scholarly journals Factors influencing fluffy layer suspended matter (FLSM) properties in the Odra River - Pomeranian Bay - Arkona Deep System (Baltic Sea) as derived by principal components analysis (PCA), and cluster analysis (CA)

2005 ◽  
Vol 9 (1/2) ◽  
pp. 67-80 ◽  
Author(s):  
J. Pempkowiak ◽  
J. Beldowski ◽  
K. Pazdro ◽  
A. Staniszewski ◽  
A. Zaborska ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Organic Matter ◽  
Baltic Sea ◽  
Suspended Matter ◽  
River Mouth ◽  
Principal Component ◽  
Data Set ◽  
Sediment Fraction ◽  
Odra River ◽  
And Cluster Analysis

Abstract. Factors conditioning formation and properties of suspended matter resting on the sea floor (Fluffy Layer Suspended Matter - FLSM) in the Odra river mouth - Arkona Deep system (southern Baltic Sea) were investigated. Thirty FLSM samples were collected from four sampling stations, during nine cruises, in the period 1996-1998. Twenty six chemical properties of the fluffy material were measured (organic matter-total, humic substances, a variety of fatty acids fractions, P, N, δ13C, δ15N; Li; heavy metals- Co, Cd, Pb, Ni, Zn, Fe, Al, Mn, Cu, Cr). The so obtained data set was subjected to statistical evaluation. Comparison of mean values of the measured properties led to conclusion that both seasonal and spatial differences of the fluffy material collected at the stations occured. Application of Principal Component Analysis, and Cluster Analysis, to the data set amended with environmental characteristics (depth, salinity, chlorophyll a, distance from the river mouth), led to quantification of factors conditioning the FLSM formation. The five most important factors were: contribution of the lithogenic component (responsible for 25% of the data set variability), time dependent factors (including primary productivity, mass exchange with fine sediment fraction, atmospheric deposition, contribution of material originating from abrasion-altogether 21%), contribution of fresh autochtonous organic matter (9%), influence of microbial activity (8%), seasonality (8%).

scholarly journals Chemometrics in the assessment of the sustainable development rule implementation

2006 ◽  
Vol 4 (3) ◽  
pp. 543-564 ◽  
Author(s):  
Aleksander Astel ◽  
Grażyna Głosińska ◽  
Tadeusz Sobczyński ◽  
Leonard Boszke ◽  
Vasil Simeonov ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Sustainable Development ◽  
Principal Component ◽  
Anthropogenic Pollution ◽  
Anthropogenic Sources ◽  
Anthropogenic Source ◽  
Data Set ◽  
The Sustainable Development ◽  
Odra River ◽  
Rule Implementation

AbstractThe sustainable development rule implementation is tested by the application of chemometrics in the field of environmental pollution. A data set consisting of Cd, Pb, Cr, Zn, Cu, Mn, Ni, and Fe content in bottom sediment samples collected in the Odra River (Germany/Poland) is treated using cluster analysis (CA), principal component analysis (PCA), and source apportionment techniques. Cluster analysis clearly shows that pollution on the German bank is higher than on the Polish bank. Two latent factors extracted by PCA explain over 88 % of the total variance of the system, allowing identification of the dominant “semi-natural” and “anthropogenic” pollution sources in the river ecosystem. The complexity of the system is proved by MLR analysis of the absolute principal component scores (APCS). The apportioning clearly shows that Cd, Pb, Cr, Zn and Cu participate in an “anthropogenic” source profile, whereas Fe and Mn are “semi-natural”. Multiple regression analysis indicates that for particular elements not described by the model, the amounts vary from 4.2 % (Mn) to 13.1 % (Cr). The element Ni participates to some extent to each source and, in this way, is neither pure “semi-natural” nor pure “anthropogenic”. Apportioning indicates that the whole heavy metal pollution in the investigated river reach is 12510.45 mg·kg−1. The contribution of pollutants originating from “anthropogenic sources” is 9.04 % and from “semi-natural” sources is 86.53 %.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

scholarly journals Use of Highly Discriminating Morphological Characters and Isozymes in the Study of Spanish Chestnut Cultivars

2006 ◽  
Vol 131 (6) ◽  
pp. 770-779 ◽  
Author(s):  
Santiago Pereira-Lorenzo ◽  
María Belén Díaz-Hernández ◽  
Ana María Ramos-Cabrer
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Castanea Sativa ◽  
Principal Component ◽  
Market Value ◽  
Morphological Characters ◽  
Minor Importance ◽  
Breeding Programs ◽  
And Cluster Analysis

Morphological characters (six traits) and isozymes (four systems, five loci) were used to discriminate between Spanish chestnut cultivars (Castanea sativa Mill.) from the Iberian Peninsula. A total of 701 accessions (representing 168 local cultivars) were analyzed from collections made between 1989 and 2003 in the main chestnut growing areas: 31 were from Andalucía (12 cultivars), 293 from Asturias (65 cultivars), 25 from Castilla-León (nine cultivars), four from Extremadura (two cultivars) and 348 from Galicia (80 cultivars). Data were synthesized using multivariate analysis, principal component analysis, and cluster analysis. A total of 152 Spanish cultivars were verified: 58 cultivars of major importance and 94 of minor importance, of which 18 had high intracultivar variation. Thirty-seven cultivars were clustered into 14 synonymous groups. Six of these were from Galicia, one from Castilla-León (El Bierzo), four from Asturias, one from Asturias and Castilla-León (El Bierzo), and two from Asturias, Castilla-León (El Bierzo), and Galicia. The chestnut cultivars from Galicia and Asturias were undifferentiated in genetic terms, indicating that they are not genetically isolated. Overall, chestnut cultivars from southern Spain showed the least variation. Many (58%) of Spanish cultivars produced more than 100 nuts/kg; removing this low market-value character will be a high priority. The data obtained will be of use in chestnut breeding programs in Spain and elsewhere.

scholarly journals Assessment of Physicochemical Properties of Tequila Brands: Authentication and Quality

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alejandra Carreon-Alvarez ◽  
Amaury Suárez-Gómez ◽  
Florentina Zurita ◽  
Sergio Gómez-Salazar ◽  
J. Felix Armando Soltero ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Refractive Index ◽  
Physicochemical Properties ◽  
Analysis Of Variance ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variability ◽  
And Cluster Analysis ◽  
Physicochemical Data

Several physicochemical properties were measured in commercial tequila brands: conductivity, density, pH, sound velocity, viscosity, and refractive index. Physicochemical data were analyzed by Principal Component Analysis (PCA), cluster analysis, and the one-way analysis of variance to identify the quality and authenticity of tequila brands. According to the Principal Component Analysis, the existence of 3 main components was identified, explaining the 87.76% of the total variability of physicochemical measurements. In general, all tequila brands appeared together in the plane of the first two principal components. In the cluster analysis, four groups showing similar characteristics were identified. In particular, one of the clusters contains some tequila brands that are not identified by the Regulatory Council of Tequila and do not meet the quality requirements established in the Mexican Official Standard 006. These tequila brands are characterized by having higher conductivity and density and lower viscosity and refractive index, determined by one-way analysis of variance. Therefore, these economical measurements, PCA, and cluster analysis can be used to determinate the authenticity of a tequila brand.

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