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Determination of amoxicillin in poultry plasma by high-performance liquid chromatography after formaldehyde derivation

Krasucka D, Kowalski C. J, Osypiuk M, Opielak G

Acta Chromatographica

2015

Vol. 27, no. 1

55--65

A high-performance liquid chromatographic (HPLC) method with fluorescence detection after precolumn formaldehyde derivation was developed to detect concentrations of amoxicillin (AMX) in poultry plasma. Proteins in plasma samples spiked with AMX were precipitated with a phosphate buffer and trichloroacetic acid. After precolumn treatment of the extraction product of AMX with formaldehyde under acidic and heating conditions, HPLC analysis with fluorescence (FL) detection at an excitation wavelength of 355 nm and an emission wavelength of 450 nm was performed. A mobile phase comprising acetonitrile and a buffer solution (0.05 M KH2PO4 pH = 5.6), which yielded AMX retention time 8.58 min, was suitable for detection of AMX. The calculated standard curve of the reaction product was linear, and the correlation coefficient was greater than 0.999. The limit of detection and quantification, the accuracy, and the precision were evaluated. Recoveries of spiked amoxicillin were >92%, with a coefficient of variation in the range of 0.35–0.89%. This method has been successfully applied to a pharmacokinetic study after oral administration of amoxicillin to poultry.

Fuzzy models of the biological-chemical processes in the west-water treatment plant

Szabat K., Kowalski C. J

Computer Applications in Electrical Engineering

2010

Vol. 8

230--241

In the work issues related to control of the electrical blowers installed in the waste water treatment plant (WWTP) are demonstrated. After a short introduction a description of a real treatment plant is presented. Then the main sewage drives are described. Next the commonly used control strategies for the biological-chemical process are presented. The model ASM1 of the biological chemical transformation used in the advanced MPC (model predictive control) strategy is introduced. Then the fuzzy system based on the TSK model is presented. The ability of the TSK fuzzy system to estimate the biological-chemical variables are demonstrated.