The Experts below are selected from a list of 3159 Experts worldwide ranked by ideXlab platform

Leon Barron - One of the best experts on this subject based on the ideXlab platform.

  • artificial neural network modelling of Pharmaceutical Residue retention times in wastewater extracts using gradient liquid chromatography high resolution mass spectrometry data
    Journal of Chromatography A, 2015
    Co-Authors: Kelly Munro, Thomas H. Miller, Claudia P.b. Martins, Anthony M. Edge, David A. Cowan, Leon Barron
    Abstract:

    The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 Pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1 min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of <1.3 min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 blind test compounds in wastewater matrices lay at or above R2 = 0.92. Finally, the model was evaluated for application to the semi-targeted identification of Pharmaceutical Residues during a weeklong wastewater sampling campaign. The model successfully identified native compounds at a rate of 83 ± 4% and 73 ± 5% in influent and effluent extracts, respectively. The use of an HRMS database and the optimised ANN model was also applied to shortlisting of 37 additional compounds in wastewater. Ultimately, this research will potentially enable faster identification of emerging contaminants in the environment through more efficient post-acquisition data mining.

  • Artificial neural network modelling of Pharmaceutical Residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data.
    Journal of chromatography. A, 2015
    Co-Authors: Kelly Munro, Thomas H. Miller, Claudia P.b. Martins, Anthony M. Edge, David A. Cowan, Leon Barron
    Abstract:

    The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 Pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1 min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of

Kelly Munro - One of the best experts on this subject based on the ideXlab platform.

  • artificial neural network modelling of Pharmaceutical Residue retention times in wastewater extracts using gradient liquid chromatography high resolution mass spectrometry data
    Journal of Chromatography A, 2015
    Co-Authors: Kelly Munro, Thomas H. Miller, Claudia P.b. Martins, Anthony M. Edge, David A. Cowan, Leon Barron
    Abstract:

    The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 Pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1 min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of <1.3 min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 blind test compounds in wastewater matrices lay at or above R2 = 0.92. Finally, the model was evaluated for application to the semi-targeted identification of Pharmaceutical Residues during a weeklong wastewater sampling campaign. The model successfully identified native compounds at a rate of 83 ± 4% and 73 ± 5% in influent and effluent extracts, respectively. The use of an HRMS database and the optimised ANN model was also applied to shortlisting of 37 additional compounds in wastewater. Ultimately, this research will potentially enable faster identification of emerging contaminants in the environment through more efficient post-acquisition data mining.

  • Artificial neural network modelling of Pharmaceutical Residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data.
    Journal of chromatography. A, 2015
    Co-Authors: Kelly Munro, Thomas H. Miller, Claudia P.b. Martins, Anthony M. Edge, David A. Cowan, Leon Barron
    Abstract:

    The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 Pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1 min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of

Ahmad Zaharin Aris - One of the best experts on this subject based on the ideXlab platform.

  • Occurrence, Human Health Risks, and Public Awareness Level of Pharmaceuticals in Tap Water from Putrajaya (Malaysia)
    Exposure and Health, 2020
    Co-Authors: Sarva Mangala Praveena, Maizatul Zahirah Mohd Rashid, Fauzan Adzima Mohd Nasir, Ahmad Zaharin Aris
    Abstract:

    Pharmaceutical Residue pollution remains as an underexplored issue, especially in Asian countries. Along with that line, the purpose of this study was to investigate the occurrence of Pharmaceutical Residues in tap water and its associated potential health risks, involving a total of 80 Putrajaya residents. Besides, this study also aimed to evaluate public awareness (knowledge, attitude, and practice) levels with regards to Pharmaceutical handling. The highest Pharmaceutical Residue occurrence was caffeine (0.38 ng/L) while the lowest was diclofenac (0.14 ng/L). These Pharmaceutical Residue occurrences in tap water were linked with rapid urbanization and industrialization in river water, poor removal efficiencies in wastewater and drinking water treatment plants as well as improper Pharmaceutical waste handling and disposal from the general public. The potential health risks (RQ_T) indicated residents in Putrajaya with ages between 61 and 75 were exposed to the highest health risks caused by the Pharmaceutical Residues in tap water. In general, low public awareness (knowledge, attitude, and practice) levels were identified with only 44.5% of Putrajaya population having good knowledge, 27.5% having good attitude and 1.6% having good practice related to Pharmaceutical handling and its effect to tap water quality. Findings of this study reflected the importance of public awareness program to educate the general public on proper unused/expired handling and disposal to minimize Pharmaceutical pollution.

  • Public awareness level and occurrence of Pharmaceutical Residues in drinking water with potential health risk: A study from Kajang (Malaysia)
    Ecotoxicology and environmental safety, 2019
    Co-Authors: Fauzan Adzima Mohd Nasir, Sarva Mangala Praveena, Ahmad Zaharin Aris
    Abstract:

    Abstract Studies on the occurrence of Pharmaceutical Residues in drinking water were conducted especially in developed countries. However, limited studies reported the occurrence of Pharmaceutical Residues in developing countries. Thus, this study is conducted to fill the knowledge gap of Pharmaceutical Residue occurrences in developing countries, particularly in Malaysia, along with public awareness level and its potential human health risk. This study investigates public awareness level of drinking water quality and Pharmaceutical handling, the occurrence of nine Pharmaceutical Residues (amoxicillin, caffeine, chloramphenicol, ciprofloxacin, dexamethasone, diclofenac, nitrofurazone, sulfamethoxazole, and triclosan) and potential human health risks in drinking water from Kajang (Malaysia) using commercially competitive enzyme-linked immunosorbent assay kits. In general, the public awareness level of Kajang population showed poor knowledge (82.02%), and less positive attitude (98.88%) with a good practice score (57.3%). Ciprofloxacin was detected at the highest concentration (0.667 ng/L) while amoxicillin was at the lowest concentration (0.001 ng/L) in drinking water from Kajang (Malaysia). Nevertheless, all the reported occurrences were lower than previous studies conducted elsewhere. There was no appreciable potential human health risk for all the Pharmaceutical Residues as the risk quotient (RQ) values were less than 1 (RQ

Claudia P.b. Martins - One of the best experts on this subject based on the ideXlab platform.

  • artificial neural network modelling of Pharmaceutical Residue retention times in wastewater extracts using gradient liquid chromatography high resolution mass spectrometry data
    Journal of Chromatography A, 2015
    Co-Authors: Kelly Munro, Thomas H. Miller, Claudia P.b. Martins, Anthony M. Edge, David A. Cowan, Leon Barron
    Abstract:

    The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 Pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1 min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of <1.3 min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 blind test compounds in wastewater matrices lay at or above R2 = 0.92. Finally, the model was evaluated for application to the semi-targeted identification of Pharmaceutical Residues during a weeklong wastewater sampling campaign. The model successfully identified native compounds at a rate of 83 ± 4% and 73 ± 5% in influent and effluent extracts, respectively. The use of an HRMS database and the optimised ANN model was also applied to shortlisting of 37 additional compounds in wastewater. Ultimately, this research will potentially enable faster identification of emerging contaminants in the environment through more efficient post-acquisition data mining.

  • Artificial neural network modelling of Pharmaceutical Residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data.
    Journal of chromatography. A, 2015
    Co-Authors: Kelly Munro, Thomas H. Miller, Claudia P.b. Martins, Anthony M. Edge, David A. Cowan, Leon Barron
    Abstract:

    The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 Pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1 min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of

David A. Cowan - One of the best experts on this subject based on the ideXlab platform.

  • artificial neural network modelling of Pharmaceutical Residue retention times in wastewater extracts using gradient liquid chromatography high resolution mass spectrometry data
    Journal of Chromatography A, 2015
    Co-Authors: Kelly Munro, Thomas H. Miller, Claudia P.b. Martins, Anthony M. Edge, David A. Cowan, Leon Barron
    Abstract:

    The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 Pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1 min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of <1.3 min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 blind test compounds in wastewater matrices lay at or above R2 = 0.92. Finally, the model was evaluated for application to the semi-targeted identification of Pharmaceutical Residues during a weeklong wastewater sampling campaign. The model successfully identified native compounds at a rate of 83 ± 4% and 73 ± 5% in influent and effluent extracts, respectively. The use of an HRMS database and the optimised ANN model was also applied to shortlisting of 37 additional compounds in wastewater. Ultimately, this research will potentially enable faster identification of emerging contaminants in the environment through more efficient post-acquisition data mining.

  • Artificial neural network modelling of Pharmaceutical Residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data.
    Journal of chromatography. A, 2015
    Co-Authors: Kelly Munro, Thomas H. Miller, Claudia P.b. Martins, Anthony M. Edge, David A. Cowan, Leon Barron
    Abstract:

    The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 Pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1 min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of