scholarly journals Reassessment of elemental concentration data of sediments from the western delta of the Nile River

2013 ◽  
Vol 1 (1) ◽  
pp. 2 ◽  
Author(s):  
Kostalena Michelaki ◽  
Ronald G.V. Hancock
Keyword(s):  
Elemental Concentration ◽  
Nile Delta ◽  
Principal Component ◽  
Data Interpretation ◽  
Geochemical Data ◽  
Concentration Data ◽  
Data Format ◽  
Specific Chemical ◽  
Bivariate Data ◽  
Data Formats

The present study re-examines geochemical data produced by instrumental neutron activa- tion analysis (INAA) of sixty-two fired clay sed- iment samples from the western Nile delta in Egypt. The goal is to assess the strengths and weaknesses of principal component analysis (PCA) and bivariate data splitting (BDS), two widely used data analysis methods, in success- fully sorting differing sediment chemistries. Both PCA and BDS are performed using vari- ous data formats [i.e. original, calcium (Ca)- corrected, scandium (Sc)-normalized, or loga- rithmically (log10) transformed]. Both PCA and BDS are shown to sort differing chemistries well. While PCA has the advantage of speed, BDS has the advantage of providing specific chemical clarity and the opportunity to assess the degree of sand dilution more precisely. In PCA, the data format is semi-immaterial, while in BDS, different formats of the data may hin- der, rather than enhance, data interpretation, depending on the questions being asked.

scholarly journals EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

2021 ◽  
pp. 000370282098784
Author(s):  
James Renwick Beattie ◽  
Francis Esmonde-White
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Original Data ◽  
Specific Chemical ◽  
Successive Refinement ◽  
Minimal Loss ◽  
Components Analysis ◽  
The Mathematical Model ◽  
Application Specific ◽  
Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing ‘concentration’ or ‘weights’. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

Application of the principal component analysis on geochemical data: a case study in the basement complex of Southern Ilesa area, Nigeria

2010 ◽  
Vol 4 (1-2) ◽  
pp. 239-247 ◽  
Author(s):  
Emmanuel A. Ariyibi ◽  
Samuel L. Folami ◽  
Bankole D. Ako ◽  
Taye R. Ajayi ◽  
Adebowale O. Adelusi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Geochemical Data ◽  
Basement Complex

1988 COMPILATION OF ELEMENTAL CONCENTRATION DATA FOR USGS AGV-1, GSP-1 AND G-2

1992 ◽  
Vol 16 (2) ◽  
pp. 111-300 ◽  
Author(s):  
Ernest S. GLADNEY ◽  
Elizabeth A. JONES ◽  
Eric J. NICKELL ◽  
Iwan ROELANDTS
Keyword(s):  
Elemental Concentration ◽  
Concentration Data

scholarly journals Seasonal Variation And Preliminary Risk Assessment Of Trace Element Pollution In Surface Water From Langat River, Malaysia

2019 ◽  
Author(s):  
Lubna Alam ◽  
Md. Mahmudul Alam ◽  
Mazlin Bin Mokhtar ◽  
Azizul Bar ◽  
Nicholas Kathijotes ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Seasonal Variation ◽  
River Water ◽  
Critical Index ◽  
Principal Component ◽  
Dermal Absorption ◽  
Potential Threat ◽  
Concentration Data ◽  
Trace Element Pollution ◽  
Langat River

Heavy metals are widely used in various industries and became a great concern all over the world due to environmental contamination. This study provides an assessment of seasonal variability and risks to human health associated with the exposure to heavy metals concentrated in Langat river water of Malaysia. The Department of Environment (DOE) Malaysia kindly provided the heavy metal concentration data in water for this study. Several multivariate estimation such as an independent t test, box-and-whisker plot and Principal component analysis were carried out to evaluate the seasonal variation of metals concentration in water. The average value of ten analyzed metals was 250.81 µg/l and followed in order of abundance by August > Jun > February > October > April > December > March > May > September > January > July > November. The calculated HPI was 123.42, which is far above the critical index value of 100, indicating pollution with respect to heavy metals. Estimates of health risks associated with river water were summarized according to non-carcinogenic and carcinogenic health effects. No potential threat was detected for noncarcinogenic risk as the HI values calculated were <1. Potential carcinogenic risks associated with the ingestion and dermal absorption of heavy metals in water were evaluated probabilistically by performing 10,000 trails for Monte Carlo simulation where potential carcinogenic risk exists in case of Cd and As.It is necessary to take proper steps to reduce the pollution of heavy metals in Langat River.

scholarly journals Whole Earth Telescope Headquarters Standard Data Formats

2003 ◽  
Vol 12 (2) ◽  
Author(s):  
R. L. Riddle ◽  
S. D. Kawaler
Keyword(s):  
Next Generation ◽  
Ccd Photometry ◽  
Data Format ◽  
Final Version ◽  
Standard Data ◽  
Data Formats ◽  
Whole Earth

AbstractAs the WET moves to CCD systems, we move away from the uniformity of the standard WET photometer into an arena where each system can be radically different. There are many possible CCD photometry systems that can fulfil the requirements of a WET instrument, but each of these will have their own unique native data format. During XCov22, it became readily apparent that the WET requires a defined data format for all CCD data that arrives at HQ. This paper describes the proposed format for the next generation of WET data; the final version will be the default format for XQED, the new photometry package discussed elsewhere in these proceedings.

scholarly journals Amino Acid Profiling Study of Psidium guajava L. Leaves as an Effective Treatment for Type 2 Diabetic Rats

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chang Xu ◽  
Xin Li ◽  
Debin Zeng ◽  
Ying Liu ◽  
Yuhang Gao ◽  
...  
Keyword(s):  
Amino Acid ◽  
Fasting Blood Glucose ◽  
Principal Component ◽  
Psidium Guajava ◽  
Diabetic Rats ◽  
New Drugs ◽  
Concentration Data ◽  
Amino Acid Profiling ◽  
Type 2 Diabetic

Type 2 diabetes mellitus (T2DM) has become a major disease threatening human health worldwide. At present, the treatment of T2DM cannot cure diabetes and is prone to many side effects. Psidium guajava L. leaves have been reported to possess hypoglycemic activity, and they have been widely used in diabetes treatment in the folk. However, the antidiabetic mechanism has not been clearly explained. Also, the change in amino acid profile can reflect a metabolic disorder and provide insights into system-wide changes in response to physiological challenges or disease processes. The study found that P. guajava L. leaves can decrease fasting blood glucose and lipid levels in type 2 diabetic rats induced by streptozotocin. Through the analysis of amino acid profiling following 20 days of gavage administration, the concentration data were modeled by principal component analysis and orthogonal partial least squares discriminant analysis to find the different metabolites and related metabolic pathways (including cysteine and methionine metabolism, valine, leucine, and isoleucine biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis) for the explanation of the hypoglycemic mechanism of P. guajava L., which provides an experimental and theoretical basis for diabetes prediction and for the development of new drugs for the treatment of diabetes.

Development and Validation of Chemometric-Assisted Spectrophotometric Methods for Simultaneous Determination of Phenylephrine Hydrochloride and Ketorolac Tromethamine in Binary Combinations

2016 ◽  
Vol 99 (5) ◽  
pp. 1247-1251 ◽  
Author(s):  
Hamed M Elfatatry ◽  
Mokhtar M Mabrouk ◽  
Sherin F Hammad ◽  
Fotouh R Mansour ◽  
Amira H Kamal ◽  
...  
Keyword(s):  
Least Squares ◽  
Simultaneous Determination ◽  
Principal Component Regression ◽  
Principal Component ◽  
Data Matrix ◽  
Ketorolac Tromethamine ◽  
Concentration Data ◽  
Phenylephrine Hydrochloride ◽  
Spectrophotometric Methods

Abstract The present work describes new spectrophotometric methods for the simultaneous determination of phenylephrine hydrochloride and ketorolac tromethamine in their synthetic mixtures. The applied chemometric techniques are multivariate methods including classical least squares, principal component regression, and partial least squares. In these techniques, the concentration data matrix was prepared by using the synthetic mixtures containing these drugs dissolved in distilled water. The absorbance data matrix corresponding to the concentration data was obtained by measuring the absorbances at 16 wavelengths in the range 244–274 nm at 2 nm intervals in the zero-order spectra. The spectrophotometric procedures do not require any separation steps. The accuracy, precision, and linearity ranges of the methods have been determined, and analyzing synthetic mixtures containing the studied drugs has validated them. The developed methods were successfully applied to the synthetic mixtures and the results were compared to those obtained by a reported HPLC method.

scholarly journals SVD-based principal component analysis of geochemical data

2005 ◽  
Vol 3 (4) ◽  
pp. 731-741 ◽  
Author(s):  
Petr Praus
Keyword(s):  
Principal Component Analysis ◽  
Euclidean Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Geochemical Data ◽  
Average Group ◽  
Testing Data ◽  
Value Decomposition ◽  
Geochemical Variables

AbstractPrincipal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2% of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration.The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.

scholarly journals Liquid Chromatographic and Spectrophotometric Determination of Diflucortolone Valerate and Isoconazole Nitrate in Creams

2011 ◽  
Vol 94 (1) ◽  
pp. 128-135 ◽  
Author(s):  
Elif Karacan ◽  
Mehmet Gokhan Çaġlayan ◽  
İsmail Murat Palabiyik ◽  
Feyyaz Onur
Keyword(s):  
Principal Component Regression ◽  
Principal Component ◽  
Derivative Spectrophotometry ◽  
Data Matrix ◽  
Concentration Data ◽  
Spectrophotometric Methods ◽  
First Derivative ◽  
First Derivative Spectrophotometry ◽  
Liquid Chromatographic

Abstract A new RP-LC method and two new spectrophotometric methods, principal component regression (PCR) and first derivative spectrophotometry, are proposed for simultaneous determination of diflucortolone valerate (DIF) and isoconazole nitrate (ISO) in cream formulations. An isocratic system consisting of an ACE® C18 column and a mobile phase composed of methanol–water (95+5, v/v) was used for the optimal chromatographic separation. In PCR, the concentration data matrix was prepared by using synthetic mixtures containing these drugs in methanol–water (3+1, v/v). The absorbance data matrix corresponding to the concentration data matrix was obtained by measuring the absorbances at 29 wavelengths in the range of 242–298 nm for DIF and ISO in the zero-order spectra of their combinations. In first derivative spectrophotometry, dA/dλ values were measured at 247.8 nm for DIF and at 240.2 nm for ISO in first derivative spectra of the solution of DIF and ISO in methanol–water (3+1, v/v). The linear ranges were 4.00–48.0 μg/mL for DIF and 50.0–400 μg/mL for ISO in the LC method, and 2.40–40.0 μg/mL for DIF and 60.0–260 μg/mL for ISO in the PCR and first derivative spectrophotometric methods. These methods were validated by analyzing synthetic mixtures. These three methods were successfully applied to two pharmaceutical cream preparations.

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