scholarly journals APPLICATION OF MULTIVARIATE STATISTICAL ANALYSIS TO BIOMARKERS IN SE-TURKEY CRUDE OILS

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 63-65
Author(s):  
K. Gürgey ◽  
S. Canbolat
Keyword(s):  
Gas Chromatography ◽  
Statistical Analysis ◽  
Crude Oil ◽  
Multivariate Statistical Analysis ◽  
Stable Carbon Isotope ◽  
Source Rocks ◽  
Data Matrix ◽  
Geochemical Data ◽  
Multivariate Statistical ◽  
Se Turkey

Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%), Stable Carbon Isotope, Gas Chromatography (GC), and Gas Chromatography-Mass Spectrometry (GC-MS) data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE) Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

scholarly journals Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4146
Author(s):  
José Enrique Herbert-Pucheta ◽  
José Daniel Lozada-Ramírez ◽  
Ana E. Ortega-Regules ◽  
Luis Ricardo Hernández ◽  
Cecilia Anaya de Parrodi
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Field Gradient ◽  
Multivariate Statistical Analysis ◽  
Data Matrix ◽  
Cabernet Sauvignon ◽  
Gradient Echo ◽  
Multivariate Statistical ◽  
Orthogonal Projections ◽  
Pulsed Field

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad 1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.

scholarly journals The distribution of the trace element contents in lignite and ash from Drama lignite deposit, using multivariate statistical analysis

2001 ◽  
Vol 34 (3) ◽  
pp. 1255
Author(s):  
S. PANILAS ◽  
G. HATZIYANNIS
Keyword(s):  
Trace Elements ◽  
Rare Earth ◽  
Statistical Analysis ◽  
Rare Earth Elements ◽  
Multivariate Statistical Analysis ◽  
Major And Trace Elements ◽  
Major Elements ◽  
Geochemical Data ◽  
Multivariate Statistical ◽  
Inorganic Matter

Multivariate statistical analysis was used on existing geochemical data of the Drama lignite deposit, eastern Macedonia, Greece. Factor analysis with varimax rotation technique was applied to study the distribution of major, trace and rare earth elements in the lignite and 850°C lignitic ash, to find a small set of factors that could explain most of the geochemical variability. The study showed that major elements AI, Na, Κ, contained in the lignite samples, presented high correlation with most of the trace and rare earth elements. In 850°C lignitic ashes major and trace elements present different redistribution. Only Al remained correlated with the trace elements Co, Cr, Rb, Ta, Th, Ti, Sc and rare earths related with inorganic matter in the lignite beds. Trace elements Fe, Mo, U, V, W, and Lu were associated with organic matter of lignite and had also been affected by the depositional environment.

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