ORCID Profile
0000-0002-1779-6231
Current Organisation
Central Queensland University
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Publisher: Elsevier BV
Date: 12-2017
Publisher: Wiley
Date: 04-03-2022
DOI: 10.1002/RRA.3959
Abstract: Exploratory analysis of existing multivariate river water datasets can provide useful insights during river basin research and can be used to identify important environmental variables and data suitable for concentration prediction models. In this work, a large dataset pertaining to coal mining areas of the Fitzroy River Basin, Australia, was used to demonstrate principal component analysis and partial least squares regression (PLS) modelling. In this ex le, a strong association between variables confirmed that that sodium was a major ion responsible for electrical conductivity across this vast river basin (PC‐1). Suspected effects of dilution, evapoconcentration, and the influence of anthropogenic inputs on concentrations of nitrogen, sulfate and dissolved metals were also elucidated (PC‐2 and PC‐3). PLS models of a Comet, Nogoa, Mackenzie Rivers‐subset indicated turbidity, dissolved Fe, total Ni, Co and Mn concentrations were not as variable during high flow as during low flow. Conductivity, sulfate and sodium concentrations were negatively correlated ( |0.7|) with total suspended solids (TSS) and several total and dissolved metals during both river conditions. Dissolved Al and Fe had a strong inverse relationship with total Fe and total Co concentrations during high flow. These relationships can be investigated further during future targeted monitoring and analysis. This work provided detailed methodology for development of concentration predictions models for parameters of environmental interest. Specifically, TestSet validated PLS models for dissolved Al (± 5.6 μg/L) and TSS (± 4.6 mg/L), and random Cross Validation models for TSS concentration (± 4.0 mg/L) during low flow and (± 3.5 mg/L) during high flow were produced.
Publisher: Elsevier BV
Date: 2009
DOI: 10.1016/J.JCHROMB.2009.10.035
Abstract: There is increasing recognition of the clinical importance of endogenous nitric oxide synthase inhibitors in critical illness. This has highlighted the need for an accurate high performance liquid chromatography (HPLC) method for detection of asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) in small volumes of blood. Here, the validation of an accurate, precise HPLC method for the determination of ADMA, SDMA, homoarginine and arginine concentrations in plasma is described. Solid phase extraction is followed by derivatisation with AccQ-Fluor and reversed phase separation on a Gemini-NX column at pH 9. Simultaneous detection by both UV-vis and fluorescence detectors affords extra validation. This solid phase extraction method gives absolute recoveries of more than 85% for ADMA and SDMA and relative recoveries of 102% for ADMA and 101% for SDMA. The intra-assay relative standard deviations are 2.1% and 2.3% for ADMA and SDMA, respectively, with inter-assay relative standard deviations of 2.7% and 3.1%, respectively. Advantages of this method include improved recovery of all analytes using isopropanol in the solid phase extraction sharp, well-resolved chromatographic peaks using a high pH mobile phase a non-endogenous internal standard, n-propyl L-arginine and accurate and precise determination of methylated arginine concentrations from only 100microL of plasma.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 02-2017
Publisher: Public Library of Science (PLoS)
Date: 18-02-2011
Publisher: Springer Science and Business Media LLC
Date: 12-2019
No related grants have been discovered for Catherine Jones.