Simultaneous determination of fermented milk aroma compounds by a potentiometric sensor array

The paper reports on the application of an electronic tongue for simultaneous determination of ethanol, acetaldehyde, diacetyl, lactic acid, acetic acid and citric acid content in probiotic fermented milk. The αAstree electronic tongue by Alpha M.O.S. was employed. The sensor array comprised of seven non-specific, cross-sensitive sensors developed especially for food analysis coupled with a reference Ag/AgCl electrode. Samples of plain, strawberry, apple-pear and forest-fruit flavored probiotic fermented milk were analyzed both by standard methods and by the potentiometric sensor array. The results obtained by these methods were used for the development of neural network models for rapid estimation of aroma compounds content in probiotic fermented milk.The highest correlation (0.967) and lowest standard deviation of error for the training (0.585), selection (0.503) and testing (0.571) subset was obtained for the estimation of ethanol content. The lowest correlation (0.669) was obtained for the estimation of acetaldehyde content. The model exhibited poor performance in average error and standard deviations of errors in all subsets which could be explained by low sensitivity of the sensor array to the compound. The obtained results indicate that the potentiometric electronic tongue coupled with artificial neural networks can be applied as a rapid method for the determination of aroma compounds in probiotic fermented milk.     

The recognition of beer with flow-through sensor array based on miniaturized solid-state electrodes

Flow-through electronic tongue based on miniaturized solid-state potentiometric sensors has been developed. A simple technique, i.e. membrane solution casting on the surface of the planar Au transducers was applied for the preparation of classical ion-selective and partially selective microelectrodes, introduced in the flow-through sensor array. The performance of the designed electronic tongue was tested in the qualitative analysis of various brands of beer. Samples of the same brand of beer but with different manufacture dates, originating from different manufacture lots, have been applied in the studies. The combination of PLS and ANN techniques allowed the discrimination between different brands of beer with 83% of correct classifications.     

Conducting polymer-based array for the discrimination of odours from trim plastic materials used in automobiles

This paper reports the test of the use of an electronic nose based on chemoresistive polymeric sensors for the discrimination of eight sample materials used in the car manufacture, including parts fabricated in polyurethane (PU). Principal component analysis (PCA) and cluster analysis (CA) have been used in order to classify and identify the odours coming from the parts under study. Good separation among odours of samples of comparable composition has been obtained using the first coefficients of the fast Fourier transform (FFT) of the whole curve as the input variable of PCA. These results show the feasibility of the conducting polymer array of sensors to discriminate odours for the air cabin quality control.     

Fish freshness detection by a computer screen photoassisted based gas sensor array

In the last years a large number of different measurement methodologies were applied to measure the freshness of fishes. Among them the connection between freshness and headspace composition has been considered by gas chromatographic analysis and from the last two decades by a number of sensors and biosensors aimed at measuring some characteristic indicators (usually amines). More recently also the so-called artificial olfaction systems gathering together many non-specific sensors have shown a certain capability to transduce the global composition of the fish headspace capturing the differences between fresh and spoiled products. One of the main objectives related to the introduction of sensor systems with respect to the analytical methods is the claimed possibility to distribute the freshness control since sensors are expected to be “portable” and “simple”. In spite of these objectives, until now sensor systems did not result in any tool that may be broadly distributed. In this paper, we present a chemical sensor array where the optical features of layers of chemicals, sensitive to volatile compounds typical of spoilage processes in fish, are interrogated by a very simple platform based on a computer screen and a web cam. An array of metalloporphyrins is here used to classify fillets of thawed fishes according to their storage days and to monitor the spoilage in filleted anchovies for a time of 8 h. Results indicate a complete identification of the storage days of thawed fillets and a determination of the storage time of anchovies held at room temperature with a root mean square error of validation of about 30 min.The optical system produces a sort of spectral fingerprint containing information about both the absorbance and the emission of the sensitive layer. The system here illustrated, based on computer peripherals, can be easily scaled to any device endowed with a programmable screen and a camera such as cellular phones offering for the first time the possibility to fulfil the sensor expectation of diffused and efficient analytical capabilities.     

Identification of catecholamine neurotransmitters using fluorescence sensor array

A nano-based sensor array has been developed for identification and discrimination of catecholamine neurotransmitters based on optical properties of their oxidation products under alkaline conditions. To produce distinct fluorescence response patterns for individual catecholamine, quenching of thioglycolic acid functionalized cadmium telluride (CdTe) quantum dots, by oxidation products, were employed along with the variation of fluorescence spectra of oxidation products. The spectral changes were analyzed with hierarchical cluster analysis (HCA) and principal component analysis (PCA) to identify catecholamine patterns. The proposed sensor could efficiently discriminate the individual catecholamine (i.e., dopamine, norepinephrine, and l-DOPA) and their mixtures in the concentration range of 0.25–30 μmol L⁻¹. Finally, we found that the sensor had capability to identify the various catecholamines in urine sample.     

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 network's parameters were optimised and its usefulness in determining NH₄⁺, 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 calcium and total hardness in natural waters using a potentiometric sensor array

The determination of calcium and total hardness in natural waters is carried out with a potentiometric sensor array which consists of a series of ion-selective electrodes (ISEs) for Ca²⁺, Mg²⁺, NH₄⁺, K⁺, Na⁺, Li⁺, and H⁺. The selectivity of the calcium and magnesium ISEs is not fully achieved as other cation species may interfere with the analysis. The proposed sensor array device can overcome this drawback since it can take advantage of the cross-selectivities of cation species towards each ISE. In this approach, the multivariate data generated by the sensor array results in a richer source of analytical information which allows the quantification of calcium and total hardness in the water samples by means of chemometric methods. Results obtained are in reasonable concordance with those given by the standard method based on complexometry.     

Quartz crystal microbalance sensor array for the detection of volatile organic compounds

A sensor array system consisting of five quartz crystal microbalance (QCM) sensors (four for measuring and one for reference) and an artificial neural network (ANN) method is presented for on-line detection of volatile organic compounds. Three ionic liquids, 1-butyl-3-methylimidazolium chloride (C₄mimCl), 1-butyl-3-methylimidazolium hexafluorophosphate (C₄mimPF₆), 1-dedocyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (C₄mimNTf₂), and silicone oil II, which is widely used as gas chromatographic stationary phase, have been selected as sensitive coatings on the quartz surface allowing the sensor array effective to identify chemical vapors, such as toluene, ethanol, acetone and dichloromethane. The success rate for the qualitative recognition reached 100%. Quantitative analysis has also been investigated, within the concentration range of 0.6-6.1 mg/L for toluene, 0.9-7.5 mg/L for ethanol, 2.8-117 mg/L for dichloromethane, and 0.7-38 mg/L for acetone, with a prediction error lower than 8%.     

Method of gas mixtures discrimination based on sensor array, temporal response and data driven approach

This work presents a method of gas mixtures discrimination. The principal concept of the method is to apply measurement data provided by a combination of sensors at single time point of their temporal response as input of the discrimination models. The pattern data combinations are selected for classes of target gases based on the criterion of 100% efficient discrimination. Combinations of sensors and time points, which provide pattern data combinations in course or repeated measurements, are encoded in the form of addresses. The designer of sensor system is responsible for their selection and they are included in the software of the final instrument. The study of the method involved the discrimination of gas mixtures composed of air and single chemical: hexane, ethanol, acetone, ethyl acetate and toluene. Two sensor arrays were utilized. Each consisted of six TGS sensors of the same type. The dynamic operation of sensors was employed. As an example the stop-flow mode was chosen. The work provides the evidence of the existence of sensor combinations and time points, which are successful in discrimination of studied classes of target gases. The persistence of addresses was discussed considering the ability of sensor array to recognize analytes, variability of repeated measurement results, number of repeated measurements and a twin sets of sensors. Altogether, the validity of the method was demonstrated.     

Use of sequential injection analysis to construct an electronic-tongue: application to multidetermination employing the transient response of a potentiometric sensor array

An electronic tongue based on the transient response of an array of non-specific-response potentiometric sensors was developed. A sequential injection analysis (SIA) system was used in order to automate its training and operation. The use of the transient recording entails the dynamic nature of the sensor's response, which can be of high information content, of primary ions and also of interfering ions; these may better discriminated if the kinetic resolution is added. This work presents the extraction of significant information contained in the transient response of a sensor array formed by five all-solid-state potentiometric sensors. The tool employed was the Fourier transform, from which a number of coefficients were fed into an artificial neural network (ANN) model, used to perform a quantitative multidetermination. The studied case was the analysis of mixtures of calcium, sodium and potassium. Obtained performance is compared with the more traditional automated electronic tongue using final steady-state potentials.     

Classification of amino acids and oligopeptides with the use of multi-mode chemical images obtained with ion selective electrode array

The analysis of amino acids and oligopeptides as important bioanalytes is a significant task in medical diagnostics and proteomic research and currently it is based on chromatographic and electrophoretic methods. In this paper, an approach based on sensor array coupled with titration is proposed for effective recognition of 5 amino acids and 4 oligopeptides. It is shown, that the increase of information gained during measurements of sensors’ signals in various pH allows for capturing more data on interaction of amino acids and oligopeptides with various polymeric membranes of potentiometric electrodes used in the array, and that leads to the lowering of classification errors in partial least squares analysis.     

BODIPY-based fluorometric sensor array for the highly sensitive identification of heavy-metal ions

A BODIPY(4,4-difluoro-4-bora-3a,4a-diaza-s-indacene)-based fluorometric sensor array has been developed for the highly sensitive detection of eight heavy-metal ions at micromolar concentration. The di-2-picolyamine (DPA) derivatives combine high affinities for a variety of heavy-metal ions with the capacity to perturb the fluorescence properties of BODIPY, making them perfectly suitable for the design of fluorometric sensor arrays for heavy-metal ions. 12 cross-reactive BODIPY fluorescent indicators provide facile identification of the heavy-metal ions using a standard chemometric approach (hierarchical clustering analysis); no misclassifications were found over 45 trials. Clear differentiation among heavy-metal ions as a function of concentration was also achieved, even down to 10⁻⁷ M. A semi-quantitative interpolation of the heavy-metal concentration is obtained by comparing the total Euclidean distance of the measurement with a set of known concentrations in the library.     

Detection and classification of gaseous sulfur compounds by solid electrolyte cyclic voltammetry of cermet sensor array

Electrochemical sensors composed of a ceramic-metallic (cermet) solid electrolyte are used for the detection of gaseous sulfur compounds SO₂, H₂S, and CS₂ in a study involving 11 toxic industrial chemical (TIC) compounds. The study examines a sensor array containing four cermet sensors varying in electrode-electrolyte composition, designed to offer selectivity for multiple compounds. The sensors are driven by cyclic voltammetry to produce a current-voltage profile for each analyte. Raw voltammograms are processed by background subtraction of clean air, and the four sensor signals are concatenated to form one vector of points. The high-resolution signal is compressed by wavelet transformation and a probabilistic neural network is used for classification. In this study, training data from one sensor array was used to formulate models which were validated with data from a second sensor array. Of the 11 gases studied, 3 that contained sulfur produced the strongest responses and were successfully analyzed when the remaining compounds were treated as interferents. Analytes were measured from 10 to 200% of their threshold-limited value (TLV) according to the 8-h time weighted average (TWA) exposure limits defined by the National Institute of Occupational Safety and Health (NIOSH). True positive classification rates of 93.3, 96.7, and 76.7% for SO₂, H₂S, and CS₂, respectively, were achieved for prediction of one sensor unit when a second sensor was used for modeling. True positive rates of 83.3, 90.0, and 90.0% for SO₂, H₂S, and CS₂, respectively, were achieved for the second sensor unit when the first sensor unit was used for modeling. Most of the misclassifications were for low concentration levels (such 10-25% TLV) in which case the compound was classified as clean air. Between the two sensors, the false positive rates were 2.2% or lower for the three sulfur compounds, 0.9% or lower for the interferents (eight remaining analytes), and 5.8% or lower for clean air. The cermet sensor arrays used in this analysis are rugged, low cost, reusable, and show promise for multiple compound detection at parts-per-million (ppm) levels.     

A fast and colorimetric sensor array for the discrimination of ribonucleotides in human urine samples by gold nanorods

Ribonucleotides are usually functioned as biomarkers to diagnose diseases and monitor the life activities in living organisms,and their discrimination is of great significance but challenging.Taking advantage of the unique characteristics of gold nanorods(AuNRs),herein,a colorimetric sensor array for discrimination of twelve ribonucleotides was developed based on the chemical etching of AuNRs with controllable aspect ratios.During the etching process,AuNRs were preferentially shortened and eventually turned into Au(Ⅲ) state by Fenton's reaction.The morphological change of AuNRs led to the significant color change and blue shift in the corresponding extinction spectrum.However,when Fe²⁺bound with ribonucleotides,the Fenton's reaction was prevented and the ability to etch AuNRs was weakened or disappeared.Due to the different structures of nucleotides,the binding ability of them with Fe²⁺ was distinct,resulting in the discrepancy in the chemical etching of AuNRs,which could be developed for distinguishing ribonucleotides.Moreover,the proposed sensor array was successfully explored to distinguish ribonucleotides in complex human urine samples.     

Synthesis of a multitopic pyrene-thiophene-anthracene-2,2′:6′,2″-terpyridine array

A one-pot synthesis of a multitopic ligand is described, along with its complexes with zinc(II) and ruthenium(II) cations.     

Methodology for the selection of suitable sensors for incorporation into a gas sensor array

An investigation into suitable mathematical techniques which can be used to select sensor components for a gas sensor array is reported. Data from a tin dioxide Taguchi semiconductor sensor array were obtained individually for various organic solvents and analysed using multivariate techniques, including principal component analysis, cluster analysis and star symbol plots. It was shown that the array data produced a series of characteristic response patterns for the analytes. It was also found that analytes of similar chemical nature had similar response patterns, indicating a correlation between sensor interaction and the chemical functional groups of the analyte. The multivariate techniques used proved to be very useful in enabling a suitable selection of the components of the array to be made by identifying which of the components or sensors were acting independently.     

Exploring the effects of cooperative interactions on affinity using a pinwheel sensor system

A series of three pinwheel sensors were constructed with 1, 2, and 3 binding sites. Binding of Zn⁺² and Cd⁺² was monitored by fluorescence over a range of temperatures. The data demonstrate that cooperative interactions generally increase the effective affinity of the sensor. This effect is more pronounced in systems which have lower inherent affinity for the analyte.     

Progress toward the development of a point-of-care photonic crystal ammonia sensor

We have developed an ammonia-sensitive material by coupling the Berthelot reaction to our polymerized crystalline colloidal array (PCCA) technology. The material consists of a periodic array of highly charged colloidal particles (110 nm diameter) embedded in a poly(hydroxyethyl acrylate) hydrogel. The particles have a lattice spacing such that they Bragg-diffract visible light. In the Berthelot reaction, ammonia, hypochlorite, and phenol react to produce the dye molecule indophenol blue in an aqueous solution. We use this reaction in our sensor by covalently attaching 3-aminophenol to the hydrogel backbone, which forms cross-links through the Berthelot mechanism. Ammonia reacts with hypochlorite, forming monochloramine, which then reacts with a pendant aminophenol to form a benzoquinone chlorimine. The benzoquinone chlorimine reacts with another pendant aminophenol to form a cross-link. The creation of new cross-links causes the hydrogel to shrink, which reduces the lattice spacing of the embedded colloidal array. This volume change results in a blue-shift in the diffracted light proportional to the concentration of NH₃ in the sample. We demonstrate that the NH₃ photonic crystal sensing material is capable of quantitative determination of concentrations in the physiological range (50–350 μmol NH₃ L⁻¹) in human blood serum.