Development of an electronic tongue based on a PEDOT-modified voltammetric sensor

Three different electrodes were tested for use as nonspecific amperometric sensors for blind analysis on real matrices, namely different fruit juices from different fruits or different brands. The first two electrodes were traditional Pt and Au electrodes, while the third one was modified with poly(3,4-ethylenedioxythiophene) conducting polymer. The sensors were tested separately, tested coupled to each other, and also tested together. The responses of the electrode system(s) were first screened via PCA and then their discriminant capabilities were quantified in terms of the sensitivities and specificities of their corresponding PLS-DA multivariate classification models. Particular attention was paid to analyzing the evolution of the response over subsequent potential sweeps. The modified electrode demonstrated the most discriminating ability, and it was the only system capable of satisfactorily performing the most complex task attempted during the analysis: discriminating between juices from the same fruit but from different brands. Moreover, the electrode "cleaning" procedure required between two subsequent potential sweeps was much simpler for the modified electrode than for the others. This electrode system was therefore shown to be a good candidate for use as an informative element in an electronic tongue applied to the analysis of other food matrices.     

Flow injection analysis applied to a voltammetric electronic tongue

A measurement system, based on flow injection analysis (FIA) technique applied to a voltammetric electronic tongue is described. A reference solution was thus continuously pumped through a cell with a voltammetric electronic tongue, and test samples were injected into the flow stream. Responses were obtained by measuring the resulting pulse height. The FIA technique offered several advantages, since relative measurements are performed, the system is less influenced by sensor baseline drift, calibration samples and/or washing solutions can be injected within a measurement series, and the system is well adapted for automatization. The system was used to analyze standard solutions of H₂O₂, KCl, CuNO₃, K₄[Fe(CN)₆], K₃[Fe(CN)₆] and NaCl, and results obtained were treated with multivariate data analysis. Principal component analysis performed showed that electrode drift could be considerably decreased, and the set-up was also used for classification of different apple juices.     

Multicomponent analysis of drinking water by a voltammetric electronic tongue

A voltammetric electronic tongue is described that was used for multicomponent analysis of drinking water. Measurements were performed on drinking water from a tap and injections of the compounds NaCl, NaN₃, NaHSO₃, ascorbic acid, NaOCl and yeast suspensions could be identified by use of principal component analysis (PCA). A model based on partial least square (PLS) was developed for the simultaneously prediction of identification and concentration of the compounds NaCl, NaHSO₃ and NaOCl. By utilizing this type of non-selective sensor technique for water quality surveillance, it will be feasible to detect a plurality of events without the need of a specific sensor for each type of event.     

Use of a voltammetric electronic tongue for predicting levels of nerve agent mimics

An electronic tongue (ET) based on pulse voltammetry containing a set of eight metallic electrodes (Au, Pt, Ir, Rh, Cu, Co, Ni and Ag) encapsulated on a stainless steel cylinder has been used to discriminate and predict levels of nerve agent mimics in aqueous environments. Analysis including principal component analysis (PCA) and partial least square techniques (PLS) have been applied for data management and prediction models. A good discrimination from other organophosphorous derivatives was found for the nerve agent simulants diethyl-chlorophosphate (DCP) and diethyl-cyanophosphate (DCNP).     

Multivariate optimisation of electrochemically pre-treated electrodes used in a voltammetric electronic tongue

The use of experimental design as a tool to optimise electrochemically cleaned electrodes applied in a voltammetric electronic tongue is described. A simple and quick activation of electrode surfaces is essential for this type of device, especially for on-line applications in industrial processes. The electronic tongue consisted of four metal electrodes, e.g. Au, Ir, Pt, and Rh in a three-electrode configuration. Current was measured as a function of large potential pulses of decreasing amplitude applied to each electrode. Preliminary results showed that electrochemical cleaning activated the electrode surfaces to similar extent as polishing. Settings of potential and time for each electrode was determined with experimental design in a solution containing 1.0 mM K₄[Fe(CN)₆] in 0.1 M phosphate buffer (pH 6.8). Electrode surfaces were deactivated in-between measurements in a complex liquid, like tea. Optimal settings for potential and time in the electrochemical cleaning procedure at each electrode were chosen at recoveries of 100% (compared to polished electrodes). The recoveries were larger than 100% when too large potentials and times were applied. This could be explained by the fact that the electrode areas increased and therefore also the current responses. Principal component analysis (PCA) was used to investigate the stability of the electrode settings at 100% recoveries. No obvious trends of drift in the signals were found.     

Determination of detergents in washing machine wastewater with a voltammetric electronic tongue

A voltammetric electronic tongue (ET) and a conductivity meter were used to predict amounts of detergents in process water from washing machines. The amount of detergent in over sixty samples was also determined by a HPLC reference method. Prediction was more accurate for the electronic tongue, but both techniques could be used. The composition of the detergent, e.g. supporting electrolyte, is an important factor for the ability to predict the detergent quantity by conductivity. Also two different surfactants, alkyl benzyl sulfonate (ABS) and etoxylated fatty alcohol (EOA), were fingerprinted by the HPLC. Their behaviour during the wash cycle differs from each other, ABS rinses away in the same proportions as the supporting electrolyte, but EOA appears to stay within the machine and laundry. Prediction models for ABS are accurate both with ET and conductivity meter, mostly due to the correlation with supporting electrolyte. The behaviour of EOA, with almost no correlation to the supporting electrolyte makes it difficult to predict using conductivity but ET prediction models give promising indications of its capabilities.     

Determination of total polyphenol index in wines employing a voltammetric electronic tongue

This work reports the application of a voltammetric electronic tongue system (ET) made from an array of modified graphite-epoxy composites plus a gold microelectrode in the qualitative and quantitative analysis of polyphenols found in wine. Wine samples were analyzed using cyclic voltammetry without any sample pretreatment. The obtained responses were preprocessed employing discrete wavelet transform (DWT) in order to compress and extract significant features from the voltammetric signals, and the obtained approximation coefficients fed a multivariate calibration method (artificial neural network-ANN-or partial least squares-PLS-) which accomplished the quantification of total polyphenol content. External test subset samples results were compared with the ones obtained with the Folin–Ciocalteu (FC) method and UV absorbance polyphenol index (I₂₈₀) as reference values, with highly significant correlation coefficients of 0.979 and 0.963 in the range from 50 to 2400 mg L⁻¹ gallic acid equivalents, respectively. In a separate experiment, qualitative discrimination of different polyphenols found in wine was also assessed by principal component analysis (PCA).     

Detection of antibiotic residues in bovine milk by a voltammetric electronic tongue system

A voltammetric electronic tongue (VE-tongue) was developed to detect antibiotic residues in bovine milk. Six antibiotics (Chloramphenicol, Erythromycin, Kanamycin sulfate, Neomycin sulfate, Streptomycin sulfate and Tetracycline HCl) spiked at four different concentration levels (0.5, 1, 1.5 and 2 maximum residue limits (MRLs)) were classified based on VE-tongue by two pattern recognition methods: principal component analysis (PCA) and discriminant function analysis (DFA). The VE-tongue was composed of five working electrodes (gold, silver, platinum, palladium, and titanium) positioned in a standard three-electrode configuration. The Multi-frequency large amplitude pulse voltammetry (MLAPV) which consisted of four segments (1 Hz, 10 Hz, 100 Hz and 1000 Hz) was applied as potential waveform. The six antibiotics at the MRLs could not be separated from bovine milk completely by PCA, but all the samples were demarcated clearly by DFA. Three regression models: Principal Component Regression Analysis (PCR), Partial Least Squares Regression (PLSR), and Least Squares-Support Vector Machines (LS-SVM) were used for concentrations of antibiotics prediction. All the regression models performed well, and PCR had the most stable results.     

A voltammetric electronic tongue made of modified epoxy-graphite electrodes for the qualitative analysis of wine

First results are presented of a new voltammetric electronic tongue which employs modified epoxy-graphite electrodes. This analytical tool has been applied to qualitative wine analysis, performing the classification of wine varieties, as well as recognition of the oxygenation effect. In the same way, studies related to the detection of some defects in wine production were also assessed, such as its vinegary taste in open-air contact or the use of excess sulphite preservative. The electronic tongue was formed by five voltammetric electrodes, four of them being bulk-modified with different substances: copper and platinum nanoparticles on one side, and polyaniline and polypyrrole powder on the other. The responses were preprocessed employing Principal Component Analysis (PCA) to visualize and identify distinct episodes. The resulting PCA scores were modelled with an artificial neural network that accomplishes final prediction with the qualitative classification of wines and/or detection of defects.     

Direct inverse voltammetric determination of Pb, Cu and Cd in some edible oils after solubilization

The direct determination of traces of certain heavy metals in oils is possible after treatment of the methanolic sample solution with Lumatom (tetramethylammoniumhydroxide). For the inverse voltammetric determination, a flow through cell with a mercury film electrode is used. The results for the determination of Cu and Pb in some edible oils after solubilization agree well with those obtained using wet oxidative high pressure ashing.     

Radial basis neural network for the classification of fresh edible oils using an electronic nose

An electronic nose utilising an array of six-bulk acoustic wave polymer coated Piezoelectric Quartz (PZQ) sensors has been developed. The nose was presented with 346 samples of fresh edible oil headspace volatiles, generated at 45°C. Extra virgin olive (EVO), Non-virgin olive oil (OI) and Sunflower oil (SFO), were used over a period of 30 days. The sensor responses were then analysed producing an architecture for the Radial Basis Function Artificial Neural Network (RBF). It was found that the RBF results were excellent, giving classifications of above 99% for the vegetable oil test samples.This revised version was published online in November 2005 with corrections to the Cover Date.     

A novel double-sampled square-wave voltammetric technique combined with quadruple-disk metallic-glassy carbon electronic tongue for isotonic beverages profiling

This work demonstrates that quadruple-disk electrodes with an innovative design, iridium-platinum, platinum-glassy carbon, and iridium-glassy carbon can effectively function as a single sensor and act as a voltammetric electronic tongue in food profiling. The key strategy used was a novel technique called double-sampled differential square-wave voltammetry (DSSWV), which increased the resolution and enabled extraction of the full, hidden information available in the measuring signals. Four clustering validity indices, namely the Calinski – Harabasz index, Davies – Bouldin index, Silhouette index and gap statistics were employed to select the optimal sensor and methodology for interpreting the current samples. The best expected results were obtained using an iridium-platinum electrode combined with overall cathodic potential modulation using the DSSWV technique. Principal component analysis and polar dendrogram confirmed the correct clustering of different isotonic beverages. However, it was also demonstrated that in some variants of measurement and signal interpretation, it was not possible to group the samples appropriately.     

Resolution of phenolic antioxidant mixtures employing a voltammetric bio-electronic tongue

This work reports the application of a Bio-Electronic Tongue (BioET) system made from an array of enzymatic biosensors in the analysis of polyphenols, focusing on major polyphenols found in wine. For this, the biosensor array was formed by a set of epoxy-graphite biosensors, bulk-modified with different redox enzymes (tyrosinase and laccase) and copper nanoparticles, aimed at the simultaneous determination of the different polyphenols. Departure information was the set of voltammograms generated with the biosensor array, selecting some characteristic features in order to reduce the data for the Artificial Neural Network (ANN). Finally, after the ANN model optimization, it was used for the resolution and quantification of each compound. Catechol, caffeic acid and catechin formed the three-analyte case study resolved in this work. Good prediction ability was attained, therefore allowing the separate quantification of the three phenols with predicted vs. expected slope better than 0.970 for the external test set (n = 10). Finally, BioET has been also tested with spiked wine samples with good recovery yields (values of 104%, 117% and 122% for catechol, caffeic acid and catechin, respectively).     

GC-MS法鉴别食用油和餐饮业中废弃油脂的研究

用气相色谱 质谱联用(GC MS)方法对7种餐饮业中废弃油脂(简称废油脂)和5种合格成品食用油(简称食用油)中所有脂肪酸进行分析。研究发现,废油脂中部分不饱和脂肪酸受到氧化,使脂肪酸相对不饱和度(U R)值明显小于同种类食用油中的脂肪酸相对不饱和度(U R)值,其脂肪酸的质量分数分布与同种类的食用油中脂肪酸的质量分数分布有很大的区别,以及绝大部分废油脂中存在较大量矿物油。研究表明,脂肪酸相对不饱和度(U R)值和脂肪酸的质量分数分布可以鉴别废油脂。     

Remote environmental monitoring employing a potentiometric electronic tongue

This work investigates the use of electronic tongues for environmental monitoring. Electronic tongues were based on arrays of potentiometric sensors plus a complex data processing by artificial neural networks and data transmission by radiofrequency. A first application, intended for a system simulating real conditions in surface water, performed a simultaneous monitoring of ammonium, potassium, sodium, chloride, and nitrate ions. The proposed system allowed us to assess the effect of natural biodegradation stages for these species. A second application was used to monitor concentrations of ammonium, potassium, and sodium in the ‘Ignacio Ramírez’ dam (Mexico). The electronic tongue used here allowed us to determine the content of the three cations in real water samples, although a high matrix effect was encountered for sodium determination. The implemented radio transmission worked robustly during all the experiments, thus demonstrating the viability of the proposed systems for automated remote applications.     

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.

Neutron activation analysis for measuring the unsaturation in edible oils

Oil smears /2–10 mg/ on a filter paper were directly brominated by bromine vapor, and the quantity of Br reacted with the lipid was determined by instrumental neutron activation analysis. The iodine value for commercially available almond oil, sunflower oil, peanut oil, soy oil and sesame oil were determined by the proposed method. The relative standard deviation associated with the measurements was less than 3%. No significant difference was observed between the iodine values determined by the proposed method and that determined by one of the officially approved methods. The proposed method possesses the advantages of shortening reaction time and applicability to small sample size.