Aquaculture production has globally increased and its environmental impact is not well understood and assessed yet. Therefore, in this work nine metals and metalloids (Cu, Cd, Pb, Hg, Ni, Fe, Mn, Zn and As) and three nutrients (P, N and C) that seem to accumulate in marine sediments, were determined under the fish cages (zero distance) and about 50 and 100 m away from them, in three aquacultures in Greece. The analysis of these data is crucial due to the negative impact of the intensive aquaculture activities on fish population, human health and marine environment. This study investigated the environmental impact associated with aquaculture cages on marine sediments, using Supervised Artificial Neural Networks (ANNs) in parallel with Classification Trees (CTs). Optimised models were constructed in order to detect the significance of each variable, predict the origin of the sediment samples and successfully visualise their results. Three popular ANN architectures, as multi-layer perceptrons (MLPs), radial basis function (RBF) and counter propagation artificial neural networks (CP-ANNs) were used to assess the impact of the intensive aquaculture activities on marine sediments. In addition, more traditional multivariate chemometric techniques like CTs were applied to the same data set for comparison purposes. The modelling study showed that P, N, Cu, Cd were the most critical (and polluting) factors of those metals studied. Moreover, single-element models achieved elevated predictive percentages. The results were justified due to the usual practices used for fish feeding or cages maintenance.

This work reports the design and construction of a novel potentiostat which features an integrator transimpedance amplifier as a current-monitoring unit. The integration approach addresses the limitations of the feedback resistor approach used for current monitoring in conventional potentiostat designs. In the present design, measurement of the current is performed by a precision switched integrator transimpedance amplifier operated in the dual sampling mode which enables sub-pA resolution. The potentiostat is suitable for measuring very low currents (typical dynamic range: 5 pA-4.7 μA) with a 16 bit resolution, and it can support 2-, 3- and 4-electrode cell configurations. Its operation was assessed by using it as a detection module in a home-made capillary electrophoresis system for the separation and amperometric detection of paracetamol and p-aminophenol at a 3-electrode microfluidic chip. The potential and limitations of the proposed potentiostat to implement fast potential-scan voltammetric techniques were demonstrated for the case of cyclic voltammetry.

The impact of intensive aquaculture activities on marine sediments along three coastal areas in Greece was studied. The content of nine metals/metalloids (Cu, Cd, Pb, Hg, Ni, Fe, Mn, Zn, As), and three nutrients (P, N and C), that seem to accumulate in marine sediments, was determined under the fish cages (zero distance) and away (50 or 100m) from them. Elevated concentrations for phosphorus, nitrogen, copper, zinc and cadmium were recorded in the areas where farming establishments are moored. In parallel, the intrinsic differences between the aquaculture facilities and their seasonal variations were investigated. The individual characteristics of each farm (local water currents, facilities' capacity, transferring mechanisms or the geological background) were the determinant factors. On the contrary, significant seasonal differences were not recorded. Statistical techniques, as the non-parametric Mann–Whitney U and Kruskal–Wallis tests and principal components analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were used for the evaluation of the results. These chemometric tools succeeded to discriminate the sampling points according to their distance from the cages or the origin of the sample. Variables' significance, correlations and potential accumulation sources were also investigated.

Neural networks are powerful tools that could explore the basic structure of environmental data. In this work, the most common artificial neural network (ANN) architectures, multi-layer perceptrons (MLPs), radial basis function (RBF) and Kohonen's self-organizing maps (SOM), are applied in order to assess the quality of the water reservoirs used for the domestic and industrial water supply of the city of Athens, Greece. In parallel, ANN models are optimized and their recognition and predictive accuracy is tested. The data set consisted of 89 samples collected from the three Athenian water reservoirs during a period of 6 months (October 2006 to April 2007). Thirteen metals and metalloids, Fe, B, Al, V, Cr, Mn, Ni, Cu, Zn, As, Cd, Ba, Pb, were determined. For the validation of the optimized ANN models, new data from subsequent sampling campaigns (December 2007) were used. The constructed classification models predicted successfully the origin of the new posterior samples and simultaneously revealed the differences in sample compositions that occurred in that period. Critical comparison of the different architectures in site classification and modeling verified the validity and usefulness of ANNs, as a powerful and effective tool for water quality assessment.

This work reports the utility of an iridium microwire plated in situ with a bismuth film for the simultaneous determination of Pb(II) and Cd(II) by square-wave anodic stripping voltammetry (SWASV). The experimental variables (concentration of the bismuth plating solution, preconcentration potential, accumulation time) were investigated. The limit of detection was 1 mu g?L-1 for Pb(II) and 1.5 mu g?L-1 for Cd(II) (at 300 s of preconcentration) and the % relative standard deviations were lower than 4.9?% and 5.5?%, respectively, at the 20 mu g?L-1 level (n=8). In addition, a study was made of coating the iridium-based bismuth-film microsensor with a film of Nafion for operation in the presence of surfactants. Finally, the electrode was applied to the determination of Pb(II) and Cd(II) in wastewater and tapwater samples.

This work reports the determination of bisphenol A (BPA) released from baby feeding bottles by adsorptive stripping voltammetry on a diphenylether carbon paste electrode (DPE-CPE). BPA was as accumulated on the surface of the DPE-CPE by an adsorptive/extractive mechanism at −0.20 V in B-R buffer at pH 7.0. Following pre-concentration, an anodic scan was applied in the range −0.20 V to +1.00 V during which BPA was oxidized and the oxidation peak current was related to the BPA concentration in the sample. The parameters related to both the preconcentration and stripping step were investigated. Using the selected conditions, the limit of detection for BPA was 7.8 × 10−9 mol L−1 at a preconcentration time of 240 s and the % relative standard deviation was 4.2% for 6.7 × 10−7 mol L−1 of BPA (n = 8). The proposed method was applied to the determination of BPA leaching from polycarbonate baby feeding bottles under simulated conditions of typical use. The results compared well with those obtained with liquid chromatography-tandem mass spectrometry (LC-MS/MS).

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration (expressed as carbon and bromine) with an ANN model, i.e., capturing non-linear relationships among the water quality variables.Thus, carbon and bromine concentrations in potable water (the second chosen due to the toxicity of brominated trihalomethanes, THMs) were predicted using artificial neural networks (ANNs) based mainly on multi-layer perceptrons (MLPs) architecture. The chlorination (detention) time as much as 58 hours in Athens distributed network, comprised the input variables to the ANNs models. Moreover, to develop an ANN model for estimating carbon and bromine, the available data set was partitioned into training, validation and test set. In order to reach an optimum amount of hidden layers or nodes, different architectures were tested. The quality of the ANN simulations was evaluated in terms of the error in the validation sample set for the proper interpretation of the results. The calculated sum-squared errors for training, validation and test set were 0.056, 0.039 and 0.060 respectively for the best model selected.Comparison of the results showed that a two-layer feed-forward back propagation ANN model could be used as an acceptable model for predicting carbon and bromine contained in potable water THMs.

In this review the history of chemistry and specifically the history and the significant steps of the evolution of analytical chemistry are presented. In chronological time spans, covering the ancient world, the middle ages, the period of the 19th century, and the three evolutional periods, from the verge of the 19th century to contemporary times, it is given information for the progress of chemistry and analytical chemistry. During this period, analytical chemistry moved gradually from its pure empirical nature to more rational scientific activities, transforming itself to an autonomous branch of chemistry and a separate discipline. It is also shown that analytical chemistry moved gradually from the status of exclusive serving the chemical science, towards serving, the environment, health, law, almost all areas of science and technology, and the overall society. Some recommendations are also directed to analytical chemistry educators concerning the indispensable nature of knowledge of classical analytical chemistry and the associated laboratory exercises and to analysts, in general, why it is important to use the chemical knowledge to make measurements on problems of everyday life.

Different ANNs models [Multi-layer Perceptrons (MLPs) and Radial Basis Function (RBF)] were developed and evaluated for the discrimination of olive oils produced in four Greek regions according to their geographical origin. For this purpose, ninety-seven samples were analyzed for 10 rare earth elements (REE) by ICP-MS. Moreover, two additional supervised techniques, discriminant analysis (DA) and classification trees (CTs), were applied to the same set for the data pre-treatment and for comparison purposes. In addition, two approaches were used for models' training and evaluation: the classical random choice of samples for the learning data set and an innovative one, which used the two linear discriminant functions (LDFs) of the preceding DA to choose the most representative learning sample set. The results were very satisfactory for the new ANNs classifiers. Over-fitting phenomena were overcome and the prediction ability was 73%, as evaluated by an independent test sample set. The results are encouraging for the ANNs efficiency even in demanding data bases, as the one under consideration. [Supplementary materials are available for this article. Go to the publisher's online edition of Analytical Letters for the following free supplemental resources: Additional figures and tables.]

The aim of the present study is to compare the application of unsupervised and supervised pattern recognition techniques for the quality assessment and classification of the reservoirs used as the source for the domestic and industrial water supply of the city of Athens, Greece. A new optimization strategy for sampling, monitoring, and water management is proposed. During the period of October 2006 to April 2007, 89 samples were collected from the three water reservoirs (Iliki, Mornos, and Marathon), and 13 parameters (metals and metalloids) were analytically determined. Generally, all the elements were found to fluctuate at very low levels, especially for Mornos that comprises the main water reservoir of Athens. Iliki and Marathon showed relatively elevated values, compared to Mornos, but below the legislative limits. Multivariate unsupervised statistical techniques, such as factor analysis/principal components analysis, and cluster analysis and supervised ones, like discriminant analysis and classification trees, were applied to the data set, and their classification abilities were compared. All the chemometric techniques successfully revealed the critical variables and described the similarities and dissimilarities among the sampling points, emphasizing the individual characteristics in every sample and revealing the sources of elements in the region. New data from posterior samplings (November and December 2007) were used for the validation of the supervised techniques. Finally, water management strategies were proposed concerning the sampling points and representative parameters.

The development of a vanadium pentoxide xerogel (VXG)-based sensor for the detection of volatile inorganic (ammonia) and organic (dimethylamine, etc.) amines is described. The xerogel film was deposited on interdigitated gold electrodes by dip-coating using an aqueous solution of VXG. The morphology of the sensing layer, its interaction with ammonia, which was used as a model analyte throughout this work, as well as the regeneration of the surface of the sensor electrodes with vapors of HCl were examined with scanning electron microscopy and FTIR spectroscopy. Signal changes, due to changes of the RC-product of the electrochemical cell (Au-VXG-Au), as a result of its interactions with ammonia vapors, were probed with a portable, homemade charge meter, the Multipulser. Exposing the sensor electrodes to various concentrations of ammonia vapors resulted in proportional changes in the signal output. Finally, the proposed sensors were successfully used for on-site, real-time monitoring of fish spoilage in ambient conditions.

A novel approach for the detection and determination of ozone is presented. The method is based on monitoring the pH increase due to the reaction of ozone with an unbuffered solution of KI, during which hydroxyl ions are produced. Based on theoretical calculations, a pH increase of 0.01 pH-units corresponds to 2.3 × 10−9 M of reacting concentration of ozone, when the initial pH of the KI solution is 7.00. Deviations from the ideal neutrality due to the presence of traces of acidic or basic impurities can be taken into account. A comparison between the expected pH changes and those experimentally achieved for given amounts of ozone has been made.

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.

This work aims at the comparative use of three different Artificial Neural Networks (ANNs) techniques for the water quality assessment and classification of the water reservoirs of Athens, Greece. During the period of October 2006 to April 2007, 89 samples were collected from the three lakes (Iliki, Mornos and Marathon) used as source for the domestic and industrial water supply of the city of Athens. The determination of 13 parameters (metals and metalloids) was carried out by ICP-MS and ETAAS. The most common network architectures, namely Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen’s self-organizations maps (SOM) were successfully applied to the data. Initially, using discriminant analysis, only three variables (V, Ni, As) were proved to be able to describe the intrinsic characteristics of the three lakes and used for the construction of the ANNs. Optimized models (through the Root Mean Square criterion of the validation sample set), describing these characteristics, were constructed and compared. Moreover, unsupervised Kohonen’s technique achieved samples’ visualized clustering. Targeting also in the prediction of source apportionment of unknown samples through robust models, newer samples were tested. Thus, from December 2007, water samples characterized only from the variables V, Ni and As were applied to the optimized ANNs models. The prediction accuracy concerning the classification of the “unknown” samples in the three lakes was impressive (92.8 % success for all the models). Thus, ANNs proved to be a powerful tool for water quality assessment, prediction and classification.

This work reports the photolithographic fabrication of disposable bismuth-film electrodes (BiFEs) using a thin-film deposition approach. The deposition of the bismuth layer was carried out by sputtering of metallic bismuth on a silicon substrate while the exact geometry of the BiFEs was produced by photolithography. The utility of these sensors was tested for the simultaneous trace determination of Cd(II) and Pb(II) by square wave anodic stripping voltammetry (SWASV). Using the selected conditions, the limits of detection were 0.5μgl−1 for Pb(II) and 1μgl−1 for Cd(II) at a preconcentration time of 4min. The interference caused by Cu(II) was alleviated by the addition of ferrocyanide in the sample solution. Finally, the proposed BiFEs were successfully applied to the determination of Cd and Pb in a phosphate fertilizer and a river water sample. These sensors offer wide scope for trace metal analysis in terms of mass-production of mercury-free disposable sensors with performance comparable to their mercury counterparts.

This work describes a novel type of bismuth electrode for stripping voltammetry based on coating a silicon substrate with a thin bismuth film by means of sputtering. The bismuth-based sensors were characterized by optical methods (scanning electron microscopy (SEM), atomic force microscopy (AFM) and X-ray diffraction (XRD)) and as well as by linear sweep voltammetry. Subsequently, the electrodes were tested for the detection of low concentrations of trace metals (Cd(II), Pb(II) and Ni(II)) by stripping voltammetry. Well-formed stripping peaks were observed for trace concentrations of the target analytes demonstrating “proof-of-principle” for these sensors. This type of electrochemical device, utilizing thin-film technology for the formation of the bismuth film, holds promise for future applications in trace metal analysis.

Common significance tests carried out using statistical software packages usually return to the user the probability p of type I error as the result. Based on p and the preset confidence level the user will decide on the acceptance or the rejection of the associated null hypothesis. Dixon's test (Q-test) is commonly used for the detection of an outlier within a set of N observations (typically: N=3-12). Q-test can only be applied by comparing the experimental value of the statistic Q with tabulated critical Q-values corresponding to some standard values of p. Hence, for a given value of Q and a number of observations, N, the user knows only the range and not the value of the associated probability p of type I error (erroneous rejection). This is due to the lack of explicit expressions of the form p=F(Q,N). In this work, a simple stochastic (Monte Carlo) approach is presented for the estimation of p corresponding to a given experimental value of Q and size N of the data set. In addition, based on Dixon's equations, explicit expressions of p are given for N=3 and 4.

A carbon paste electrode containing ruthenium(IV) oxide as a modifier was tested as an effective hydrogen peroxide amperometric sensor in bulk measurements (hydrodynamic amperometry). Factors that influence its overall analytical perform ance, such as pH and the applied potential, were examined. The RuO2-modified electrode displayed high sensitivity towards hydrogen peroxide, with detection limits as low as 0.02 mm at pH 7.4 and 0.007 mM at pH 9.0. The method was applied for monitoring the decomposition of hydrogen peroxide (by catalase) in phosphate buffer of pH 7.4. The relative response of the electrode towards ascorbic acid was assessed and it was found that the selectivity of the RuO2-modified electrode towards hydrogen peroxide over ascorbic acid could be significantly improved by electro-polymerizing m-phenylenediamine on its surface prior to measurements. The RuO2-modified electrode was used for the kinetic (fixed time) determination of catalase activity in the range of 4-40 U/mL (detection limit 1.2 U/mL). The method was applied to the determination of catalase-like activity in various plant materials (recov-ery ranged from 93 to 101%, detection limit 480 U/100 g).

In the present paper we report the use of a homemade electronic device (Multipulser), for monitoring interactions between biomolecules that may change the capacitance of an electrode. Multipulser can be used as a stand-alone, low-cost, yet effective alternative monitoring device instead of other well established commercial instruments. The operation of Multipulser is based on the integration of the electric charge used for the repetitive charging of the electrochemical cell capacitance after the application of a predetermined number of short-duration, low-amplitude voltage pulses (perturbation pulses). Multipulser was used to monitor the binding of biotinylated dextran on two different avidin modified electrode assemblies; one based on a thiol SAMs on gold and another based on Ti/TiO2 semiconductor. Measurements conducted in parallel with a commercial frequency response analyzer gave similar reaction patterns. Pulse polarity dependent behavior was revealed in the case of the Ti/TiO2-electrode assembly when bipolar potential perturbation modes were used with Multipulser.