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

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2136 ◽  
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.

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

2018 ◽  
Vol 20 (1) ◽  
pp. 161-168 ◽  
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.

Student Employees and Recreational Sports Administrators: A Comparison of Perceptions

2006 ◽  
Vol 30 (1) ◽  
pp. 53-69 ◽  
Author(s):  
Gary L. Miller ◽  
Thomas E. Grayson
Keyword(s):  
Recreational Sports ◽  
Multivariate Statistical ◽  
Big Ten ◽  
Work Tasks ◽  
Multivariate Statistical Approach ◽  
Student Employees ◽  
The Mean ◽  
And Cluster Analysis ◽  
The Individual ◽  
Ladder Graphs

This study evaluates the differences in perceptions between student employees and recreational sports administrators over a consistent set of work tasks and responsibilities typically done by student employees in a recreational sports setting. The focus of the study was to provide a method of improving the effectiveness and efficiency by which recreational sports programs deliver their services and programs. Nine of the 11 schools in the Big Ten Conference participated in the study with a total of eighty-five participants taking part. Concept mapping, a multivariate statistical approach using multidimensional scaling and cluster analysis was used to analyze the data. Ninety-five work tasks were sorted for similarity and rated on scales for importance toward attaining recreational sports goals and frequency of performance. Cluster maps, ladder graphs and go-to-zones were developed from the data defining the results of the analysis. Results were presented in a composite form for the nine schools participating in the study with the intent to provide comparison between individual schools and the conference composite as requested. Cluster maps illustrated the levels of importance among the six clusters, ladder graphs demonstrated the differences between the student employees and the recreational sports administrators and go-to zones broke out the individual tasks into areas of alignment, gap zones where either importance or frequency were below the mean, and a “?” zone where neither importance nor frequency rose to the mean rating on that scale. The results allow administrators now to compare, examine, and make decisions based each of the 95 work tasks in a guided manner.

scholarly journals Reconstructing Herbivore Diets: A Multivariate Statistical Approach To Interpreting Compound-Specific Isotope Values

2021 ◽  
Author(s):  
Mickey Hong Yi Chen ◽  
Iain P. Kendall ◽  
Richard P. Evershed ◽  
Amy Bogaard ◽  
Amy K. Styring
Keyword(s):  
Case Studies ◽  
Isotope Analysis ◽  
Statistical Treatment ◽  
Principal Component ◽  
Nitrogen Isotope ◽  
Multivariate Statistical ◽  
Sharp Separation ◽  
Statistical Framework ◽  
Multivariate Statistical Approach ◽  
Isotope Analyses

Abstract Stable nitrogen (N) isotope analysis of bulk tissues is a technique for reconstructing the diets of organisms. However, bulk nitrogen isotope (δ15N) values can be influenced by a variety of metabolic and environmental factors that can confound accurate dietary reconstruction. Compound-specific isotope analyses of amino acids (CSIA-AA) have demonstrated the power of the approach in understanding how the δ15N values of bulk collagen are assembled from the constituent AAs. Furthermore, by connecting these AA δ15N values within a robust biochemical framework interpretation of diet and environment are greatly enhanced. Several new proxies have emerged, built around selected AAs; however, the interconnectedness of AA biosynthetic pathways means that patterning of δ15N values across a wider suite of collagen AAs will occur under different environmental or dietary influences. This work seeks to test this idea by situating CSIA-AA within a robust statistical framework using principal component analysis (PCA) and Bayesian statistics to increase the interpretability of a wider range of AA δ15N values in terms of reconstructing herbivore diet. The model was tested using wild and domestic herbivores from the Neolithic settlements of Çatalhöyük (Turkey), Makriyalos (Greece), and Vaihingen (Germany) as case studies. It was found that at Makriyalos there was a sharp separation between domesticated and wild herbivores, which was present to a lesser extent at Çatalhöyük and not observed at Vaihingen. The case studies presented in this work demonstrate that multivariate statistical treatment of CSIA-AA data can deliver new insights into herbivore diet, exceeding those achievable with the Bayesian model.

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

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 31 ◽  
Author(s):  
Oghenero Ohwoghere-Asuma ◽  
Kizito Aweto ◽  
Chukwuma Ugbe
Keyword(s):  
Groundwater Quality ◽  
Principal Component ◽  
Organic Pollution ◽  
Clayey Sand ◽  
Multivariate Statistical ◽  
Subsurface Geology ◽  
Dissolved Ions ◽  
Considerable Impact ◽  
And Cluster Analysis

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.

scholarly journals THE INFLUENCE OF CHECK DAMS ON FLUVIAL PROCESSES AND RIPARIAN VEGETATION IN MOUNTAIN REACHES OF TORRENTS

2010 ◽  
Vol 41 (3) ◽  
pp. 37 ◽  
Author(s):  
Giuseppe Bombino ◽  
Vincenzo Tamburino ◽  
Demetrio Antonio Zema ◽  
Santo Marcello Zimbone
Keyword(s):  
Riparian Vegetation ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Vegetation Development ◽  
Multivariate Statistic ◽  
Check Dams ◽  
Multivariate Statistic Analysis ◽  
Multivariate Statistical Approach ◽  
Specific Discharge ◽  
Active Channel

The complex hydrogeomorphological processes within the active channel of rivers strongly influence riparian vegetation development and organization, particularly in mountain streams where such processes can be remarkably impacted by engineering control works. In four mountain reaches of Calabrian fiumaras we analyze, through previously arranged methods (integrated by a multivariate statistic analysis), the relationships among hydrogeomorphological river characteristics and structure and the development of riparian vegetation within the active channel in transects located in proximity of check dams and in less disturbed sites. The results of this study demonstrate clear and relevant contrasts, due to the presence of check dams, in the physical and vegetation properties of upstream, downstream and intermediate sites around check dams. The multivariate statistical approach through the Principal Component Analysis (PCA) highlighted evident relationships in all transects between groups of physical and vegetation properties. The regression analysis performed between the vegetation properties and the width:depth ratio or the specific discharge showed very different relationships between groups of transects, due to evident changes in channel morphology and in flow regime locally induced by check dams. Overall we have shown that check dams have far reaching effects in the extent and development of riparian vegetation of mountain torrent reaches, which extend far beyond physical adjustments to changed morphological, hydraulic and sedimentary conditions.

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

1998 ◽  
Vol 81 (5) ◽  
pp. 1087-1095 ◽  
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

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

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
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.

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

1984 ◽  
Vol 14 (3) ◽  
pp. 389-394 ◽  
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.

scholarly journals The 3Rs: Reduction and refinement through a multivariate statistical analysis approach in a behavioural study to unveil anxiolytic effects of natural extracts of Tilia tomentosa

2020 ◽  
Author(s):  
Guendalina Olivero ◽  
Federica Turrini ◽  
Matteo Vergassola ◽  
Raffaella Boggia ◽  
Paola Zunin ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Principal Component ◽  
Experimental Protocol ◽  
Multivariate Statistical ◽  
Data Set ◽  
Natural Extracts ◽  
Multivariate Statistical Approach ◽  
Behavioural Tests ◽  
Tilia Tomentosa

We propose a multivariate statistical approach based on Principal Component Analysis (PCA) as an useful instrument to improve the Rules of Refinement and Reduction in in vivo animal experimentation. We analysed with PCA the preliminary data from a study on the effects of the oral administration of Tilia tomentosa bud extracts (TTBEs) on the behavioural skills of adult and aged male and female mice. PCA allows to rationalize the data set information and to dissect the results, showing connections among variables under study (behavioural parameters) and different trends in the experimental groups (control and TTBEs-administered animals). Our results show that PCA can give some important information that can be useful for the refinement of the experimental protocol, in order to reduce the number of the animals used in the experiments and/or the behavioural tests to get reliable information.

scholarly journals AMSM Summary Papers- (Chapter One- Introduction)

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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