Aitor Mimendia

A review of the use of the potentiometric electronic tongue in the monitoring of environmental systems

This paper introduces electronic tongue systems for remote environmental monitoring applications. This new approach in the chemical sensor field consists of the use of an array of non-specific sensors coupled with a multivariate calibration tool which may form a node of a sensor network. In our work, the proposed arrays were made up of potentiometric sensors based on polymeric membranes, and the subsequent cross-response processing was based on a multilayer artificial neural network model. Two cases are described: the environmental monitoring of ammonium pollutant plus alkaline ions at different measuring sites in the states of Mexico and Hidalgo (Mexico), and the monitoring of heavy metals (Cu^2^+, Pb^2^+, Zn^2^+ and Cd^2^+) in open air waste streams and rivers heading down the Gulf of Mexico.

Use of Sequential Injection Analysis to construct a Potentiometric Electronic Tongue: Application to the Multidetermination of Heavy Metals

An automated potentiometric electronic tongue (ET) was developed for the quantitative determination of heavy metal mixtures. The Sequential Injection Analysis (SIA) technique was used in order to automate the obtaining of input data, and the combined response was modeled by means of Artificial Neural Networks (ANN). The sensor array was formed by four sensors: two based on chalcogenide glasses Cd sensor and Cu sensor, and the rest on poly(vinyl chloride) membranes Pb sensor and Zn sensor. The Ion Selective Electrode (ISE) sensors were first characterized with respect to one and two analytes, by means of high‐dimensionality calibrations, thanks to the use of the automated flow system; this characterization enabled an interference study of great practical utility. To take profit of the dynamic nature of the sensor’s response, the kinetic profile of each sensor was compacted by Fast Fourier Transform (FFT) and the extracted coefficients were used as inputs for the ANN in the multidetermination applications. ...

SIA system employing the transient response from a potentiometric sensor array: Correction of a saline matrix effect

A Sequential Injection Analysis (SIA) system and an 8-potentiometric all-solid-state sensor array were coupled in a simple and automated electronic tongue device. The potentiometric sensors used were planar microfabricated structures with standard PVC membranes deposited onto a gold contact. The SIA system permitted the automated operation and generation of the calibration data, needed to build an Artificial Neural Network model, thanks to the precise dosing and mixing of volumes of stock solutions. The resolution of a four-ion mixture, i.e. ammonium, sodium, nitrate and chloride was the study case used for characterization of the system. Two different variants for signal acquisition, steady-state and transient recording, were arranged and compared. The dynamic treatment is shown to offer improved performance thanks to the benefits of the kinetic resolution. For this, it first extracts meaningful data from a FFT transform of each sensors transient, which is then fed to an ANN model for estimation of each concentration in the four-ion mixture. While in a standard laboratory situation there was no difference between the two approaches, the dynamic treatment allowed the correction of a matrix effect in the case study, where an uncontrolled saline effect could be counterbalanced.

Data Fusion from Voltammetric and Potentiometric Sensors to Build a Hybrid Electronic Tongue Applied in Classification of Beers

A hybrid electronic tongue based on data fusion of two different sensor families was built and used to recognize three types of beer. The employed sensor array was formed by three modified graphite‐epoxy voltammetric sensors plus six potentiometric sensors with cross‐sensitivity. The sensors array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Discriminant Factor Analysis (DFA), were trained to classify the data clusters related to different beer types. PCA was used to visualize the different categories of taste profiles and DFA with leave‐one‐out cross validation approach permitted the qualitative classification. According to the DFA model, 96% of beer samples were correctly classified. The aim of this work is to prove performance of hybrid electronic tongue systems by exploiting the new approach of data fusion of different sensor families, in comparison of electronic tongue with only one sensor type.

Two analyte calibrations from the transient response of a single potentiometric sensor employed with the SIA technique.

Simultaneous quantification of Cd(2+) and Pb(2+) in solution has been correctly targeted using the kinetic information from a single non-specific potentiometric sensor. Dual quantification was accomplished from the complex information in the transient response of an electrode used in a Sequential Injection Analysis (SIA) system and recorded after step injection of sample. Data was firstly preprocessed with the Discrete Wavelet Transform (DWT) to extract significant features and then fed into an Artificial Neural Network (ANN) for building the calibration model. DWT stage was optimized regarding the wavelet function and decomposition level, while the ANN stage was optimized on its structure. To simultaneously corroborate the effectiveness of the approach, two different potentiometric sensors were used as study case, one using a glass selective to Cd(2+) and another a PVC membrane selective to Pb(2+).

Monitoring of environmental systems using electronic tongues as sensor networks

This paper introduces Electronic Tongues (ETs) for remote environmental monitoring applications. ETs are bio-inspired systems that employ an array of sensors for analysis, recognition or identification in liquid media. In this work it will be used as node sensor network for monitoring of heavy metals (Cu2+, Pb2+, Zn2+ and Cd2+) in open air waste streams and rivers heading down the Gulf of Mexico. The proposed arrays were formed by potentiometric sensors based on polymeric membranes (PVC) and the subsequent cross-response processing was based on a multilayer Artificial Neural Network (ANN) model. Analytical measures were performed using a laboratory-made electronic system which includes data transmission by radiofrequency, in order to demonstrate system viability for automated remote applications.

Data fusion in electronic tongue for qualitative analysis of beers

This paper presents the development of an Electronic Tongue based on two different arrays of electrochemical sensors (i.e. potentiometric and voltammetric) for the identification of three styles of beer. Conventionally, electrochemical measurements contain hundreds of records and cannot be processed directly, due to its high data dimension. Therefore, information obtained from both sensor families was prepossessed in order to extract representative features and then fused to improve the classification ability regarding to the use of single sensor data. On the one hand, Discrete Wavelet Transform and statistical procedures were employed as feature extraction techniques. On the other hand, classification model was build using Linear Discriminant Analysis and validated by Leave-one-out cross-validation procedure. Final results demonstrate that the ET employing data fusion is able to distinguish 100% of the types of beer as well as its manufacturing process.

Discrimination of soils and assessment of some soil fertility parameters using an electronic tongue

In this communication, a new strategy to perform soil classification and/or characterization is reported, which is the coupling of chemical sensors with a pattern recognition method, what is known as an electronic tongue. Following this approach, the system proposed in this paper uses a sensor array formed by potentiometric sensors with generic cross response against several cations and anions, plus a pattern recognition method based on Artificial Neural Networks (ANNs); the sensor‐based system allows performing a simple laboratory procedure where the advanced data processing methodology permits to extract the meaningful information. In this way this work represents the first application and testing of an electronic tongue in soil analysis. Apart from the qualitative classification application, a quantitative analysis of certain chemical features related to soil fertility has also been attempted.

Two Analyte Calibration From The Transient Response Of Potentiometric Sensors Employed With The SIA Technique

Calibration models for multi‐analyte electronic tongues have been commonly built using a set of sensors, at least one per analyte under study. Complex signals recorded with these systems are formed by the sensors’ responses to the analytes of interest plus interferents, from which a multivariate response model is then developed. This work describes a data treatment method for the simultaneous quantification of two species in solution employing the signal from a single sensor. The approach used here takes advantage of the complex information recorded with one electrode’s transient after insertion of sample for building the calibration models for both analytes. The departure information from the electrode was firstly processed by discrete wavelet for transforming the signals to extract useful information and reduce its length, and then by artificial neural networks for fitting a model. Two different potentiometric sensors were used as study case for simultaneously corroborating the effectiveness of the appr...