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

We describe the use of a computational neural network platform to optimize the fluorescence upon binding 5-carboxyfluorescein-d-Ala-d-Ala-d-Ala (5-FAM(DA)3 ) (1) to the antibiotic teicoplanin covalently attached to a glass slide. A three-level response surface experimental design was used as the first stage of investigation. Subsequently, three defined experimental parameters were examined by the neural network approach: (i) the concentration of teicoplanin used to derivatize a glass platform on the microfluidic device, (ii) the time required for the immobilization of teicoplanin on the platform, and (iii) the length of time 1 is allowed to equilibrate with teicoplanin in the microfluidic channel.

Metabolic pathway elucidation towards time- and dose-dependent electrophoretic screening of stable oxidative phenolic compounds.

This study demonstrates an untested link between model phenolic compounds and the formation/electrophoretic separation of stable urinary metabolites. Sterically encumbered carbonyl groups were examined, and mass determination was used to confirm the presence and stability of two oxidative metabolites of pentachlorophenol: tetrachloro-1,2-benzoquinone and tetrachloro-1,4-dihydroquinone. Subsequently, baseline resolved separation of pentachlorophenol and the two oxidative metabolites was demonstrated under the following conditions: 75 mM sodium tetraborate buffer (pH = 8.

Implementation of a genetically tuned neural platform in optimizing fluorescence from receptor-ligand binding interactions on microchips.

This paper describes 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-(D-Ala)(3) ) (1) to the antibiotic teicoplanin from Actinoplanes teichomyceticus electrostatically attached to a microfluidic channel originally modified with 3-aminopropyltriethoxysilane. Here, three parameters: (i) the length of time teicoplanin was in the microchannel; (ii) the length of time 1 was in the microchannel, thereby, in equilibrium with teicoplanin, and; (iii) the amount of time buffer was flushed through the microchannel to wash out any unbound 1 remaining in the channel, are examined at a constant concentration of 1, with neural network methodology applied to optimize fluorescence. Optimal neural structure provided a best fit model, both for the training set (r(2) = 0.

Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening.

This work reveals a computational framework for parallel electrophoretic separation of complex biological macromolecules and model urinary metabolites. More specifically, the implementation of a particle swarm optimization (PSO) algorithm on a neural network platform for multiparameter optimization of multiplexed 24-capillary electrophoresis technology with UV detection is highlighted. Two experimental systems were examined: (1) separation of purified rabbit metallothioneins and (2) separation of model toluene urinary metabolites and selected organic acids.

Supramolecular electrostatic nanoassemblies for bacterial forensics.

Electrostatic nanoassemblies were employed to identify bacterial growth conditions. They comprise a cationic conjugated oligoelectrolyte and fluorescein-tagged ssDNA and were optimized with a hybrid, computational neural network model. The photoluminescence spectra contained the oligomer and sensitized fluorescein emission.

Swarm intelligence metaheuristics for enhanced data analysis and optimization.

The swarm intelligence (SI) computing paradigm has proven itself as a comprehensive means of solving complicated analytical chemistry problems by emulating biologically-inspired processes. As global optimum search metaheuristics, associated algorithms have been widely used in training neural networks, function optimization, prediction and classification, and in a variety of process-based analytical applications. The goal of this review is to provide readers with critical insight into the utility of swarm intelligence tools as methods for solving complex chemical problems.

Identification of bacteria by conjugated oligoelectrolyte/single-stranded DNA electrostatic complexes.

Electrostatic complexes containing a cationic conjugated oligoelectrolyte (COE) and fluorescein (FAM)-labeled single-stranded DNA (ssDNA) serve as the basis for identifying various bacteria. The approach involves the preparation of five COE/ssDNA(x)-FAM electrostatic complexes, which differ in the ssDNA composition and which provide different photoluminescence (PL) spectra as a result of different degrees of energy transfer efficiency from the COE to FAM. Changes in the PL spectra upon addition of the bacteria can be quantified, and the differential response from the five ssDNAs gives rise to a multicomponent array response that allows identification of the microorganism under investigation.

Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC.

Cholesteryl esters have antimicrobial activity and likely contribute to the innate immunity system. Improved separation techniques are needed to characterize these compounds. In this study, optimization of the reversed-phase high-performance liquid chromatography separation of six analyte standards (four cholesteryl esters plus cholesterol and tri-palmitin) was accomplished by modeling with an artificial neural network-genetic algorithm (ANN-GA) approach.

Computational neural networks driving complex analytical problem solving.

Neural network computing demonstrates advanced analytical problem solving abilities to meet the demands of modern chemical research. (To listen to a podcast about this article, please go to the Analytical Chemistry multimedia page at pubs.acs.

Detection of geothermal phosphite using high-performance liquid chromatography.

Little is known about the prebiotic mechanisms that initiated the bioavailability of phosphorus, an element essential to life. A better understanding of phosphorus speciation in modern earth environments representative of early earth may help to elucidate the origins of bioavailable phosphorus. This paper presents the first quantitative measurements of phosphite in a pristine geothermal pool representative of early earth.

Design and development of an automated flow injection instrument for the determination of arsenic species in natural waters.

The design and development of an automated flow injection instrument for the determination of arsenite [As(III)] and arsenate [As(V)] in natural waters is described. The instrument incorporates solenoid activated self-priming micropumps and electronic switching valves for controlling the fluidics of the system and a miniature charge-coupled device spectrometer operating in a graphical programming environment. The limits of detection were found to be 0.

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

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.

Application of artificial neural networks in the prediction of product distribution in electrophoretically mediated microanalysis.

The successful application of artificial neural networks toward the prediction of product distribution in electrophoretically mediated microanalysis is presented. To illustrate this concept, we examined the factors and levels required for optimization of reaction conditions for the conversion of nicotinamide adenine dinucleotide to nicotinamide adenine dinucleotide, reduced form by glucose-6-phosphate dehydrogenase in the conversion of glucose-6-phosphate to 6-phosphogluconate. A full factorial experimental design examining the factors voltage, enzyme concentration, and mixing time of reaction was utilized as input-output data sources for suitable artificial neural networks training for prediction purposes.

Univariate and multivariate optimization of the separation conditions for the analysis of five bisphenols by micellar electrokinetic chromatography.

In this paper we used a chromatographic response function (CRF) that included an output for each of the two performance parameters (resolution and analysis time) to optimize the separation of five bisphenols by micellar electrokinetic chromatography (MEKC) using multivariate response surface methodology (RSM). To validate the real utility of this approach, we have also compared the efficiency of the proposed optimization method with a traditional univariate analysis. For both methods, the selected variables of the analysis were: buffer concentration, pH, amount of organic solvent, and concentration of surfactant.

The development of iodide-based methods for batch and on-line determinations of phosphite in aqueous samples.

Recent developments in the field of microbiology and research on the origin of life have suggested a possible significant role for reduced, inorganic forms of phosphorus (P) such as phosphite [HPO(3)(2-), P(+III)] and hypophosphite [H(2)PO(2)(-), P(+I)] in the biogeochemical cycling of P. New, robust methods are required for the detection of reduced P compounds in order to confirm the importance of these species in the overall cycling of P in the environment. To this end, we have developed new batch and flow injection (FI) methods for the determination of P(+III) in aqueous solutions.

Reduced inorganic phosphorus in the natural environment: significance, speciation and determination.

It is commonly assumed that phosphorus occurs almost exclusively in the environment as fully oxidized phosphate (primarily H(2)PO(4)(-) and HPO(4)(2-), where the oxidation state of phosphorus is +V). Recent developments in the field of microbiology and research on the origin of life have suggested a possibly significant role for reduced, inorganic forms of phosphorus in bacterial metabolism and as evolutionary precursors of biological phosphate compounds. Reduced inorganic forms of phosphorus include phosphorus acid (H(3)PO(3), P(+III)), hypophosphorus acid (H(3)PO(2), P(+I)) and various forms of phosphides (P(-III)).

Sampling, sample treatment and quality assurance issues for the determination of phosphorus species in natural waters and soils.

Phosphorus is an important macronutrient and the accurate determination of phosphorus species in environmental matrices such as natural waters and soils is essential for understanding the biogeochemical cycling of the element, studying its role in ecosystem health and monitoring compliance with legislation. This paper provides a critical review of sample collection, storage and treatment procedures for the determination of phosphorus species in environmental matrices. Issues such as phosphorus speciation, the molybdenum blue method, digestion procedures for organic phosphorus species, choice of model compounds for analytical studies, quality assurance and the availability of environmental CRMs for phosphate are also discussed in detail.

Chemometrical examination of active parameters and interactions in flow injection-capillary electrophoresis.

The first detailed examination of flow injection-capillary electrophoresis (FI-CE) active parameters and their interactions via response surface methodology (RSM) is presented. Specifically, RSM in the form of a Box-Behnken design was implemented to effectively predict the significance of capillary length, voltage and injection volume on the optimization of an in-house built FI-CE analyzer. Initial studies were performed assessing peak height and peak shape of the model compound N,N-dimethylformamide.

Use of chemometric methodology in optimizing conditions for competitive binding partial filling affinity capillary electrophoresis.

This work expands the knowledge of the use of chemometric response surface methodology (RSM) in optimizing conditions for competitive binding partial filling ACE (PFACE). Specifically, RSM in the form of a Box-Behnken design was implemented in flow-through PFACE (FTPFACE) to effectively predict the significance of injection time, voltage, and neutral ligand (neutral arylsulfonamide) concentration, [L(o)], on protein-neutral ligand binding. Statistical analysis results were used to create a model for response surface prediction via contour and surface plots at a given maximum response (DeltaRMTR) to reach a targeted K(b) = 2.

Response surface examination of the relationship between experimental conditions and product distribution in electrophoretically mediated microanalysis.

This work presents the first known use of response surface methodology (RSM) in electrophoretically mediated microanalysis. This concept is demonstrated by examining the optimization of reaction conditions for the conversion of nicotinamide adenine dinucleotide to nicotinamide adenine dinucleotide, reduced form by glucose-6-phosphate dehydrogenase (G6PDH, EC 1.1.

Chemometric experimental design based optimization techniques in capillary electrophoresis: a critical review of modern applications.

A critical review of recent developments in the use of chemometric experimental design based optimization techniques in capillary electrophoresis applications is presented. Current advances have led to enhanced separation capabilities of a wide range of analytes in such areas as biological, environmental, food technology, pharmaceutical, and medical analysis. Significant developments in design, detection methodology and applications from the last 5 years (2002-2007) are reported.

Method development and validation for optimized separation of benzo[a]pyrene-quinone isomers using liquid chromatography-mass spectrometry and chemometric response surface methodology.

The successful separation of three benzo[a]pyrene-quinone isomers, two of which were previously unresolved, using liquid chromatography-mass spectrometry (LC-MS) and response surface methodology is presented. Initial efforts centered on chromatographic separation of benzo[a]pyrene-1,6/3,6-quinone peaks evaluated for both resolution and retention time. The mergence of the two parameters was accomplished using the Derringer's desirability function with subsequent optimization by a Box-Behnken response surface design.

Design and fabrication of chemically robust three-dimensional microfluidic valves.

A current problem in microfluidics is that poly(dimethylsiloxane) (PDMS), used to fabricate many microfluidic devices, is not compatible with most organic solvents. Fluorinated compounds are more chemically robust than PDMS but, historically, it has been nearly impossible to construct valves out of them by multilayer soft lithography (MSL) due to the difficulty of bonding layers made of "non-stick" fluoropolymers necessary to create traditional microfluidic valves. With our new three-dimensional (3D) valve design we can fabricate microfluidic devices from fluorinated compounds in a single monolithic layer that is resistant to most organic solvents with minimal swelling.

Design and development of a flow injection-capillary electrophoresis analyzer employing fiber optic detection.

The development and experimental optimization of a novel flow injection-capillary electrophoresis (FI-CE) analyzer employing UV-visible fiber optic detection is described. The analyzer incorporates a miniature charge-coupled device (CCD) spectrometer and operates in a graphical programming environment. Data from experimental optimization studies and small molecule separations involving affinity capillary electrophoresis (ACE) and indirect detection of anions are presented.

Implementation of chemometric methodology in ACE: predictive investigation of protein-ligand binding.

An ACE predictive investigation of protein-ligand binding using a highly effective chemometric response surface design technique is presented. Here, K(d) was estimated using one noninteracting standard which relates to changes in the electrophoretic mobility of carbonic anhydrase B (CAB, EC 4.2.