Correlation of Heavy Metal Concentrations with Various Factors in Canned Liver Paste Products Using Multivariate Statistical Strategies

2004 ◽  
Vol 67 (9) ◽  
pp. 1927-1932 ◽  
Author(s):  
G. BRITO ◽  
K. NOVOTNÁ ◽  
E. M. PEÑA-MÉNDEZ ◽  
C. DÍAZ ◽  
F. J. GARCÍA
Keyword(s):  
Control Charts ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Absorption Spectrometry ◽  
Statistical Techniques ◽  
Multivariate Statistical ◽  
Heavy Metal Concentrations ◽  
Multivariate Quality Control ◽  
Heat Treated ◽  
Exploratory Data

The content of Cu, Fe, Mn, Zn, Co, Cr, Ni, and Pb were determined in 496 samples of heat-treated canned liver pastes by atomic absorption spectrometry. Canned samples were classified according to the presence or absence of coated varnish on the inner side of the can. For each sample, two subsamples were taken: one from the area in contact with the side of the can, the other from the center of the container. Univariate (correlation, box and whisker) and multivariate (quality control charts, principal component analysis, and factor analysis) statistical techniques were applied to detect the presence of outliers and for exploratory data analysis. No significant differences (P < 0.05) were found between the subsamples considered, presence or absence of coated varnish, the sampling areas, or countries of origin. The multivariate analysis allows for the interpretation of grouping tendencies in samples. Cr, Ni, and Pb were associated with presence or absence of oxide in the can, and the essential metals (Fe, Cu, Zn, and Co) were associated with the kind of can. The samples tended to differentiate according to the type of container.

scholarly journals MAIN MINERALS AND ORGANIC COMPOUNDS IN COMMERCIAL ROASTED AND GROUND COFFEE: AN EXPLORATORY DATA ANALYSIS

Química Nova ◽  
2020 ◽  
Author(s):  
Daneysa Kalschne ◽  
Nathalia Silva ◽  
Cristiane Canan ◽  
Marta Benassi ◽  
Eder Flores ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Absorption Spectrometry ◽  
Molecular Absorption ◽  
Wet Digestion ◽  
Caffeoylquinic Acid ◽  
Flame Atomic Absorption ◽  
Molecular Absorption Spectrometry ◽  
Exploratory Data

Coffee is one of the most popular beverages in the world, however, little information is found regarding the mineral composition of commercial roasted and ground coffees (RG) and its correlation with organic bioactive compounds. 21 commercial Brazilian RG coffee brands - 9 traditional (T) and 12 extra strong (ES) roasted ones - were analyzed for the Cu, Ca, Mn, Mg, K, Zn, and Fe minerals, caffeine, 5-caffeoylquinic acid (5-CQA) and melanoidins contents. For minerals determination by flame atomic absorption spectrometry (FAAS), the samples were decomposed by microwave-assisted wet digestion. Caffeine and 5-CQA were determined by liquid chromatography and melanoidins by molecular absorption spectrometry. The minerals and organic compounds contents association in RG coffee was observed by a principal component analysis. The thermostable compounds (minerals and caffeine) were related to dimension 1 and 2, while 5-CQA and melanoidins were related to dimension 3, allowing for the T coffees segmentation from ES ones.

Methods and Graphical Tools for Exploratory Data Analysis of Artificial Olfaction Experiments

2012 ◽  
pp. 317-339 ◽  
Author(s):  
Matteo Falasconi ◽  
Matteo Pardo ◽  
Giorgio Sberveglieri
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Linear Mapping ◽  
Multivariate Statistical ◽  
Human Errors ◽  
Full Dataset ◽  
Exploratory Data ◽  
Visualization Of Data ◽  
Data Investigation

Visualization and initial examination of the Electronic Nose data is one of the most important parts of the data analysis cycle. This aspect of data investigation should ideally be performed iteratively together with data collection in order to optimize experimental protocols and final results. Once exploration has been completed, a complete supervised data analysis on a full dataset can be run, leading to prediction and thereby to e-nose performance evaluation. Exploratory Data Analysis (EDA) comprises three tasks: checking the quality of the data, calculating summary statistics, and producing plots of the data to get a feel of their structure. Graphical visualization of data allows checking for instrumental malfunctioning, discovering human errors, removing outliers, understanding the influence of experimental parameters, verifying the ability of the machine in discriminating the examined samples, and eventually formulating new hypotheses. A number of different techniques have been developed for data visualization, including multivariate statistical analysis, non-linear mapping, and clustering techniques. This chapter will present an overview of methods, tools, and software for EDA of artificial olfaction experiments. These will cover visualization and data mining tools for both raw and preprocessed data, such as: histograms, scatter plots, feature and box plots, Principal Component Analysis (PCA), Cluster Analysis (CA), and Cluster Validity (CV). Some case studies that demonstrate the application of the methods to specific chemical sensing problems will be illustrated.

scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

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.

scholarly journals Study of groundwater contamination and drinking suitability in basaltic terrain of Maharashtra, India through PIG and multivariate statistical techniques

2020 ◽  
Vol 69 (4) ◽  
pp. 398-414 ◽  
Author(s):  
Vasant Wagh ◽  
Shrikant Mukate ◽  
Aniket Muley ◽  
Ajaykumar Kadam ◽  
Dipak Panaskar ◽  
...  
Keyword(s):  
Major Ions ◽  
Principal Component ◽  
World Health ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Post Monsoon ◽  
Groundwater Samples

Abstract The integration of pollution index of groundwater (PIG), multivariate statistical techniques including correlation matrix (CM), principal component analysis (PCA), cluster analysis (CA) and various ionic plots was applied to elucidate the influence of natural and anthropogenic inputs on groundwater chemistry and quality of the Kadava river basin. A total of 80 groundwater samples were collected and analysed for major ions during pre- and post-monsoon seasons of 2012. Analytical results inferred that Ca, Mg, Cl, SO4 and NO3 surpass the desirable limit (DL) and permissible limit (PL) of Bureau of Indian Standards (BIS) and the World Health Organization (WHO) in both the seasons. The elevated content of total dissolved solids (TDS), Cl, SO4, Mg, Na and NO3 is influenced by precipitation and agricultural dominance. PIG results inferred that 52.5 and 35%, 30 and 37.5%, 12.5 and 20%, 2.5 and 5% groundwater samples fall in insignificant, low, moderate and high pollution category (PC) in pre- and post-monsoon seasons, respectively. PC 1 confirms salinity controlled process due to high inputs of TDS, Ca, Mg, Na, Cl and SO4. Also, PC 2 suggests alkalinity influence by pH, CO3, HCO3 and F content. PIG and statistical techniques help to interpret the water quality data in an easier way.

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

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.

Box Plots: Basic and Advanced

1990 ◽  
Vol 83 (2) ◽  
pp. 108-112
Author(s):  
James L. Mullenex
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Statistical Techniques ◽  
Graphical Representations ◽  
Box Plots ◽  
Exploratory Data

Box plots are used for the purpose of analyzing and displaying important features of sets of data. More specifically, box plots are used as graphical representations of five-number summaries. Box plots and five-number summaries are new statistical techniques that were developed by John W. Tukey of Bell Telephone Laboratories. They are parts of a larger set of modern statistical techniques known collectively as exploratory data analysis, or EDA.

scholarly journals An efficient eigenspace updating scheme for high-dimensional systems

2014 ◽  
Vol 24 (1) ◽  
pp. 123-131
Author(s):  
Simon Gangl ◽  
Domen Mongus ◽  
Borut Žalik
Keyword(s):  
Exploratory Data Analysis ◽  
Principal Component ◽  
Current Data ◽  
High Dimensional ◽  
Computationally Efficient ◽  
Basic Scheme ◽  
The Past ◽  
Prior Selection ◽  
Exploratory Data ◽  
Selection Of

Abstract Systems based on principal component analysis have developed from exploratory data analysis in the past to current data processing applications which encode and decode vectors of data using a changing projection space (eigenspace). Linear systems, which need to be solved to obtain a constantly updated eigenspace, have increased significantly in their dimensions during this evolution. The basic scheme used for updating the eigenspace, however, has remained basically the same: (re)computing the eigenspace whenever the error exceeds a predefined threshold. In this paper we propose a computationally efficient eigenspace updating scheme, which specifically supports high-dimensional systems from any domain. The key principle is a prior selection of the vectors used to update the eigenspace in combination with an optimized eigenspace computation. The presented theoretical analysis proves the superior reconstruction capability of the introduced scheme, and further provides an estimate of the achievable compression ratios.

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