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Mabel Beatriz Tudino - One of the best experts on this subject based on the ideXlab platform.

  • a novel combination of experimental design and artificial neural networks as an Analytical Tool for improving performance in thermospray flame furnace atomic absorption spectrometry
    Chemometrics and Intelligent Laboratory Systems, 2016
    Co-Authors: Ezequiel Martin Morzan, Jorge Stripeikis, Hector C Goicoechea, Mabel Beatriz Tudino
    Abstract:

    Abstract In this work, we present the combined effect of artificial neural networks (ANN) and experimental design as a suitable Analytical Tool for improving the performance of thermospray flame furnace atomic absorption spectrometry (TS-FFAAS) using Mg as leading case. To this end, mixtures of different amounts of methanol, ethanol, and i-propanol in water were assayed as carriers at different flow rates and different flame stoichiometries (air/acetylene ratios). Different levels of these variables determined the experimental domain, consisting in a cube which was divided into eight identical cubical regions that allowed increase in the number of available experimental points. A Box–Behnken design (BBD) was employed in each one of the regions. The name Multiple Box–Behnken design (MBBD) was given to this new approach. Then, the features of ANN were exploited to find the optimum conditions for conducting Mg determination by TS-FFAAS. The prediction capability of ANN was examined and compared to the least-squares (LS) fitting when applied to the response surface method (RSM). The suitability of the new approach and the implications on TS-FFAAS Analytical performance are discussed.

Ezequiel Martin Morzan - One of the best experts on this subject based on the ideXlab platform.

  • a novel combination of experimental design and artificial neural networks as an Analytical Tool for improving performance in thermospray flame furnace atomic absorption spectrometry
    Chemometrics and Intelligent Laboratory Systems, 2016
    Co-Authors: Ezequiel Martin Morzan, Jorge Stripeikis, Hector C Goicoechea, Mabel Beatriz Tudino
    Abstract:

    Abstract In this work, we present the combined effect of artificial neural networks (ANN) and experimental design as a suitable Analytical Tool for improving the performance of thermospray flame furnace atomic absorption spectrometry (TS-FFAAS) using Mg as leading case. To this end, mixtures of different amounts of methanol, ethanol, and i-propanol in water were assayed as carriers at different flow rates and different flame stoichiometries (air/acetylene ratios). Different levels of these variables determined the experimental domain, consisting in a cube which was divided into eight identical cubical regions that allowed increase in the number of available experimental points. A Box–Behnken design (BBD) was employed in each one of the regions. The name Multiple Box–Behnken design (MBBD) was given to this new approach. Then, the features of ANN were exploited to find the optimum conditions for conducting Mg determination by TS-FFAAS. The prediction capability of ANN was examined and compared to the least-squares (LS) fitting when applied to the response surface method (RSM). The suitability of the new approach and the implications on TS-FFAAS Analytical performance are discussed.

Jorge Stripeikis - One of the best experts on this subject based on the ideXlab platform.

  • a novel combination of experimental design and artificial neural networks as an Analytical Tool for improving performance in thermospray flame furnace atomic absorption spectrometry
    Chemometrics and Intelligent Laboratory Systems, 2016
    Co-Authors: Ezequiel Martin Morzan, Jorge Stripeikis, Hector C Goicoechea, Mabel Beatriz Tudino
    Abstract:

    Abstract In this work, we present the combined effect of artificial neural networks (ANN) and experimental design as a suitable Analytical Tool for improving the performance of thermospray flame furnace atomic absorption spectrometry (TS-FFAAS) using Mg as leading case. To this end, mixtures of different amounts of methanol, ethanol, and i-propanol in water were assayed as carriers at different flow rates and different flame stoichiometries (air/acetylene ratios). Different levels of these variables determined the experimental domain, consisting in a cube which was divided into eight identical cubical regions that allowed increase in the number of available experimental points. A Box–Behnken design (BBD) was employed in each one of the regions. The name Multiple Box–Behnken design (MBBD) was given to this new approach. Then, the features of ANN were exploited to find the optimum conditions for conducting Mg determination by TS-FFAAS. The prediction capability of ANN was examined and compared to the least-squares (LS) fitting when applied to the response surface method (RSM). The suitability of the new approach and the implications on TS-FFAAS Analytical performance are discussed.

Hector C Goicoechea - One of the best experts on this subject based on the ideXlab platform.

  • a novel combination of experimental design and artificial neural networks as an Analytical Tool for improving performance in thermospray flame furnace atomic absorption spectrometry
    Chemometrics and Intelligent Laboratory Systems, 2016
    Co-Authors: Ezequiel Martin Morzan, Jorge Stripeikis, Hector C Goicoechea, Mabel Beatriz Tudino
    Abstract:

    Abstract In this work, we present the combined effect of artificial neural networks (ANN) and experimental design as a suitable Analytical Tool for improving the performance of thermospray flame furnace atomic absorption spectrometry (TS-FFAAS) using Mg as leading case. To this end, mixtures of different amounts of methanol, ethanol, and i-propanol in water were assayed as carriers at different flow rates and different flame stoichiometries (air/acetylene ratios). Different levels of these variables determined the experimental domain, consisting in a cube which was divided into eight identical cubical regions that allowed increase in the number of available experimental points. A Box–Behnken design (BBD) was employed in each one of the regions. The name Multiple Box–Behnken design (MBBD) was given to this new approach. Then, the features of ANN were exploited to find the optimum conditions for conducting Mg determination by TS-FFAAS. The prediction capability of ANN was examined and compared to the least-squares (LS) fitting when applied to the response surface method (RSM). The suitability of the new approach and the implications on TS-FFAAS Analytical performance are discussed.

C Shaw - One of the best experts on this subject based on the ideXlab platform.

  • spiqe an automated Analytical Tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis
    Clinical Neurophysiology, 2019
    Co-Authors: James Bashford, Kerry R. Mills, Aidan Wickham, Raquel Iniesta, Emmanuel M. Drakakis, Martyn G. Boutelle, C Shaw
    Abstract:

    Abstract Objectives Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated Analytical Tool. Methods Six ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model’s predictive ability. Results By applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/− 0.2 SEM), specificity of 91.6% (+/− 0.1 SEM) and classification accuracy of 87.9% (+/− 0.1 SEM). An amplitude exclusion threshold (100 μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p = 0.394). Conclusion We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). Significance Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.

  • spiqe an automated Analytical Tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis
    bioRxiv, 2019
    Co-Authors: James Bashford, Kerry R. Mills, Aidan Wickham, Raquel Iniesta, Emmanuel M. Drakakis, Martyn G. Boutelle, C Shaw
    Abstract:

    Abstract OBJECTIVES Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated Analytical Tool. METHODS Six ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model’s predictive ability. RESULTS By applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/-0.2 SEM), specificity of 91.6% (+/-0.1 SEM) and classification accuracy of 87.9% (+/-0.1 SEM). An amplitude exclusion threshold (100μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p=0.394). CONCLUSION We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). SIGNIFICANCE Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health. Highlights SPiQE combines serial high-density surface EMG with an innovative signal-processing methodology SPiQE identifies fasciculations in ALS patients with high sensitivity and specificity The optimal noise-responsive model achieves an average classification accuracy of 88%