No abstract

Amperometric acetylcholinesterase biosensors have been developed for quantification of the pesticides carbofuran, carbaryl, methylparaoxon, and dichlorvos in phosphate buffer containing 5% acetonitrile. Three different biosensors were built using three different acetylcholinesterase (AChE) enzymes-AChE from electric eel, and genetically engineered (B394) and wild-type (B1) AChE from Drosophila melanogaster. Enzymes were immobilized on cobalt(II) phthalocyanine-modified electrodes by entrapment in a photocrosslinkable polymer (PVA-AWP). Each biosensor was tested against the four pesticides. Good operational stability, immobilisation reproducibility, and storage stability were obtained for each biosensor. The best detection limits were obtained with the B394 enzyme for dichlorvos and methylparaoxon (9.6 x 10(-11) and 2.7 x 10(-9) mol L(-1), respectively), the B1 enzyme for carbofuran (4.5 x 10(-9) mol L(-1)), and both the B1 enzyme and the AChE from electric eel for carbaryl (1.6 x 10(-7) mol L(-1)). Finally, the biosensors were used for the direct detection of the pesticides in spiked apple samples.

No abstract

Intelligent and automatic systems based on arrays of non-specific-response chemical sensors were recently developed in our laboratory. For multidetermination applications, the normal choice is an array of potentiometric sensors to generate the signal, and an artificial neural network (ANN) correctly trained to obtain the calibration model. As a great amount of information is required for the proper modelling, we proposed its automated generation by using the sequential injection analysis (SIA) technique. First signals used were steady-state: the equilibrium signal after a step-change in concentration. We have now adapted our procedures to record the transient response corresponding to a sample step. The novelty in this approach is therefore the use of the dynamic components of the signal in order to better discriminate or differentiate a sample. In the developed electronic tongue systems, detection is carried out by using a sensor array formed by five potentiometric sensors based on PVC membranes. For the developed application we employed two different chloride-selective sensors, two nitrate-selective sensors and one generic response sensor. As the amount of raw data (fivefold recordings corresponding to the five sensors) is excessive for an ANN, some feature extraction step prior to the modelling was needed. In order to attain substantial data reduction and noise filtering, the data obtained were fitted with orthonormal Legendre polynomials. In this case, a third-degree Legendre polynomial was shown to be sufficient to fit the data. The coefficients of these polynomials were the input information fed into the ANN used to model the concentrations of the determined species (Cl-, NO3- and HCO3-). Best results were obtained by using a backpropagation neural network trained with the Bayesian regularisation algorithm; the net had a single hidden layer containing three neurons with the tansig transfer function. The results obtained from the time-dependent response were compared with those obtained from steady-state conditions, showing the former superior performance. Finally, the method was applied for determining anions in synthetic samples and real water samples, where a satisfactory comparison was also achieved.