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Chemometric characterisation of the quality of ground waters from different wells in Slovenia
Ernest Vončina, Darinka Brodnjak-Vončina, Nataša Sovič, Marjana Novič, 2007, original scientific article

Abstract: The quality of ground water as a source of drinking water in Slovenia is regularly monitored. One of the monitoring programmes is performed on 5 wells for drinking water supply, 3 industrial wells and 2 ground water monitoring wells. Two hundred and fourteen samples of ground waters were analysed in the time 2003-2004. Samples were gathered from ten different sampling sites and physical chemical measurements were performed. The following 13 physical chemical parameters were regularly controlled: temperature, pH, conductivity, nitrate, AOX (adsorbable organic halogens), metals such as chromium, pesticides (desethyl atrazine, atrazine and 2,6-dichlorobenzamide), highly-volatile halogenated hydrocarbons (trichlorometane, 1,1,2,2-tetrachloroethene and 1,1,2-trichloroethene). For handling the results different chemometrics methods were employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, cluster analysis (CA), the principal component analysis (PCA), the clustering method based on Kohonen neural network, and linear discriminant analysis (LDA). The study gives the opportunity to follow the quality of ground waters at different sampling sites within the defined time period. Monitoring of general pollution of ground waters and following measuring can be used to search the pollution source, to plan prevention measures and to protect from pollution, as well.
Keywords: ground waters, water quality, principal component analysis, classification, Kohonen neural networks
Published in DKUM: 21.12.2015; Views: 2047; Downloads: 139
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Multivariate data analysis of natural mineral waters
Katja Šnuderl, Marjana Simonič, Jan Mocak, Darinka Brodnjak-Vončina, 2007, original scientific article

Abstract: Fifty samples of natural mineral waters from springs in Slovenia, Hungary, Germany, Czech Republic and further countries of former Yugoslavia have been analysed. The mass concentration of cations ($Na^+$, $K^+$, $Ca^{2+}$, $Mg^{2+}$, $Fe^{2+}$, $Mn^{2+}$, $NH^+_4$) and anions ($F^-$, $Cl^-$, $I^-$, $NO^-_3$, $SO_4^{2-}$, $HCO_3^-$), the spring temperature, pH, conductivity and carbon dioxide mass concentration have been measured using standard analytical methods. Appropriate statistical methods and different chemometric tools were used to evaluate the obtained data, namely, (i) descriptive statistics, (ii) principal component analysis (PCA), (iii) cluster analysis, and (iv) linear discriminant analysis (LDA). It was confirmed that Slovenian natural mineral water samples differ most from the German ones but are relatively similar to the Czech and Hungarian ones. Water samples from Hungary are similar to waters from the eastern part of Slovenia.
Keywords: natural mineral water, ion determination, principal component analysis, cluster analysis, linear discriminant analysis
Published in DKUM: 21.12.2015; Views: 1681; Downloads: 91
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An efficient eigenspace updating scheme for high-dimensional systems
Simon Gangl, Domen Mongus, Borut Žalik, 2014, original scientific article

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.
Keywords: eigenspace, projection space, data compression, principal component analysis
Published in DKUM: 10.07.2015; Views: 1331; Downloads: 90
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PCA - a powerful method for analyze ecological niches
Franc Janžekovič, Tone Novak, 2012, independent scientific component part or a chapter in a monograph

Keywords: principal component analysis, ecology, environment, biology
Published in DKUM: 07.06.2012; Views: 2178; Downloads: 156
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Multivariate data analysis of erythrocyte membrane phospholipid fatty acid profiles in the discrimination between normal blood tissue and various disease states
Zdenka Cencič-Kodba, Darinka Brodnjak-Vončina, Marjana Novič, Uroš Potočnik, 2010, original scientific article

Abstract: The investigation presented here aims to compare the fatty acid composition of red blood cells of patients with different disease states and to test the hypothesis that the changes in fatty acid profiles derived from erythrocyte phospholipids might be relevant to various diseases. The study sample consisted of 342 blood donors, among them 135 with inflammatory bowel disease, 53 with uterine leiomyoma, 14 with verified absence of uterine leiomyoma, 52 with asthma, 18 with colon adenomas, and 70 blood samples without any of mentioned diseases that was used as a control group. After the isolation of erythrocytes from blood samples, total extracted lipids were separated by solid-phase extraction (SPE) into non polar lipids and polar phospholipids. After the saponification of phospholipid fraction, the esterification process followed with boron trifluoride-methanol reagent. The fatty acid methyl ester (FAME) composition of the total red blood cell phospholipid fraction was analyzed by gas chromatography (GC) with flame ionization detector (FID). Additionally two fatty aldehyde dimethyl acetals (hexadecanal and octadecanal dimethyl acetals; 16:0 DMA and 18:0 DMA) derived from erythrocyte membrane plasmalogen phospholipids were also determined. The resulting fatty acid and plasmalogen linked fatty acid composition was evaluated by the principal component analysis (PCA). We demonstrated decreased levels of omega-3 polyunsaturated fatty acids (n-3 PUFAs) in red blood cell membrane of patients with colon adenomas. Also, a large negative correlation was observed among all samples between the quantity of saturated acids and arachidonic (20:4n6) acid as well as saturated acids and adrenic (22:4n6) acid. In PCA score plot a group of female donors is distinguished mainly by the content of linoleic (18:2n6) acid; a small subgroup shows its concentration highly above the average value. At the same time, the same subgroup has both dimethyl acetals below the average concentrations. The study demonstrates feasibility of multivariate data analysis in discrimination of patients with different diseases according to fatty acid profile and suggests considerable differences in membrane fatty acid profiles in patients with various disease states.
Keywords: erythrocyte phospholipids, cell membrane, fatty acid profiles, differentiation, disease state, gas chromatography, principal component analysis
Published in DKUM: 31.05.2012; Views: 2969; Downloads: 88
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Classification of white varietal wines using chemical analysis and sensorial evaluations
Katja Šnuderl, Jan Mocak, Darinka Brodnjak-Vončina, Bibiana Sedláčkova, 2009, original scientific article

Abstract: The ways of application of multivariate data analysis and ANOVA to classification of white varietal wines are here demonstrated. Wine classification was performed using the following classification criteria: winevariety, year of production, wine producer, and wine quality, as found by sensorial testing (bouquet, colour, and taste). Subjective wine evaluation, made by wine experts, is combined with commonly used chemical and physico-chemical properties, measured in analytical laboratory. Importance of the measured variables was determined by principal component analysis and confirmed by analysis of variance. Linear discriminant analysis enabled not only a very successful wine classification but also prediction of the wine category for unknown samples. The wine categories were set up either by three wine varieties, or two vintages, wine producers; two or three wine categories established by wine quality reflected either total points obtained in sensorial evaluation or the points obtained for a particular quality descriptor like colour, taste and bouquet.
Keywords: multivariate data analysis, principal component analysis, discriminant analysis, feature selection, ANOVA, sensory analysis
Published in DKUM: 31.05.2012; Views: 2145; Downloads: 100
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