Manuel del Valle

Application of a potentiometric electronic tongue as a classification tool in food analysis

This paper reports on the application of a potentiometric sensor array to the food analysis field, in order to distinguish simple tastes and to classify food samples. This array is formed by a set of non-specific all-solid-state potentiometric sensors and has been used in combination with principal component analysis (PCA) for the classification of food samples in batch and in flow injection mode. First attempt was to classify synthetic samples prepared with controlled variability. Once this ability is proven, satisfactory classification results are presented for commercial waters, orange-based drinks and tea samples. An interesting correlation is achieved between the natural juice content and its first calculated component, which allows for a very simple tool for screening purposes.

Sequential injection system with higher dimensional electrochemical sensor signals Part 2. Potentiometric e-tongue for the determination of alkaline ions.

An intelligent, automatic system based on an array of non-specific-response chemical sensors was developed. As a great amount of information is required for its correct modelling, we propose a system generating it itself. The sequential injection analysis (SIA) technique was chosen as it enables the processes of training, calibration, validation and operation to be automated simply. Detection was carried out using an array of potentiometric sensors based on PVC membranes of different selectivity. The diluted standard solutions needed for system learning and response modelling are automatically prepared from more concentrated standards. The electrodes used were characterised with respect to one and two analytes, by means of high-dimensionality calibrations, and the response surface of each was represented; this characterisation enabled an interference study of great practical utility. The combined response was modelled by means of artificial neural networks (ANNs), and thus it was possible to obtain an automated electronic tongue based on SIA. In order to identify the ANN which provided the best model of the electrode responses, some of the networks parameters were optimised and its usefulness in determining NH(4)(+), K(+) and Na(+) ions in synthetic samples was then tested. Finally, it was used to determine these ions in commercial fertilisers, the obtained results being compared with reference methods.

Determination of Ammonium Ion Employing an Electronic Tongue Based on Potentiometric Sensors

Abstract A method for determining ammonium ion concentration from complex aqueous samples is presented in this work. It does not need to eliminate chemical interferences, mainly sodium and potassium, because an array of potentiometric sensors with intrinsic responses is used. The measurements taken from the array are processed with a multicomponent data treatment. This approach is already known as electronic tongue. Multivariable calibration was implemented with an artificial neural network, trained under the rules of the Bayesian regularization. The developed system has been applied to water samples from rivers and wastewaters with ammonium content in the range 1 × 10−4–5 × 10−2 mol L−1. Results are similar to those obtained with other reference methods.

Determination of trace levels of anionic surfactants in river water and wastewater by a flow injection analysis system with on-line preconcentration and potentiometric detection.

The authors present an automated flow injection analysis (FIA) system for the determination of low levels of anionic surfactants in river water and wastewater. The system uses especially constructed tubular flow-through ion-selective electrodes (ISEs) as potentiometric sensors and on-line preconcentration techniques. The anionic surfactant ISEs employed are of the all-solid-state type with a plasticized PVC membrane. They show a general response to anionic surfactants with a lower limit of linear response of ∼10(-)(5) M, when used in direct determinations. However, their specificity is limited, which hampers their direct use with environmental samples. Therefore, the FIA system presented here includes a solid-phase extraction procedure for purification and preconcentration of analytes. Breakthrough curves were constructed to characterize different sorbents and different eluents were tested to optimize the preconcentration process. The FIA system was first applied to the determination of different types of anionic surfactant standards. Potentially interfering substances such as chloride, nitrate, and nonionic surfactants were checked to verify that they did not interfere on the response of the system. Concentrations of ∼10(-)(7) M (0.03 ppm) of sodium dodecyl sulfate could be detected in the nonlinear response region when 3 mL of sample was preconcentrated and eluted with 50 μL of a 75% acetonitrile/water (v/v) solution. Precision was 2% RSD (n = 31) for a 1 × 10(-)(6) M sodium dodecyl sulfate standard solution and the sample throughput was 10 h(-)(1). The FIA system was then used for the determination of total anionic surfactants in river water and wastewater.

Data Compression for a Voltammetric Electronic Tongue Modelled with Artificial Neural Networks

Abstract In the study of voltammetric electronic tongues, a key point is the preprocessing of the departure information, the voltammograms which form the response of the sensor array, prior to classification or modeling with advanced chemometric tools. This work demonstrates the use of the discrete wavelet transform (DWT) for compacting these voltammograms prior to modeling. After compression, a system based on artificial neural networks (ANNs) was used for the quantification of the electroactive substances present, using the obtained wavelet decomposition coefficients as their inputs. The Daubechies wavelet of fourth order permitted an effective compression up to 16 coefficients, reducing the original dimension by ca. 10 times. The case studied is a mixture of three oxidizable amino acids:tryptophan, cysteine, and tyrosine. With the reduced information, one ANN per specie was trained using the Bayesian regularization algorithm. The proposed procedure was compared with the more conventional treatments of downsampling the voltammogram, or its feature extraction employing principal component analysis prior to ANNs.

Application of an all-solid-state ion-selective electrode for the automated titration of anionic surfactants

A poly(vinyl chloride) matrix membrane electrode responsive to anionic surfactants in an all-solid-state graphite–epoxy support was applied to the determination of anionic surfactants by potentiometric titration, using Hyamine 1622 as the titrant, in a pH 2.2 phosphate buffer. The results from this method compare favourably with those of the two-phase mixed indicator titration method for several commercial anionic surfactants (alkylsulfates, alkylbenzenesulfonates, α-alkene sulfonates, alkyl ether sulfates and sulfosuccinates). The performance characteristics improve the commercially available surfactant electrodes: the standard deviation for the titration of 4 mmol dm–3 dodecylsulfate with 4 mmol dm–3 titrant is 0.15%(n= 23), the relative standard deviation of the end-point potential is 0.95% and the average value of the potential jump is 140 mV, which allows for the titration of surfactants at concentrations down to 10 µmol dm–3 and shows its applicability in routine analysis.

Wavelet neural networks to resolve the overlapping signal in the voltammetric determination of phenolic compounds

Three phenolic compounds, i.e. phenol, catechol and 4-acetamidophenol, were simultaneously determined by voltammetric detection of its oxidation reaction at the surface of an epoxy-graphite transducer. Because of strong signal overlapping, Wavelet Neural Networks (WNN) were used in data treatment, in a combination of chemometrics and electrochemical sensors, already known as the electronic tongue concept. To facilitate calibration, a set of samples (concentration of each phenol ranging from 0.25 to 2.5mM) was prepared automatically by employing a Sequential Injection System. Phenolic compounds could be resolved with good prediction ability, showing correlation coefficients greater than 0.929 when the obtained values were compared with those expected for a set of samples not employed for training.

Nutrient Solution Monitoring in Greenhouse Cultivation Employing a Potentiometric Electronic Tongue

This work investigates the use of electronic tongues for monitoring nutrient solution compositions in closed soilless systems. This is a horticultural technique in which the nutrient solution is continuously recirculated and an automatic recomposition system maintains the concentration of the different ions in the optimum range for the plants. Electronic tongues used in this study comprised an array of potentiometric sensors and complex data processing by artificial neural networks. A first experiment was able to carry out the simultaneous inline monitoring of ammonium, potassium, sodium, chloride, and nitrate ions during the winter. In the second and third applications, done during summer, some changes were introduced in the sensor array to improve its response toward chloride ions and to incorporate phosphate in the model. This electronic tongue was validated with real greenhouse samples and was also able to detect the variations in the ion concentrations caused by an incorrect configuration of the recomposition system.

A New Potentiometric Photocurable Membrane Selectiveto Anionic Surfactants

The preparation of photocurable polymer membranes selective to anionic surfactants and their application to all-solid state ion-selective electrodes are reported. A preliminary trial employing CHEMFET devices is also shown. Membranes are based on a urethane-acrylate polymer, and use 2-cyanophenyl octyl ether (CPOE), a plasticizer compatible with the photocuring process. These membranes are highly selective to the anionic surfactants assayed while common inorganic anions did not interfere. An optimization methodology is proposed for their formulation, suited to the final application. Several membrane compositions were obtained, using between 44 and 48 % (w/w) CPOE. A membrane with a general-purpose formulation is fully characterized, and we show calibration results for anionic surfactants such as dodecylbenzenesulfonate (DBS−), tetrapropylenebenzenesulfonate and dodecylsulfate, or cationic surfactants such as Hyamine 1622 and cetyltrimethylammonium ion. With the primary ion DBS− we verified a 58.1 mV/dec sensitivity, a linear response between 1×10−3 M and 3×10−6 M, a detection limit corresponding to 0.26 ppm DBS− (7.9×10−7 M) and a slope precision of 2.8 % RSD between days.

Spectrophotometric determination of low levels of anionic surfactants in water by solvent extraction in a flow injection system

An approach to automation, in routine control, for the determination of anionic surfactants in river water and treatment plant water in the concentration range 0.04–3.5 µg ml–1 using a continuous solvent extraction process with flow injection analysis, is described. The method is based on an ion-pair extraction reaction with Methylene Blue in chloroform. The separation of both phases is accomplished by means of a membrane phase separator and detection is based on injection of the coloured organic phase into a chloroform stream which passes to the detector. Methanol was added to the organic phase in order to enhance the extraction process and to ensure efficient extraction of the various anionic surfactants. A number of anionic species and non-ionic surfactants have been tested as possible interferents. The results obtained for samples of river water show good agreement with those obtained with the Methylene Blue batch method.