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An HPLC method using C18-modified silica as stationary phase has been developed for environmental trace analysis of nine (fluoro)quinolones. Detection is done by fluorescence measurement or MS using the modes of SIM and selected reaction monitoring (SRM). Best separation is achieved with a gradient consisting of 50 mM formic acid and methanol, which is fully compatible with MS coupling. LOQs (S/N of 10) for fluorescence detection are between 10 and 60 microg/L, depending on the analyte. MS detection (SIM and SRM) yields LOQs that are better by a factor of at least an order of magnitude. Sample preconcentration and sample clean-up is accomplished by SPE (preconcentration factor of 1000), leading to LOQs in the low ng/L range. Recoveries of the preconcentration procedure are better than 80% for all analytes. The suitability for real samples has been demonstrated by analyzing surface waters, municipal waste waters, sewage treatment plant effluents, sewage sludge, and sediment taken from rivers and fish ponds. The method should also be useful for determination of residues of (fluoro)quinolones in food or other matrices. The degradation of the (fluoro)quinolones has been examined over 5 days in order to get information about the decomposition rate and the degradation products eventually occurring in the environment.

Drying behaviour of apple particles was investigated in a laboratory type dryer. The effect of drying air temperature, airflow velocity, initial height of layer, particles shape and size on the dehydration characteristics of apples was investigated. Increase in drying air temperature and increase in the airflow velocity caused a decrease in the drying time and an increase in drying rate. Increase in initial height of layer and increase in the sample thickness caused an increase in the drying time and decrease in drying rate. Drying time of the cubes was shorter and their drying rate was higher than for slices. The experimental dehydration data of apple particles obtained were fitted to the semi-theoretical, empirical and theoretical models. The accuracies of the models were measured using the correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), reduced chi-square (v 2 ), and t-statistic method. All models described the drying characteristics of apple particles satisfactorily (R > 0.9792). The Logarythmic model can be considered as the most appropriate (R > 0.9976, MBE = )10 )11 )4.5 · 10 )6 , RMSE = 0.00287-0.01746, v 2 = 8.5 · 10 )6 )3.1 · 10 )4 , t-stat = 7.3 · 10 )9 )1.2 · 10 )3 ). The effect of drying air temperature, airflow velocity, characteristic dimension of the particle and initial height of layer on the drying models parameters were also determined.

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In this study, an efficient optimisation method by combining response surface methodology (RSM) and genetic algorithm (GA) is introduced to find the optimal topology of artificial neural networks (ANNs) for predicting colour changes in rehydrated apple cubes. A multi-layered feed-forward backpropagation ANN model of algorithms was developed to correlate one output (colour change) to four input variables (drying air temperature, drying air velocity, temperature of distilled water and rehydration time). A predictive model for ANN topology in terms of the best mean squared error (MSE) performance on validation samples was created using RSM. RSM model was integrated with an effective GA to find the optimum topology of ANN. The optimum ANN had minimum MSE when the number of hidden neurons, learning rate, momentum constant, number of epochs and number of training runs were 13, 0.33, 0.89, 3869 and 3, respectively. MSE of optimal ANN topology on validation samples was 0.0072095. It turned out that the optimal ANN topology can be considered as more precise for predicting colour change in the rehydrated apple cubes. Mean absolute error and regression coefficient (R) of the optimal ANN topology were determined as 0.0259 and 0.96475 for training, 0.0399 and 0.95243 for testing and 0.0264 and 0.95151 for validation data sets. The results of the testing model on new samples showed excellent agreement between the actual and predicted data with coefficient of determination R 2 = 0.97.

A procedure based on HPLC and mass spectrometric detection has been developed for screening of residues of the illicit drug amphetamine in sewage sludge. Sample pretreatment consisted in extraction by 50 mM formic acid and methanol (80:20 v/v), followed by adjustment of the pH to 10 and preconcentration by SPE at poly(di-vinylbenzene)-N-vinylpyrrolidone. HPLC separation of the extract was done on a C18 RP with a mixture of 50 mM formic acid and methanol (80:20 v/v) as mobile phase. The mass spectrometer was operated in the MS2 and MS3 mode using the transition from m/z 136 to 119 and from m/z 119 to 91. Due to the complex matrix, ionization suppression effects as well as shifts in the sensitivity of the detector within a series of runs could not be fully excluded. Therefore, quantitation was done by standard addition together with external standards, so that semiquantitative results could be obtained down to concentrations of 2 microg/kg sewage sludge. Samples taken from various municipal sewage treatment plants indicate that amphetamine residues are ubiquitous in urban areas.