Assessment of chromatographic peak purity by means of artificial neural networks

Comparison of general rate model with a new model—artificial neural network model in describing chromatographic kinetics of solanesol adsorption in packed column by macroporous resins

Herein, two models, the general rate model taking into account convection, axial dispersion, external and intra-particle mass transfer resistances and particle size distribution (PSD) and the artificial neural network model (ANN) were developed to describe solanesol adsorption process in packed column using macroporous resins. First, Static equilibrium experiments and kinetic experiments in packed column were carried out respectively to obtain experimental data. By fitting static experimental data, Langmuir isotherm and Freundlich isotherm were estimated, and the former one was used in simulation coupled with general rate model considering better correlative coefficients. The simulated results showed that theoretical predictions of general rate model with PSD were well consistent with experimental data. Then, a new model, the ANN model, was developed to describe present adsorption process in packed column. The encouraging simulated results showed that ANN model could describe present system even better than general rate model. At last, by using the predictive ability of ANN model, the influence of each experimental parameter was investigated. Predicted results showed that with the increases of particle porosity and the ratio of bed height to inner column diameter (ROHD), the breakthrough time was delayed. On the contrary, an increase in feed concentration, flow rate, mean particle diameter and bed porosity decreased the breakthrough time.

Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research

Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine and incorporate both literature-based and experimental data to solve problems. The various applications of ANNs can be summarised into classification or pattern recognition, prediction and modeling. Supervised 'associating networks can be applied in pharmaceutical fields as an alternative to conventional response surface methodology. Unsupervised feature-extracting networks represent an alternative to principal component analysis. Non-adaptive unsupervised networks are able to reconstruct their patterns when presented with noisy samples and can be used for image recognition. The potential applications of ANN methodology in the pharmaceutical sciences range from interpretation of analytical data, drug and dosage form design through biopharmacy to clinical pharmacy.

Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography

The separation process in capillary micellar electrochromatography (MEKC) can be modelled using artificial neural networks (ANNs) and optimisation of MEKC methods can be facilitated by combining ANNs with experimental design. ANNs have shown attractive possibilities for non-linear modelling of response surfaces in MEKC and it was demonstrated that by combining ANN modelling with experimental design, the number of experiments necessary to search and find optimal separation conditions can be reduced significantly. A new general approach for computer-aided optimisation in MEKC has been proposed which, because of its general validity, can also be applied in other separation techniques.

Capillary zone electrophoresis determination of galanthamine in biological fluids and pharmaceutical preparatives: experimental design and artificial neural network optimization

Galanthamine is a third-generation cholinesterase inhibitor used against Alzheimer's disease. New analytical methods for the determination of galanthamine in pharmaceutical preparatives and biological fluids, such as urine and serum, were developed. An experimental design and artificial neural network approach were used for method optimization. Thirty-five ppb of galanthamine were determined in serum samples (with addition of 10 mM magnesium chloride and using solid-phase preconcentration).

Analyte identification in capillary electrophoretic separation techniques

A review on applications of on-line hyphenation in capillary electrophoresis and capillary electrochromatography for the identification of migrating analytes is presented. There is an urgent need for unambiguous analyte identification by combining spectral information and observed migration times, because the parameters influencing the migration times and separation efficiencies in these separation techniques are not easily controlled, especially when real samples containing unknown interferences have to be analyzed. The spectrometric techniques covered here are ultraviolet and visible radiation (UV/Vis) absorption, fluorescence including fluorescence line-narrowing spectroscopy, Raman spectroscopy, nuclear magnetic resonance and mass spectrometry. Attention is essentially confined to literature reports in which the extra information provided by the detector is really used for identification purposes, especially in real-life samples, while the interfacing as such and analyte detectabilities in standard solutions are only briefly discussed. This article covers an extensive fraction of the literature published on this topic until the beginning of 1998.