On-capillary derivatization using a hybrid artificial neural network-genetic algorithm approach

Abstract

The first reported hybrid artificial neural network-genetic algorithm (ANN-GA) approach for the optimization of on-capillary dipeptide derivatization is presented. More specifically, genetic optimization proved valuable in the determination of effective network structure with three defined parameter inputs: (i) phthalic anhydride injection volume, (ii) time of injection, and (iii) voltage, for the maximum conversion of the dipeptide D-Ala-D-Ala by phthalic anhydride. Results obtained from the hybrid approach proved superior to an ANN model without GA optimization in terms of training data and predictive ability. The model developed will likely prove useful for the analysis of other organic-based reaction systems.

On‐capillary derivatisation as an approach to enhancing sensitivity in capillary electrophoresis

“…In addition, the method was able to analyse the insulin and its two glycated forms in the plasma of diabetic patients. Gomez's group used the dipeptide d ‐Ala‐ d ‐Ala as a model sample for the development of a hybrid artificial neural network algorithm for optimising the on‐capillary derivatisation‐based EMMA methodology .…”

Computational Neural Networks Driving Complex Analytical Problem Solving

“…In this study, a GA was developed to normalize the peptide retention data into a range (0−1), improving the peptide elution time reproducibility to ∼1%. A more recent study by our group used a neural network-GA approach to optimize on-capillary dipeptide derivatization . More specifically, for the maximum conversion of the dipeptide d -Ala- d -Ala by phthalic anhydride, genetic optimization proved valuable in the determination of effective network structure with three defined parameter inputs: 1) phthalic anhydride injection volume, 2) time of injection, and 3) voltage.…”

Application of a computational neural network to optimize the fluorescence signal from a receptor–ligand interaction on a microfluidic chip

“…While much of the current research on microfluidic devices has focused on the technology and applications of devices, there has been little focus on statistical methods and computational techniques examining the role experimental parameters have on experimental outcomes in microfluidic devices as well as their role in improving the performance of the devices . Previous work has demonstrated the use of statistical methods and computational techniques in different systems to study the effects that experimental variables have on an output response for optimization .…”

“…Proficient treatment of these factors is critical to optimize the resolution or response of a given analysis in the shortest time frame possible. In recent efforts to explore the effects of experimental variables on output response in CE, we employed response surface methodology and neural networks to estimate affinity constants between receptors and ligands in flow‐through partial filling affinity capillary CE (FTPFACE) and enzyme reaction conditions using electrophoretically mediated microanalysis (EMMA), respectively . We recently described the use of a genetically tuned neural network platform to optimize the fluorescence realized upon binding 5‐carboxyfluorescein‐ d ‐Ala‐ d ‐Ala‐ d ‐Ala (5‐FAM(DA) 3 ) ( 1 ) to the antibiotic teicoplanin from Actinoplanes teichomyceticus electrostatically attached to a microfluidic channel originally modified with 3‐aminopropyltriethoxysilane (APTES) .…”