Flow injection analysis applied to a voltammetric electronic tongue

Fabrication and application of three-dimensional nanocomposites modified electrodes for evaluating the aging process of Huangjiu (Chinese rice wine)

In this study, three modified glassy carbon electrodes based on three-dimensional conducting polymer nanocomposites (TDCPNs) were fabricated for evaluating the aging process of Huangjiu (Chinese rice wines). The electrochemical activity and experimental conditions of the TDCPNs modified electrodes were investigated by cyclic voltammetry, the aging information obtained by the modified electrodes were optimized by variance inflation factor (VIF). Principal components analysis (PCA), locally linear embedding (LLE), and locality preserving projection (LPP, which presented the best classification result) based on the optimized data were applied to classify the wine samples. Then, the dimensionality reduction data of PCA, LLE, and LPP were used as input variables of the logistic regression and extreme learning machine (ELM) for evaluating the aging process of Huangjiu, and the LLE-ELM method exhibited the best prediction results. These results demonstrated that the TDCPNs modified electrodes presented the potential for the quality analysis of food and beverages.

Recent advances in electronic tongues

This minireview describes the main developments of electronic tongues (e-tongues) and taste sensors in recent years, with a summary of the principles of detection and materials used in the sensing units. E-tongues are sensor arrays capable of distinguishing very similar liquids employing the concept of global selectivity, where the difference in the electrical response of different materials serves as a fingerprint for the analysed sample. They have been widely used for the analysis of wines, fruit juices, coffee, milk and beverages, in addition to the detection of trace amounts of impurities or pollutants in waters. Among the various principles of detection, electrochemical measurements and impedance spectroscopy are the most prominent. With regard to the materials for the sensing units, in most cases use is made of ultrathin films produced in a layer-by-layer fashion to yield higher sensitivity with the advantage of control of the film molecular architecture. The concept of e-tongues has been extended to biosensing by using sensing units capable of molecular recognition, as in films with immobilized antigens or enzymes with specific recognition for clinical diagnosis. Because the identification of samples is basically a classification task, there has been a trend to use artificial intelligence and information visualization methods to enhance the performance of e-tongues.

Electronic integrated multisensor tongue applied to grape juice and wine analysis

An integrated multisensor composed by six ISFET devices selective to common ions and heavy metals combined with a flow injection analysis (FIA) system has been applied as an electronic tongue to grape juice and wine sample analysis. The data obtained for several grape and wine variety samples analysis have been treated using multiparametric tools like principal component analysis (PCA) and soft independent modelling class analogy technique (SIMCA) for the patterning recognition and classification of samples and partial least squares (PLS) regression for quantification of several parameters of interest in wine production. The results obtained have demonstrated the potential of using those multisensors as electronic tongues not only for distinguishing the samples according to the grape variety and the vintage year but also for quantitative prediction of several sample parameters.

Sensor arrays for liquid sensing--electronic tongue systems

Electronic tongue systems are multisensor devices dedicated to automatic analysis of complicated composition samples and to the recognition of their characteristic properties. Recently, the number of publications covering this topic has significantly increased. Many possible architectures of such devices were proposed: potentiometric, voltammetric, as well as approaches embracing mass- and optical-sensors. For the analysis of sensor array data, various pattern recognition systems were proposed. All of these topics are summarized in this review. Moreover, additional problems are considered: miniaturization of electronic tongues and hybrid systems for liquid sensing.

Electronic tongues in flow analysis

This review presents the evolution of recent flow-based analytical systems, characterized by the use of arrays of sensors as a detection scheme. For the proper processing of the complex responses generated, the systems require the use of advanced chemometric treatment, in which received the term "electronic tongue". Applications employing the flow injection analysis (FIA) and sequential injection analysis (SIA) are described. Chronologically, the progresses made by different research groups are shown, emphasizing their final applications in real problem solving.

Chemometric exploration of an amperometric biosensor array for fast determination of wastewater quality

Four wastewater samples of different treatment qualities; untreated, alarm, alert and normal, from a Swedish chemi-thermo-mechanical pulp mill and pure water were investigated using an amperometric bio-electronic tongue in a batch cell. The aim was to explore enzymatically modified screen-printed amperometric sensors for the discrimination of wastewater quality and to counteract the inherent drift. Seven out of eight platinum electrodes on the array were modified with four different enzymes; tyrosinase, horseradish peroxidase, acetyl cholinesterase and butyryl cholinesterase. At a constant potential the current intensity on each sensor was measured for 200s, 100s before injection and 100s after injection of the sample. The dynamic biosensor response curves from the eight sensors were used for principal component analysis (PCA). A simple baseline and sensitivity correction equivalent to multiplicative drift correction (MDC), using steady state intensities of reference sample (catechol) recordings, was employed. A clear pattern emerged in perfect agreement with prior knowledge of the samples explaining 97% of the variation in the data by two principal components (PCs). The first PC described the treatment quality of the samples and the second PC described the difference between treated and untreated samples. Horseradish peroxidase and pure platinum sensors were found to be the determinant sensors, while the rest did not contribute much to the discrimination. The wastewater samples were characterized by the chemical oxygen demand (COD), biological oxygen demand (BOD), total organic carbon (TOC), inhibition of nitrification, inhibition of respiration and toxicity towards Vibrio fischeri using Microtox, the freshwater alga Pseudokirchneriella subcapita and the freshwater crustacean Daphnia magna.

Simultaneous determination of phenolic compounds by means of an automated voltammetric “electronic tongue”

This contribution describes the simultaneous determination of three phenolic compounds, o-cresol, p-chlorophenol and 4-chloro-3-methylphenol, using direct oxidation and amperometric detection coupled by signal deconvolution, accomplished via chemometric methods. Direct oxidation of phenolic compounds is performed at the surface of an epoxy-graphite transducer, by linear scan voltammetry. Due to strong signal overlapping, artificial neural networks (ANNs) were used during data treatment, in a combination of chemometrics and electrochemical sensors known as an "electronic tongue". To calibrate this system properly, a total of 80 mixed samples were prepared automatically by employing a sequential injection analysis (SIA) system designed to automatically generate the information needed to train the network. The phenolic compound concentration varied from 1 to 70 microM for o-cresol, from 0.5 microM to 140 microM for p-chlorophenol and from 1 microM to 100 microM for 4-chloro-3-methylphenol. A good prediction capability was obtained, with correlation coefficients >0.964 when the obtained values were compared with those expected for a set of 24 external test samples not used for training. The results presented here indicate that this technique is a simple and robust analytical method of environmental interest.