Voltammetric Electronic Tongues Research Articles

Comparison of various data analysis techniques applied for the classification of oligopeptides and amino acids by voltammetric electronic tongue

Abstract This work reports a critical evaluation of performance of various data analysis techniques applied to the classification of biosamples using voltammetric electronic tongue. Data obtained by sensor array of carbon-paste electrodes were processed by 3 pattern recognition methods: Partial Least Squares Discriminant Analysis (PLS-DA) as the “golden standard” in electronic tongue data analysis, linear Soft Independent Modelling of Class Analogy (SIMCA) and non-linear Support Vector Machine Discriminant Analysis (SVM-DA), the two latter being less commonly used. Due to high dimensionality of the data, various preprocessing methods (autoscaling, Standard Normal Variable – SNV, Discrete Wavelet Transform – DWT) were tested for each kind of these techniques to find most suitable workflow allowing to obtain satisfactory performance. Totally 1026 models were developed and tested. In order to compare their ability to estimate class affinity of the studied biosamples, 4 performance parameters: accuracy, sensitivity, precision, and specificity, were calculated for every model for both train and test sets to obtain reliable and repeatable results. It must be underlined, that some general remarks and findings that had been concluded during classification of oligopeptides, were later additionally validated using different dataset (comprising oligopeptides and amino acids). The treatment used confirmed that the presented recommendations can be generalized and adapted to other classification tasks by means of voltammetric electronic tongues.

Voltammetric Electronic Tongues in Food Analysis.

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

A new multivariate standard addition strategy for stripping voltammetric electronic tongues: Application to the determination of Tl(I) and In(III) in samples with complex matrices

A new multivariate standard addition strategy applicable to stripping methods was proposed as an extention of the classical univariate standard addition method for the resolution of complex samples involving overlapped peaks and complex matrices. The proposed strategy consists in alternate additions of the considered analytes and the further extrapolation to a simulated blank solution measured by skipping the preconcentration step (deposition time = 0). This calibration approach was successfully tested in tonic water samples spiked with Tl(I) and In(III) using a sensor array based on a SeCyst-SPCNFE and an ex-situ-BiSPCE, providing good concordance between replicates and much better accuracy than the usual multivariate external calibration method.

Multisensor systems based on phthalocyanines for monitoring the quality of grapes

Arrays of phthalocyanine-based sensors with complementary activity have been used to develop voltammetric electronic tongues. Such systems have demonstrated to be useful in enology for the evaluation of quality of wines in different production stages, from grapes to bottles. In this paper, the state of the art of multisensor systems based on phthalocyanines dedicated to the analysis of musts (juices obtained from crushed grapes) is described. Such multisensor systems cover different types of sensors from simple Carbon Paste Electrodes, to sophiticated nanostructured sensors, including Langmuir–Blodgett or Layer by Layer thin films and biomimetic biosensors where phthalocyanines play a crucial role as electron mediator between enzymes and electrodes. In all cases, multisensor systems based on phthalocyanines have been able to discriminate musts prepared from different varieties of grapes. The performance of these systems can be improved by combining non-specific sensors with biosensors containing enzymes selective to phenols. In this case, excellent relationships have been found between the responses provided by the array and the content in phenols and acids provided by traditional chemical analysis.

Analysis of Amino Acid Mixtures by Voltammetric Electronic Tongues and Artificial Neural Networks

AbstractA new voltammetric electronic tongue formed with graphite‐epoxy composite electrodes which were modified with metal‐oxide nanoparticles is presented for the quantification of tryptophan, tyrosine and cysteine aminoacid mixtures. The signals were obtained by cyclic voltammetry, and data was processed using two different chemometric techniques, artificial neural networks and partial least squares regression, for comparison purposes. Before performing artificial neural networks data was compressed by fast Fourier transform or discrete wavelet transform. The best results were attained using artificial neural networks with previous fast Fourier transform compression of the data with a normalized root‐mean‐square error of 0.032 (n=15) for the external test subset. The present method shows results comparable to other similar approaches, but with a much easier sampling process for the training set and new electrode modifiers to form the voltammetric sensors.

On the application of simple matrix methods for electronic tongue data processing: Case study with black tea samples

The paper is devoted to the application of several simple mathematical methods that can be used for analysis of multisensor system data. RV and modified RV′ matrix correlation coefficients, Tucker's congruency coefficients and canonical correlation analysis can be effectively employed to reveal the similarity degree shared between two different data sets obtained for the same samples. This approach can be particularly useful when the number of available samples is too small to construct and validate quantitative regression models, but still the researcher is interested in getting some numerical estimate of the data quality to judge on electronic tongue applicability for particular analytical tasks. As a case study for this work we analyzed the data from potentiometric and voltammetric electronic tongues acquired during the analysis of black tea samples. These samples were also assessed with standard physicochemical methods and professional sensory panel. The relationships between these four data sets were studied with various matrix correlation techniques.

Comparison of methods for the processing of voltammetric electronic tongues data

We are making a numerical comparison of various preprocessing strategies for dealing with data from voltammetric electronic tongues in order to reduce the high dimensionality of the response matrices. Different modelling tools are presented and briefly described. We then compare combinations of four preprocessing strategies (principal component analysis, fast Fourier transform, discrete wavelet transform, voltammogram-windowed slicing integral) with four modelling alternatives (principal component regression, partial least squares regression, multi-way partial least squares regression, artificial neural networks) by employing data from a voltammetric bioelectronic tongue, an array formed by enzyme-modified biosensors and applied to the discrimination and quantification of phenolic compounds.

Sinusoidal envelope voltammetry as a new readout technique for electronic tongues

We introduce a novel voltammetric method, so-called sinusoidal envelope voltammetry, for use in electronic tongues. Fourier transformation was used to transform the data of the signal from the time domain to the frequency domain. The four taste substances, acesulfame potassium, monosodium glutamate, potassium chloride and tartaric acid, are shown to exhibit abundant frequency characteristics in the power spectrum of a Fourier transformation. This indicates that the power spectrum from sinusoidal envelope voltammetry can be used as fingerprints of samples for classification. Principal component analysis along with discrimination index and multi-frequency large amplitude pulse voltammetry as a reference technique is used to evaluate the separation ability of sinusoidal envelope voltammetry. The score plots of the method for the four taste substances and for the five brands of Jiafan rice wine show better discrimination ability than multi-frequency large amplitude pulse voltammetry. Sinusoidal envelope voltammetry is considered to be a promising technique for use in voltammetric electronic tongues.

A fast graphical similarity algorithm for pattern recognition for data from a voltammetric electronic tongue

Multi-sensor arrays have been applied more and more extensively in various fields, especially in the electrochemical industry. Among them, cross-sensitive multi-sensor arrays (e.g. electronic tongues and electronic noses) are the most popular ones. Taking advantage of the cross-sensitive response of voltammetric electronic tongues, a unique fast approach for variable reduction and pattern recognition is proposed in this paper based on a set of well-designed graphical similarity principles. It translates the measurement data of electronic tongues into a net graph containing rich pattern information, and then compares the similarity between the graphs on the levels of topology, geometric shape, and geometric size respectively and finally gives the corresponding similarity distances between the samples for pattern recognition.

A method of pulse array design for voltammetric electronic tongues

This paper proposes a reasonable methodology for the design of pulse arrays applied in classification and quantification techniques based on the use of pulse voltammetric electronic tongues. As model problem we designed a procedure to predict levels of ferrocene, TCNQ and Ru(bipy) 3 2+ using a voltammetric electronic tongue employing a set of noble electrodes; i.e. Au, Pt, Ir and Rh. Ferrocene, TCNQ and Ru(bipy) 3 2+ have been selected as model compounds because these are molecules with well defined redox processes. The electrodes were housed inside a stainless steel cylinder which was used as the body of the electronic tongue system. A SCE was used as reference electrode. An electrochemical study of the response of ferrocene, TCNQ and Ru(bipy) 3 2+ was carried out as a first step with the aim of characterise the redox processes in these compounds. Different samples containing ferrocene, TCNQ and Ru(bipy) 3 2+ were prepared following an experimental design in which a system of three compounds/three levels was established. Multivariate analysis based on Partial Least Square (PLS) techniques was applied for data management and prediction models building. Several set of pulses were tested and a general procedure to their design for their use in voltammetric electronic tongues is discussed and proposed.

State of the Art in the Field of Electronic and Bioelectronic Tongues – Towards the Analysis of Wines

AbstractThis review compares various types of (bio)electronic tongues. The design and principles of potentiometric and voltammetric electronic tongues are discussed together with applications in food and environmental analysis. Different approaches towards bioelectronic tongue are presented. Several methods for evaluation and interpretation of the measured data are described. Finally, the potential of such devices for analysis of wine is discussed.

Fabrication of disposable voltammetric electronic tongues by using Prussian Blue films electrodeposited onto CD-R gold surfaces and recognition of milk adulteration

A new approach to fabricate a disposable electronic tongue is reported. The fabrication of the disposable sensor aimed the integration of all electrodes necessary for measurement in the same device. The disposable device was constructed with gold CD-R and copper sheets substrates and the sensing elements were gold, copper and a gold surface modified with a layer of Prussian Blue. The relative standard deviation for signals obtained from 20 different disposable gold and 10 different disposable copper electrodes was below 3.5%. The performance, electrode materials and the capability of the device to differentiate samples were evaluated for taste substances model, milk with different pasteurization processes (homogenized/pasteurized, ultra high temperature (UHT) pasteurized and UHT pasteurized with low fat content) and adulterated with hydrogen peroxide. In all analysed cases, a good separation between different samples was noticed in the score plots obtained from the principal component analysis (PCA).

Voltammetric electronic tongues – basic principles and applications

This review discusses basic principles and applications of voltammetric electronic tongues. It is introduced by a description of the concept of electronic tongues or taste sensors followed by a general overview of electrochemical measurement principles that have been used for electronic tongues. A special emphasis is given on measurement principles for voltammetric electronic tongues, also including pulse voltammetry and variable reduction. Applications of voltammetric electronic tongue are described, such as in the food industry, environmental analysis, paper and pulp industry, household appliances and agriculture. Future developments of the concept, such as self polishing or miniaturized devices are also described. Finally, a continuous measurement system for chemical oxygen demand (COD), which has been commercialized, is depicted.

Multivariate calibration model from overlapping voltammetric signals employing wavelet neural networks

This work presents the use of a Wavelet Neural Network (WNN) to build the model for multianalyte quantification in an overlapped-signal voltammetric application. The Wavelet Neural Network is implemented with a feedforward multilayer perceptron architecture, in which the activation function in hidden layer neurons is substituted for the first derivative of a Gaussian function, used as a mother wavelet. The neural network is trained using a backpropagation algorithm, and the connection weights along with the network parameters are adjusted during this process. The principle is applied to the simultaneous quantification of three oxidizable compounds namely ascorbic acid, 4-aminophenol and paracetamol, that present overlapping voltammograms. The theory supporting this tool is presented and the results are compared to the more classical tool that uses the wavelet transform for feature extraction and an artificial neural network for modeling; results are of special interest in the work with voltammetric electronic tongues.

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

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.

An electronic tongue in the dairy industry

The concept of electronic tongues or taste sensors has developed rapidly during recent years due to their large potential. They are based on electrochemical sensors combined with multivariate data analysis. Voltammetric electronic tongues have proven valuable in many applications. Due to their ruggedness and simplicity, they have been found especially suitable for on-line monitoring of industrial processes. A voltammetric electronic tongue, specially designed for use in the dairy industry is described. It consisted of four working electrodes (gold, platinum, rhodium and stainless steel), embedded in PEEK™. It was mounted in a housing of stainless steel, which was inserted in the process line for direct on-line measurements. The voltammetric electronic tongue was used to follow different sources of milk coming into the process and to monitor the cleaning process.