scholarly journals Multivariate statistical methods applied to interpretation of saturated biomarkers (Velebit oil field, se Pannonian basin, Serbia)

2006 ◽  
Vol 71 (7) ◽  
pp. 745-769 ◽  
Author(s):  
Tatjana Solevic ◽  
Ksenija Stojanovic ◽  
Branimir Jovancicevic ◽  
Gorica Mandic ◽  
Jan Schwarzbauer ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Pannonian Basin ◽  
Oil Field ◽  
Gas Chromatography Mass Spectrometry ◽  
Thermal Maturity ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Correlation Studies ◽  
Discriminant Analyses ◽  
Ward Method

Twenty-five crude oils originating from the Velebit oil field (SE Pannonian Basin), the most important oil field in Serbia, were investigated. Saturated biomarkers (n-alkanes, isoprenoids, steranes and triterpanes) were analyzed by gas chromatography-mass spectrometry (GC-MS). Based on the distribution and abundance of these compounds, a large number of source and maturation parameters were calculated, particularly those most often used in correlation studies of oils. The examined samples were classified according to their origin and level of thermal maturity using factor, cluster and discriminant analyses. According to the source and maturation parameters, combined factor and cluster analyses using the Ward method enabled the categorization of the investigated oils into three groups. The cluster Ward analysis was shown to be of greater susceptibility and reliability. However, in addition to the two aforementioned methods, K-Means cluster analysis and discriminant analysis were shown to be necessary for a more precise and detailed categorization in the case of a large number of samples in one group. Consequently, it was concluded that factor and cluster K-Means and Ward analyses can generally be used for the interpretation of saturated biomarkers in correlation studies of oils, but the observed results have to be checked, i.e., confirmed by discriminant analysis.

Application of gas chromatography-mass spectrometry and chemometrics methods for assessing volatile profiles of Pu-erh tea with different processing methods and ageing years

RSC Advances ◽  
2015 ◽  
Vol 5 (107) ◽  
pp. 87806-87817 ◽  
Author(s):  
Shidong Lv ◽  
Yuanshuang Wu ◽  
Jifu Wei ◽  
Ming Lian ◽  
Chen Wang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Solid Phase Microextraction ◽  
Solid Phase ◽  
Gas Chromatography Mass Spectrometry ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Chromatography Mass Spectrometry ◽  
Different Types ◽  
Volatile Profiles

A method was developed based on head-space solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) combined with multivariate statistical methods to assess volatile profiles in different types of Pu-erh teas.

scholarly journals Evaluation of saturated and aromatic hydrocarbons oil-oil maturity correlation parameters (SE Pannonian Basin, Serbia)

2007 ◽  
Vol 72 (12) ◽  
pp. 1237-1254 ◽  
Author(s):  
Ksenija Stojanovic ◽  
Branimir Jovancicevic ◽  
Dragomir Vitorovic ◽  
Yulia Golovko ◽  
Galina Pevneva ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Pannonian Basin ◽  
Oil Field ◽  
Crude Oils ◽  
Methyl Group ◽  
Cluster Analyses ◽  
Saturated And Aromatic Hydrocarbons ◽  
Correlation Parameters ◽  
Ward Method ◽  
Maturity Parameters

Twenty three crude oils from the Serbian part of the Pannonian Basin (14 from the Vojvodina Province and 9 from the Drmno Depression) were investigated, aimed at an evaluation of oil-oil maturity correlation parameters based on the distribution and abundance of saturated biomarkers and alkylarene constituents. Factor and cluster analyses were used for this purpose. Factor analyses using varimax rotation were first run separately, i.e., of maturity parameters based on the abundance of (a) n-alkanes and isoprenoids, (b) steranes and triterpanes, (c) alkylnaphthalenes, and (d) alkylphenanthrenes. These analyses yielded 9 important "maturity factors". Eight of them, showing higher than 30 % of variance, were further involved in another factor analysis, as well as in cluster analysis using the Ward method. In this way, all maturity parameters based on saturated biomarkers and alkylarenes were evaluated and ranged, considering the fact that the observed factors represented their linear combinations. The results showed that in the correlation of crude oils from the Serbian part of the Pannonian Basin, the most important were maturity parameters based on isomerization reactions involving one methyl group in thermodynamically less stable ?-methylnaphthalenes, ethylnaphthalenes, dimethylnaphthalenes and methylphenanthrenes, and their change into more stable isomers with the methyl group in the ?-position in the aromatic ring. Processes constituting high loadings factor 2 and factor 3 parameters were also defined. Hierarchy between the "factors" and parameters were controlled, and approved, by cluster analysis using the Ward method. Finally, the investigated crude oils were correlated by factor and cluster analyses, using all the important "maturity factors". Differences in maturity were observed between the Vojvodina and Drmno Depression crude oils, as well as between oils originating from South Banat, North Banat and the Velebit oil field (Vojvodina locality).

Multivariate Statistical Methods Used in Population Genetics

2020 ◽  
pp. 59-70
Author(s):  
Maman Laouali Adamou Ibrahim ◽  
Oumarou Zango ◽  
Maman Maarouhi Inoussa ◽  
Soulé Moussa ◽  
Yacoubou Bakasso
Keyword(s):  
Population Genetics ◽  
Discriminant Analysis ◽  
Statistical Methods ◽  
Population Variation ◽  
Data Matrix ◽  
Factorial Correspondence Analysis ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Clustering Model ◽  
Principal Coordinates

Several multivariate statistical methods are used in population genetics but there are very few studies that have revealed the strengths and weaknesses of different methods. Thus, this study aims to reveal the strengths and weaknesses of the different multivariate statistical methods used in population genetics through the world. This synthesis is carried out according to the methodology "Preferred Reporting Items for Systematic Reviews and Meta-Analyzes" (PRISMA). This study shown that various statistical methods or combination of multivariate statistical methods are used in population genetics. It emerges that there is no a priori a better method, so it is necessary to determine the method adapted to both the data collected and the research objective. This study identified the most commonly used multivariate statistical methods in genetics such as: Ordination methods (52.50%) are methods that summarize the information contained in the data matrix by minimizing wastage. This are: principal components analysis (by 32.0% of the articles), principal coordinates analysis (by 7.50% of the articles), discriminant analysis of principal component, factorial correspondence analysis, factorial discriminant analysis, factorial analysis on distance table. Clustering methods (35%) that aim to form groups of individuals that are as similar as possible, including the hierarchical ascending clustering (17.50% of articles), neighbor-joining, and Bayesian clustering model (by 15% of the articles). The analysis of the molecular variance (7.50%) which consists of studying the intra and inter-population variation and the Mantel test (5%) which aims to test the correlation between the matrix of genetic distances and other distance matrices (environmental causes of genetic variability).

scholarly journals Application of Interactive Regularized Discriminant Analysis to Wine Data

2016 ◽  
Vol 35 (1) ◽  
Author(s):  
Ute Römisch ◽  
Dimitar Vandev ◽  
Katrin Zur
Keyword(s):  
Discriminant Analysis ◽  
Model Building ◽  
Geographical Origin ◽  
Data Bank ◽  
Optimal Choice ◽  
Error Rates ◽  
Nonlinearity Parameter ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Discriminant Model

Testing the possibility of determining the geographical origin (country) of wines on the base of chemico-analytical parameters was the aim of the European project ”Establishing of a wine data bank for analytical parameters for wines from Third countries (G6RD-CT-2001-00646-WINE DB)” supported by the European Commission. Therefore a data base containing 400 samples of commercial and authentic wines from Hungary, Czech Republic, Romania and South Africa was created. For each of those samples around 100 analytical parameters, among them rare earth elements and isotopic ratios were measured.Besides other multivariate statistical methods of discrimination and classification the method of regularized discriminant analysis (RDA) was used to distinguish the wines of the different countries on the base of a minimal number of the most important parameters. A MATLAB-program, developed by Vandev (2004) which allows an interactive stepwise discriminant model building on the base of an optimal choice of the “nonlinearity” parameter alpha was used. This program will be described shortly and models for commercial wines with corresponding classification and prediction error rates will be given.As a result of using RDA it was possible to reduce the number of analytical parameters to the eight to infer the geographical origin of these commercial wines.

Bayesian discriminant analysis of lithofacies integrate the Fisher transformation and the kernel function estimation

2017 ◽  
Vol 5 (2) ◽  
pp. SE1-SE10 ◽  
Author(s):  
Xingye Liu ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Lin Zhou ◽  
Kangkang Guo
Keyword(s):  
Discriminant Analysis ◽  
Statistical Methods ◽  
Kernel Function ◽  
Statistical Characteristics ◽  
Conditional Probability Density ◽  
Posteriori Probability ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Accurate Identification ◽  
Advantages And Disadvantages

The accurate identification of lithofacies is indispensable for reservoir parameter prediction. In recent years, the application of multivariate statistical methods has gained more and more attention in petroleum geology. In terms of the identification for lithofacies, the commonly used multivariate statistical methods include discriminant analysis and cluster analysis. Fisher and Bayesian discriminant analyses are two different discriminant analysis methods, which include intrinsic advantages and disadvantages. Given the discriminant efficiency of different methods, calculation cost, difficulty in the degree of determining the parameters, and the ability to analyze statistical characteristics of data, we put forward a new method combined with seismic information to classify reservoir lithologies and pore fluids. This method integrates the advantages of Fisher discrimination, the kernel function, and Bayesian discrimination. First, we analyze training data and search a projection direction. Then, data are transformed through Fisher transformation according to this direction and different kinds of facies can be distinguished more efficiently by exploiting transformed data than by using primitive data. Subsequently, using the kernel function estimates the conditional probability density function of the transformed variable. A classifier is constructed based on Bayesian theory. Then, the pending data are input to the classifier and the solution whose posteriori probability reaches the maximum is extracted as the predicted result at each grid node. An a posteriori probability distribution of predicted lithofacies can be acquired as well, from which interpreters can evaluate the uncertainty of the results. The ultimate goal of this study is to provide a novel and efficient lithofacies discriminant method. Tests on model and field data indicate that our method can obtain more accurate identification results with less uncertainty compared with conventional Fisher approaches and Bayesian methods.

Improved Automated Mapping of Sinkholes Using High-Resolution DEMs

2021 ◽  
Author(s):  
Yonathan Admassu ◽  
Celestine Woodruff
Keyword(s):  
Discriminant Analysis ◽  
High Resolution ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Simple Task ◽  
Automated Mapping ◽  
Linear Discriminant ◽  
Bedrock Geology ◽  
Digital Elevation ◽  
Automated Methods

ABSTRACT Sinkholes are common surface manifestations of the presence of networks of subsurface caverns in areas where the bedrock geology is dominated by soluble rocks such as limestones. Accurate mapping of sinkholes is crucial as they are hazardous to transportation infrastructure and may serve as conduits of contaminants to the groundwater. The use of high-resolution digital elevation models extracted from LiDAR and tools in ArcGIS have made it a simple task to automate the process of identification of closed depressions. However, these automated methods do not differentiate between sinkholes and other man-made depressions. Multivariate statistical methods such as linear discriminant analysis, quadratic discriminant analysis, and logistic regression were used to produce predictive models based on selected shape factor values such as circularity, sphericity, and curvature. Curvature values, especially when combined with circularity, were found to be the most powerful variables in separating closed depressions into sinkholes and other artificial depressions.

Long-term variations in individual particle types in the aerosol of Phoenix, Arizona, as determined using automated electron microprobe analysis and multivariate statistical techniques

1993 ◽  
Vol 51 ◽  
pp. 742-743
Author(s):  
Karen A. Katrinak ◽  
James R. Anderson ◽  
Peter R. Buseck
Keyword(s):  
Electron Microprobe ◽  
Fine Fraction ◽  
Coarse Fraction ◽  
Motor Vehicles ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
X Ray ◽  
Mineral Particles ◽  
Anthropogenic Air Pollutants

Aerosol samples were collected in Phoenix, Arizona on eleven dates between July 1989 and April 1990. Elemental compositions were determined for approximately 1000 particles per sample using an electron microprobe with an energy-dispersive x-ray spectrometer. Fine-fraction samples (particle cut size of 1 to 2 μm) were analyzed for each date; coarse-fraction samples were also analyzed for four of the dates.The data were reduced using multivariate statistical methods. Cluster analysis was first used to define 35 particle types. 81% of all fine-fraction particles and 84% of the coarse-fraction particles were assigned to these types, which include mineral, metal-rich, sulfur-rich, and salt categories. "Zero-count" particles, consisting entirely of elements lighter than Na, constitute an additional category and dominate the fine fraction, reflecting the importance of anthropogenic air pollutants such as those emitted by motor vehicles. Si- and Ca-rich mineral particles dominate the coarse fraction and are also numerous in the fine fraction.

Pollen morphology of some Tamarix (Tamaricaceae) species of Iran

2020 ◽  
Vol 62 (1-2) ◽  
pp. 151-161
Author(s):  
T. Shagholi ◽  
M. Keshavarzi ◽  
M. Sheidai
Keyword(s):  
North Africa ◽  
Pollen Grains ◽  
Ecological Niches ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Qualitative And Quantitative ◽  
Petal Length ◽  
Electron Microscopes ◽  
High Level ◽  
Scanning Electron Microscopes

Tamarix L. (Tamaricaceae) is a halophytic shrub in different parts of Asia and North Africa. Taxonomy and species limitation of Tamarix is very complex. This genus has three sections as Tamarix, Oligadenia, and Polyadenia, which are mainly separated by petal length, the number of stamens, the shape of androecial disk and attachment of filament on the androecial disk. As there was no palynological data on pollen features of Tamarix species of Iran, in the present study 12 qualitative and quantitative pollen features were evaluated to find diagnostic ones. Pollen grains of 8 Tamarix species were collected from nature. Pollen grains were studied without any treatment. Measurements were based on at least 50 pollen grains per specimen. Light and scanning electron microscopes were used. Multivariate statistical methods were applied to clarify the species relationships based on pollen data. All species studied showed monad and tricolpate (except some individuals of T. androssowii). Some Tamarix species show a high level of variability, in response to ecological niches and phenotypic plasticity, which make Tamarix species separation much more difficult. Based on the results of the present study, pollen grains features are not in agreement with previous morphological and molecular genetics about the sectional distinction.

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