Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.     

Peptide Formation by Aminolysis of 1-Thio-β-D-glucopyranosyl Esters of N-Acylaminoacids

Abstract

The susceptibility of the fully acetylated 1-thio–β-D-glucopyranosyl esters of N-protected amino acids toward the amino group of an external amino acid-or peptide-ester was examined in dichloromethane at room temperature and at 40°, respectively. In each case, the aminolysis reaction led to rupture of the C-1 thiolester bond and formation of the corresponding N-acylpeptide ester; the reaction proceeded without racemization of the aglycon chiral centre. Evidence for a remarkably high acylating efficiency of the sugar—amino acid C-1 thiolester bond is presented.     

Stability of phonon branch of the Bose spectrum of a superfluid Fermi system for large momenta

The phonon branch of Bose spectrum of a superfluid Fermi system (Bogolyubov sound) is investigated atT=0. It is shown that a Bogolyubov sound quantum (with arbitrary momentum) is stable and cannot decay into several excitations with lower energy.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 131, pp. 114–117, 1983.     

Tracing glycoprotein structures: electrospray ionization tandem mass spectrometric analysis of sugar-peptide adducts

Owing to the diversity of carbohydrate structures and their significance for the function of many biopolymers, structural analysis of various carbohydrate-related compounds is of great importance. Electrospray ionization tandem mass spectrometry (ESI-MS/MS) was used to establish the fragmentation behaviour of a range of sugar-peptide adducts as model compounds of widespread glycoprotein structures. The compounds used in this study were chosen to provide correlation of distinct fragment ions with specific structural differences, namely position and type of carbohydrate-peptide bond and structure of the sugar moiety. All compounds show N- and C-terminal sequence ions along with losses of up to three water molecules. Fructose-related Amadori compounds exhibit M + 78 modified N-terminal peptide fragment ions. Fragmentation of glucose-peptide esters is characterized by the sugar ring fragmentation. Additionally, under the ESI-MS conditions applied, the esters studied undergo intramolecular reaction giving cyclic sugar-peptide structures that can be traced by the presence of N-terminal peptide M + 42 adducts. Detailed analysis of cyclic fructose-related compound comprising structural features of both studied groups revealed a rich fragmentation pattern derived from amino acid residues and water molecules losses from [M - 2H(2)O + H](+) ion. Also, some interesting differences were found with respect to the nature of carbohydrate moieties.     

Development of an ion chromatographic gradient retention model from isocratic elution experiments

When facing separation problems in ion chromatography, chromatographers often lack guidelines to decide a priori if isocratic elution will give enough separation in a reasonable analysis time or a gradient elution will be required. This situation may be solved by the prediction of retention in gradient elution mode by using isocratic experimental data. This work describes the development of an ion chromatographic gradient elution retention model for fluoride, chloride, nitrite, bromide, nitrate, sulfate and phosphate by using isocratic experimental data. The isocratic elution retention model was developed by applying a polynomial relation between the logarithm of the retention factor and logarithm of the concentration of competing ions; the gradient elution retention model was based on the stepwise numerical integration of the corresponding differential equation. It was shown that the developed gradient elution retention model was not significantly affected by transferring data form isocratic experiment. The root mean squared prediction error for gradient elution retention model was between 0.0863 for fluoride and 0.7027 for bromide proving a very good predictive ability of developed gradient elution retention model.     

STREAK CAMERA MEASUREMENT OF TRYPTOPHAN AND RHODAMINE MOTIONS WITH PICOSECOND TIME RESOLUTION

We have used a signal averaging laser (30 ps pulse)-streak camera system to measure fluorescence anisotropy decays of dyes in solution and protein tryptophan chromophores with picosecond time resolution. Emission polarization as a function of time can be measured directly and simultaneously for both polarization directions and stored in an optical multichannel analyzer. The corrected anisotropy is computed after background subtraction. Rotations of free dyes in solution (Sulforhodamine 101, tryptophan) and the temperature-dependent internal motions of human serum albumin (tryptophan emission) are displayed.     

Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms

This paper describes development of artificial neural network (ANN) retention model, which can be used for method development in variety of ion chromatographic applications. By using developed retention model it is possible both to improve performance characteristic of developed method and to speed up new method development by reducing unnecessary experimentation. Multilayered feed forward neural network has been used to model retention behaviour of void peak, lithium, sodium, ammonium, potassium, magnesium, calcium, strontium and barium in relation with the eluent flow rate and concentration of methasulphonic acid (MSA) in eluent. The probability of finding the global minimum and fast convergence at the same time were enhanced by applying a two-phase training procedure. The developed two-phase training procedure consists of both first and second order training. Several training algorithms were applied and compared, namely: back propagation (BP), delta-bar-delta, quick propagation, conjugate gradient, quasi Newton and Levenberg-Marquardt. It is shown that the optimized two-phase training procedure enables fast convergence and avoids problems arisen from the fact that every new weight initialization can be regarded as a new starting position and yield irreproducible neural network if only second order training is applied. Activation function, number of hidden layer neurons and number of experimental data points used for training set were optimized in order to insure good predictive ability with respect to speeding up retention modelling procedure by reducing unnecessary experimental work. The predictive ability of optimized neural networks retention model was tested by using several statistical tests. This study shows that developed artificial neural network are very accurate and fast retention modelling tool applied to model varied inherent non-linear relationship of retention behaviour with respect to mobile phase parameters.     

Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions

In this work, three different methods for modeling of gradient retention were combined with several optimization objective functions in order to find the most appropriate combination to be applied in ion chromatography method development. The system studied was a set of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) with a KOH eluent. The retention modeling methods tested were multilayer perceptron artificial neural network (MLP-ANN), radial-basis function artificial neural network (RBF-ANN), and retention model based on transfer of data from isocratic to gradient elution mode. It was shown that MLP retention model in combination with the objective function based on normalized retention difference product was the most adequate tool for optimization purposes.     

Characterization of Azerbaijani and Croatian brown naphthalane

In spite of the fact that noncolored (nonaromatic) naphthalane containing high sterane content has been proved as bioactive and efficient in the treatment of psoriasis vulgaris, brown naphthalanes, which contain aromatics, are still in use. Thousands of patients, who have been passing through the therapy, deserve thorough and permanent study of brown naphthalanes. For that reason, two frequently used brown naphthalane preparations, one of the Azerbaijani (N1) and the other of the Croatian (N2) origin, were studied. The samples underwent the study by means of GC, OT LC, and normal-phase HPLC, as separation techniques. In addition, some bulk properties, elemental composition, and group composition by 1H NMR were determined. GC-MS served in compound types, especially in sterane detection. Both of the samples were found to be unresolved complex mixtures, relatively poor in n-alkanes. Isoalkanes, cycloalkanes, and aromatic compounds, in a great variety of isomers and homologs, made the majority of both of the samples. N1 was a dark, viscous, nontransparent fluid with a strong petroleum smell. Aromatics, which made 53% mass by OT LC and 54% mass by 1H NMR were found to be composed of mono-, di-, and tri+ -aromatic compounds. Tri+ -aromatic compounds were three to four times more abundant in N1 than in N2. Beside hydrocarbons, N1 comprised some organic compounds with polar functional groups. Also, some asphaltenes were found in it. N1 contained well-presented steranes, which are thought to be bioactive naphthalane ingredients. N2 was a pale brown liquid, with smell similar to gasoline. It contained somewhat lower percentage of aromatics (46% mass) and comparatively lower percentage of tri+ fused aromatics among which carcinogens might be expected. N2 was almost purely composed of hydrocarbons. It seemed to contain low content of steranes due to relatively low upper temperature of the distillation range applied in N2 preparation.     

Ion chromatographic method development for monitoring of seawater quality used in over-the-counter pharmaceutical industry

A methodology is developed for the analysis of inorganic anions (fluoride, chloride, bromide, sulphate) in seawater used for over-the-counter (OTC) nasal spray production. The eluent flow rate and concentration of eluent competing ions are optimised by using an artificial neural network resolution model in combination with normalised resolution product criterion function. The developed artificial neural network resolution model shows good predictive ability R2 > or = 0.9973. The determined ion chromatographic parameters enable baseline separation of all components of interest. By performing a validation procedure and a number of statistical tests, it is shown that the developed ion chromatographic method has superior performance characteristics: linearity R2 > or = 0.9993, recovery = 99.77-100.65%, repeatability RSD < or = 1.85%. This result proves that the proposed method can be used for routine quality assurance analysis in OTC pharmaceutical industry.