽

Chemical Components ◽

Chemical Compositions ◽

Chemical Fingerprints ◽

Chemical Discrimination ◽

Chromatographic Peaks ◽

And Cluster Analysis ◽

Different Origins

Abstract BackgroundLeonuri Herba (Yimucao) is a very commonly Chinese herbs for treating menstrual and maternal diseases for thousands of years in China. However, the herb collected in different origins was easily found in the markets which induce the unstable quality for clinic use. In this study, a comprehensive strategy of using multiple chromatographic analysis and chemometric analysis was firstly investigated for chemical discrimination of Leonuri Herba from different geographical origins.MethodsUHPLC-QTOF-MS/MS was applied to identify the peaks of Leonuri Herba and chemical fingerprints were established in 30 batches from different geographical origins. Meanwhile, dissimilarities of chemical compositions among different origins were further investigated by principal component analysis and cluster analysis.ResultsA total of 49 chromatographic peaks of Leonuri Herba were unequivocally or tentatively identified by UHPLC-QTOF-MS/MS. Leonuri Herba were classified into four categories, and eight major compounds detected could be used as chemical markers for discrimination. Also, the eight components, including leonurine, 4',5-dihydroxy-7-methoxyflavone, rutin, hyperoside, apigenin, quercetin, kaempferol and salicylic acid, were simultaneously quantified using the extracting ion mode of UHPLC-QTOF-MS/MS.ConclusionThis systematic information could ensure Leonuri Herba with well-controlled quality and safe use in clinic. This study could also provide a research model for further study of other Chinese Materia Medica.

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Application of multivariate statistical analysis in the assessment of groundwater quality of Tan Thanh district, Ba Ria – Vung Tau province

Science & Technology Development Journal - Science of The Earth & Environment ◽

10.32508/stdjsee.v1im2.446 ◽

2017 ◽

Vol 1 (M2) ◽

pp. 66-72

Author(s):

Au Hai Nguyen ◽

Ngan Thi Khanh Phan ◽

Thuy Thi Thanh Hoang ◽

Ngoc Nguyen Hong Phan

Keyword(s):

Cluster Analysis ◽

Statistical Analysis ◽

Groundwater Quality ◽

Multivariate Statistical Analysis ◽

Anthropogenic Pollution ◽

Dry Season ◽

Wet Season ◽

Multivariate Statistical ◽

Rock Interaction ◽

And Cluster Analysis

In the present study, Multivariate Statistical Analysis (MSA) such as Principle Component Analysis (PCA) and Cluster Analysis (CA) were applied to determine the temporal and spatial variations of groundwater quality in Tan Thanh district, Ba Ria – Vung Tau province. Groundwater samples were collected from 18 monitoring wells in April (dry season) and October (wet season) during the year 2012. Fifteen parameters (pH, TH, TDS, Cl-, F-, NO3-, SO42-, Cr6+, Cu2+, Ca2+, Mg2+, Na+, K+, HCO3- and Fe2+) were selected for MSA. PCA identified a reduced number of mean three latent factors of groundwater quality. Three factors called salinization, water-rock interaction and anthropogenic pollution explanined 70,5% (dry season) and 71.28% (wet season) of the variances. Cluster analysis revealed two main different groups of similarities between the sampling sites. This study presents the necessity of MSA in order to extract more precise information from a huge minitoring data, which will be usefull to groundwater quality management.

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

Water Science & Technology Water Supply ◽

10.2166/ws.2021.390 ◽

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.

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Characterization of Balsamic Vinegar by Multivariate Statistical Analysis of Trace Element Content

Journal of AOAC International ◽

10.1093/jaoac/81.5.1087 ◽

1998 ◽

Vol 81 (5) ◽

pp. 1087-1095 ◽

Cited By ~ 5

Author(s):

Antonella Del Signore ◽

Barbara Campisi ◽

Franco Di Giacomo

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Trace Element Content ◽

The Other ◽

Statistical Techniques ◽

Multivariate Statistical ◽

Linear Discriminant ◽

Balsamic Vinegar ◽

And Cluster Analysis

Abstract To characterize vinegars according to the types prescribed by Italian regulations, 8 trace elements (Cr, Mn, Co, Ni, Cu, Zn, Cd, and Pb) were determined. The data collected were successively elaborated by 3 statistical techniques: linear principal component analysis (LPCA), linear discriminant analysis (LDA), and cluster analysis (CA). LDA and LPCA best classified and discriminated the 3 types of vinegar under study, separating traditional balsamic vinegars from the other 2 types, nontraditionally aged balsamic vinegars and common vinegars. The latter 2 types were appreciably distinguished only by LDA through bidimensional analysis of discriminant scores

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Multivariate Statistical Analysis of Hydrochemical Data and Stable Isotopes of Groundwater Contaminated with Nitrate at Huay Sai Royal Development Study Center and Adjacent Areas in Phetchaburi Province, Thailand

Water ◽

10.3390/w12041127 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1127 ◽

Cited By ~ 1

Author(s):

Srilert Chotpantarat ◽

Tewanopparit Parkchai ◽

Wanlapa Wisitthammasri

Keyword(s):

Stable Isotopes ◽

Statistical Analysis ◽

Groundwater Quality ◽

Multivariate Statistical Analysis ◽

Principal Component ◽

Potassium Nitrate ◽

Multivariate Statistical ◽

Hydrochemical Data ◽

Hydrogeological Characteristics ◽

Groundwater Wells

Due to the continuous expansion in agriculture production and industry for many years, groundwater usage has been increasing, with a decrease in groundwater levels in many cases. In addition, in some areas, groundwater quality has degraded due to agrochemical contamination from agricultural areas. The aims of this research pertains to aquifers as follows: (1) to evaluate hydrochemical characteristics of groundwater using multivariate statistical analysis, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), and (2) to integrate the stable isotopes 18O and 2H with hydrochemical data to evaluate the origin of the groundwater and indirectly identify the pollution sources of groundwater contaminated with nitrate (NO3). Water samples were collected from 60 groundwater wells with different hydrogeological characteristics and land use types in both the rainy season (in October) and the summer seasons (in February) in the Cha Am district of Phetchaburi Province. The groundwater was separated into 3 types: Ca-Na-Cl, Ca-Na-HCO3-Cl, and Na-Cl. Two groundwater wells (no. 19 and 41), which were located southeast and southwest of the study area, had relatively high NO3− concentrations (47 mg/L NO3 and 50 mg/L NO3, respectively) that were higher than the groundwater quality standards. These two wells corresponded to the second group that was exposed by HCA. The PCA results revealed the influence of seawater intrusion. Furthermore, multivariate statistical analysis (PC 2) revealed that the NO3− that is mainly released from potassium nitrate (KNO3), for example, during pineapple cultivation, directly contaminated the groundwater system.

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Monitoring the Modifications of the Vitreous Humor Metabolite Profile after Death: An Animal Model

BioMed Research International ◽

10.1155/2015/627201 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 12

Author(s):

Maria Francesca Rosa ◽

Paola Scano ◽

Antonio Noto ◽

Matteo Nioi ◽

Roberta Sanna ◽

...

Keyword(s):

Principal Component ◽

Metabolite Profile ◽

Vitreous Humor ◽

Multivariate Statistical ◽

H Nmr ◽

Multivariate Statistical Approach ◽

Compositional Changes ◽

Latent Structures ◽

Metabolomic Approach

We applied a metabolomic approach to monitor the modifications occurring in goat vitreous humor (VH) metabolite composition at different times (0, 6, 12, 18, and 24 hours) after death. The1H-NMR analysis of the VH samples was performed for the simultaneous determination of several metabolites (i.e., the metabolite profile) representative of the VHstatusat different times. Spectral data were analyzed by Principal Component Analysis (PCA) and by Orthogonal Projection to Latent Structures (OPLS) regression technique. PCA and OPLS suggested that different spectral regions were involved in time-related changes. The major time-related compositional changes, here detected, were the increase of lactate, hypoxanthine, alanine, total glutathione, choline/phosphocholine, creatine, andmyo-inositol and the decrease of glucose and 3-hydroxybutyrate. We attempted a speculative interpretation of the biological mechanisms underlying these changes. These results show that multivariate statistical approach, based on1H NMR metabolite profiling, is a powerful tool for detecting ongoing differences in VH composition and may be applied to investigate several physiological and pathological conditions.

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The climatic regions of New Brunswick: a multivariate analysis of meteorological data

Canadian Journal of Forest Research ◽

10.1139/x84-069 ◽

1984 ◽

Vol 14 (3) ◽

pp. 389-394 ◽

Cited By ~ 11

Author(s):

H. van Groenewoud

Keyword(s):

New Brunswick ◽

Classification Scheme ◽

Meteorological Data ◽

Principal Component ◽

Site Classification ◽

Forest Site ◽

Multivariate Statistical ◽

Climatic Regions ◽

Geological Maps ◽

And Cluster Analysis

New Brunswick was divided into 11 climatic regions by means of three multivariate statistical analyses (principal component analysis, R and Q type, and cluster analysis) of data on precipitation, various temperature parameters, elevation, latitude, and longitude for 76 climatological stations. These regions form the first-level division for a forest site classification scheme being implemented in New Brunswick. Comparison of the climatic and geological maps of New Brunswick with the plant community distribution shows that either climatic or geologic parameters may control the distribution of the vegetation.

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AMSM Summary Papers- (Chapter One- Introduction)

10.31219/osf.io/n8a2x ◽

2018 ◽

Author(s):

Mohammed R. Dahman

Keyword(s):

Linear Regression ◽

Structural Equation ◽

Principal Component ◽

Descriptive Statistics ◽

Basic Knowledge ◽

Equation Modeling ◽

Multivariate Statistical ◽

Statistics And Probability ◽

Prerequisite Skills ◽

And Cluster Analysis

In the upcoming some 40 summary papers I will demonstrate a comprehensive view of Applied Multivariate Statistical Modeling. First, I will start with a thorough introduction of AMSM. Then, I will explain the univariate descriptive statistics, sampling distribution, estimation, in addition to hypothesis testing. After that, I will do a comprehensive review of multivariate descriptive statistics, the normal distribution of it, and the inferential statistics. Having we accomplished that, it will be the time to discuss some various models: ANOVA, MANOVA, Multiple Linear Regression, and Multivariate Linear Regression. Furthermore, we will discuss, Principal Component analysis, Factor Analysis, and Cluster Analysis. At the end of this series of summaries, some intro to structural equation modeling (SEM), and correspondence analysis will be discussed. Prerequisite skills are, of which readers must have, basic knowledge of statistics and probability, in addition to some advanced knowledge of linear algebra. I have published summary papers in both disciplines, see the reference page.

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