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

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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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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Principal component analysis and cluster analysis for the characterization of dental composites

The Analyst ◽

10.1039/b004662k ◽

2000 ◽

Vol 125 (11) ◽

pp. 2044-2048 ◽

Cited By ~ 14

Author(s):

Concepción Domingo ◽

Raul W. Arcis ◽

Estrella Osorio ◽

Manuel Toledano ◽

Javier Saurina

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Principal Component ◽

Component Analysis ◽

Dental Composites ◽

And Cluster Analysis

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Water Quality Assessment of Porsuk River, Turkey

E-Journal of Chemistry ◽

10.1155/2010/438180 ◽

2010 ◽

Vol 7 (2) ◽

pp. 593-599 ◽

Cited By ~ 8

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.

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

Journal of Water and Land Development ◽

10.2478/jwld-2018-0026 ◽

2018 ◽

Vol 37 (1) ◽

pp. 65-74 ◽

Cited By ~ 4

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.

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THE CONVERGENCE OF HABITAT STRUCTURE IN TUNDRA COLLEMBOLAN COMMUNITIES (INSECTA: ORDER COLLEMBOLA)

The Canadian Entomologist ◽

10.4039/ent1151129-9 ◽

1983 ◽

Vol 115 (9) ◽

pp. 1129-1145 ◽

Cited By ~ 3

Author(s):

Masanori J. Toda ◽

Kouzou Tanno

Keyword(s):

Habitat Structure ◽

Principal Component ◽

The Other ◽

Statistical Techniques ◽

Mackenzie Delta ◽

Multivariate Statistical Techniques ◽

Multivariate Statistical ◽

Cluster Analyses ◽

Ecological Changes ◽

Principal Component Analyses

AbstractHabitat structure of two collembolan communities, one at Barrow, Alaska, U.S.A., the other at Tuktoyaktuk in the Mackenzie Delta, Canada, has been analyzed in relation to microtopographies characteristic of tundra regions. Multivariate statistical techniques, cluster analyses (UPGMA), and principal component analyses (PCA) reveal various ecological changes in component species. In spite of such local variations in component species, the two communities show similar patterns of habitat structure that are organized principally along a gradient of environmental moisture.

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Chemical and Microbiological Characterization for PDO Labelling of Typical East Piedmont (Italy) Salami

Journal of Chemistry ◽

10.1155/2015/597471 ◽

2015 ◽

Vol 2015 ◽

pp. 1-22 ◽

Cited By ~ 1

Author(s):

V. Gianotti ◽

S. Panseri ◽

E. Robotti ◽

M. Benzi ◽

E. Mazzucco ◽

...

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Biogenic Amines ◽

Principal Component ◽

Component Analysis ◽

Statistical Tools ◽

Multivariate Statistical ◽

North West ◽

And Cluster Analysis ◽

Aroma Components

This study is focused on the characterisation of typical salami produced in Alessandria province (North West of Italy). Seventeen small or medium salami producers from this area were involved in the study and provided the samples investigated. The aim is double and consists in obtaining a screening of the characteristics of different products and following their evolution along ripening. The study involved five types of typical salami that were characterised for aroma components and nutritional features. This approach could provide a basis for a possible PDO or PGI label request. Principal Component Analysis and cluster analysis were used as multivariate statistical tools for data treatment. The overall results obtained point out that the products investigated do not deviate from analogous European products and show the possibility of characterising by specific parameters three main groups of samples:Salamini di Mandrogne,Muletta, andNobile Giarolo; moreover some considerations can also be drawn with respect to the nutritional characterization considering the biogenic amines profile.

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Comparison of antioxidant capacity assays with chemometric methods

Analytical Methods ◽

10.1039/c5ay00330j ◽

2015 ◽

Vol 7 (10) ◽

pp. 4216-4224 ◽

Cited By ~ 13

Author(s):

Anita Rácz ◽

Nóra Papp ◽

Emőke Balogh ◽

Marietta Fodor ◽

Károly Héberger

Keyword(s):

Principal Component Analysis ◽

Antioxidant Capacity ◽

Pair Correlation ◽

Correlation Method ◽

Principal Component ◽

The Other ◽

Chemometric Methods ◽

Sum Of Ranking Differences ◽

And Cluster Analysis ◽

Antioxidant Capacity Assays

The antioxidant capacity assays are compared with principal component analysis and cluster analysis. The best candidate to replace all of the other methods is selected using sum of ranking differences and the pair correlation method.

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FTIR spectroscopy in combination with principal component analysis or cluster analysis as a tool to distinguish beech (Fagus sylvatica L.) trees grown at different sites

Holzforschung ◽

10.1515/hf.2008.104 ◽

2008 ◽

Vol 62 (5) ◽

Cited By ~ 32

Author(s):

Rumana Rana ◽

Günter Müller ◽

Annette Naumann ◽

Andrea Polle

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Ftir Spectroscopy ◽

Fagus Sylvatica ◽

Principal Component ◽

Component Analysis ◽

Climatic Conditions ◽

Multivariate Statistical ◽

Ftir Spectral Analysis ◽

And Cluster Analysis

Abstract FTIR spectroscopy was used to distinguish between beech (Fagus sylvatica L.) trees grown at five different sites; one in middle Germany close to Göttingen (forest district Reinhausen), three located in the southwest (two in Rhineland-Palatinate: forest districts Saarburg and Hochwald, and one in Luxembourg), and one in North-Rhine Westfalia. Detailed investigation of the spectra in the fingerprint region (1800–600 cm-1) revealed 16 distinct peaks and shoulders, most of which were assignable to wavenumbers previously shown to represent wood compounds. Differences in peak heights and peak ratios indicated differences in wood composition of beech trees from different sites. To determine if the wood of individual trees could be distinguished, principal component analysis (PCA) and cluster analysis were performed using FTIR spectra as input data. With both PCA and cluster analysis, trees from four of the five different sites were separated. It was not possible to distinguish between trees from Saarburg and Hochwald, where similar edaphic and climatic conditions exist, while wood spectra from trees from all other areas clearly segregated. Wood collected at different positions in the stem (bottom, crown, center and outer year rings) of trees grown at the same site was not distinguishable. Therefore, FTIR spectral analysis in combination with multivariate statistical methods can be used to distinguish wood of trees from different growth habitats. Extension of this method to other species may be of great interest for wood certification, as it may be possible to distinguish wood, of a given species, originating from different regions.

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Monitoring and assessment of Deepor Beel water quality using multivariate statistical tools

Water Practice & Technology ◽

10.2166/wpt.2018.098 ◽

2018 ◽

Vol 13 (4) ◽

pp. 893-908

Author(s):

Siddhant Dash ◽

Smitom Swapna Borah ◽

Ajay Kalamdhad

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Principal Component ◽

Hierarchical Cluster ◽

Quality Parameters ◽

Statistical Techniques ◽

Multivariate Statistical Techniques ◽

Statistical Tools ◽

Multivariate Statistical ◽

Sampling Locations

AbstractThe aim of this study was application of multivariate statistical techniques – e.g., hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) – to analyse significant sources affecting water quality in Deepor Beel. Laboratory analyses for 20 water quality parameters were carried out on samples collected from 23 monitoring stations. HCA was used on the raw data, categorising the 23 sampling locations into three clusters, i.e., sites of relatively high (HP), moderate (MP) and low pollution (LP), based on water quality similarities at the sampling locations. The HCA results were then used to carry out PCA, yielding different principal components (PCs) and providing information about the respective sites' pollution factors/sources. The PCA for HP sites resulted in the identification of six PCs accounting for more than 84% of the total cumulative variance. Similarly, the PCA for LP and MP sites resulted in two and five PCs, respectively, each accounting for 100% of total cumulative variance. Finally, the raw dataset was subjected to DA. Four parameters, i.e., BOD5, COD, TSS and SO42− were shown to account for large spatial variations in the wetland's water quality and exert the most influence.

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