Anthony Edge - Academia.edu (original) (raw)
Related Authors
Indian Institute of Chemical Technology
Uploads
Papers by Anthony Edge
Journal of Chromatography A, May 1, 2015
Journal of chromatography. A, Jan 30, 2015
The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient... more 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 >1min 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 <1.3min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 bli...
Journal of Chromatography A, 2012
We present the first investigation into the utility of porous graphitic carbon (PGC) as a station... more We present the first investigation into the utility of porous graphitic carbon (PGC) as a stationary phase in proteomic workflows involving complex samples. PGC offers chemical and physical robustness and is capable of withstanding extremes of pH and higher temperatures than traditional stationary phases, without the likelihood of catastrophic failure. In addition, unlike separations driven by ion exchange mechanisms, there is no requirement for high levels of non-volatile salts such as potassium chloride in the elution buffers, which must be removed prior to LC-MS analysis. Here we present data which demonstrate that PGC affords excellent peptide separation in a complex whole cell lysate digest sample, with good orthogonality to a typical low pH reversed-phase system. As strong cation exchange (SCX) is currently the most popular first dimension for 2D peptide separations, we chose to compare the performance of a PGC and SCX separation as the first dimension in a comprehensive 2D-LC-MS/MS workflow. A significant increase, in the region of 40%, in peptide identifications is reported with off-line PGC fractionation compared to SCX. Around 14,000 unique peptides were identified at an estimated false discovery rate of 1% (n = 3 replicates) from starting material constituting only 100 g of protein extract.
Journal of Chromatography A, May 1, 2015
Journal of chromatography. A, Jan 30, 2015
The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient... more 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 >1min 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 <1.3min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 bli...
Journal of Chromatography A, 2012
We present the first investigation into the utility of porous graphitic carbon (PGC) as a station... more We present the first investigation into the utility of porous graphitic carbon (PGC) as a stationary phase in proteomic workflows involving complex samples. PGC offers chemical and physical robustness and is capable of withstanding extremes of pH and higher temperatures than traditional stationary phases, without the likelihood of catastrophic failure. In addition, unlike separations driven by ion exchange mechanisms, there is no requirement for high levels of non-volatile salts such as potassium chloride in the elution buffers, which must be removed prior to LC-MS analysis. Here we present data which demonstrate that PGC affords excellent peptide separation in a complex whole cell lysate digest sample, with good orthogonality to a typical low pH reversed-phase system. As strong cation exchange (SCX) is currently the most popular first dimension for 2D peptide separations, we chose to compare the performance of a PGC and SCX separation as the first dimension in a comprehensive 2D-LC-MS/MS workflow. A significant increase, in the region of 40%, in peptide identifications is reported with off-line PGC fractionation compared to SCX. Around 14,000 unique peptides were identified at an estimated false discovery rate of 1% (n = 3 replicates) from starting material constituting only 100 g of protein extract.