Potentiometric Chemical Sensors Research Articles

Paralytic Shellfish Toxin Extraction from Bivalve Meat for Analysis Using Potentiometric Chemical Sensors.

A simple and reliable methodology for the detection of paralytic shellfish toxins (PSTs) in bivalve tissues using potentiometric chemical sensors was developed. Five methods of PST extraction from mussel and oyster tissues were evaluated, including the AOAC-recommended method, which served as the reference. The main objective was to minimize the matrix effect of the extracts on the sensors' responses and ensure efficient toxin recovery. Extraction procedures using acetic acid with heating and water yielded the highest responses from the potentiometric chemical sensors to PSTs. The highest recovery of PSTs from bivalve tissues was achieved with extraction using acetic acid and heating. Further extract purification, which is indispensable for liquid chromatography with fluorometric detection (LC-FLD) analysis, was found to be unnecessary for analysis with chemical sensors. While water extraction can also be used as a rapid and simple PST extraction method, the lower recoveries should be considered when interpreting the results. Further research is needed to identify the compounds remaining in the extracts that cause a decrease in sensor responses and to develop procedures for their elimination.

Ultra-sensitive nitrate-ion detection via transconductance-enhanced graphene ion-sensitive field-effect transistors

Current potentiometric sensing methods are limited to detecting nitrate at parts-per-billion (sub-micromolar) concentrations, and there are no existing potentiometric chemical sensors with ultralow detection limits below the parts-per-trillion (picomolar) level. To address these challenges, we integrate interdigital graphene ion-sensitive field-effect transistors (ISFETs) with a nitrate ion-sensitive membrane (ISM). The work aims to maximize nitrate ion transport through the nitrate ISM, while achieving high device transconductance by evaluating graphene layer thickness, optimizing channel width-to-length ratio (RWL), and enlarging total sensing area. The captured nitrate ions by the nitrate ISM induce surface potential changes that are transduced into electrical signals by graphene, manifested as the Dirac point shifts. The device exhibits Nernst response behavior under ultralow concentrations, achieving a sensitivity of 28 mV/decade and establishing a record low limit of detection of 0.041 ppt (4.8 × 10−13 M). Additionally, the sensor showed a wide linear detection range from 0.1 ppt (1.2 × 10−12 M) to 100 ppm (1.2 × 10−3 M). Furthermore, successful detection of nitrate in tap and snow water was demonstrated with high accuracy, indicating promising applications to drinking water safety and environmental water quality control.

Optimization of the polyaniline solid contact in potentiometric sensors for detection of paralytic shellfish toxins in mussels

The present study optimized the properties of the electropolymerized polyaniline (PANI) solid inner contact for a potentiometric chemical sensor by varying the thickness of the polymer layer. A potentiometric sensor for detecting one of the paralytic shellfish toxins, decarbamoyl saxitoxin (dcSTX), in mussel extracts was selected as a case study. The plasticized PVC membrane composition, developed in previous work for the detection of this toxin, was used. The structure and electrical properties of PANI layers of different thicknesses were studied using scanning electron microscopy and electrochemical impedance spectroscopy. The effect of PANI solid contact thickness on the characteristics of the potentiometric sensor, including sensitivity, detection limit, and selectivity to dcSTX in buffer solutions and mussel extracts, was evaluated. The formation of a water layer at the inner solid contact and PVC membrane interface was investigated using electrochemical impedance spectroscopy and a water test. PANI layer thickness 1.1 µm was found to be optimal for solid inner contact for potentiometric sensor with PVC membrane providing highest sensitivity and selectivity.

Designing new sulfate ionophores for potentiometric membrane sensors: Selectivity assessment and practical application

Developing potentiometric chemical sensors for sulfate determination is a rather challenging task due to a considerable hydrophilicity of this anion and its poor affinity to lipophilic plasticized polymeric membranes – one of the most popular sensor membrane materials nowadays. Not too many research works were devoted to this problem in the last years. Here we report on the successful development of several novel sulfate ionophores based on the synthetic modification of the commercially available sulfate ionophore I. The newly developed sensors outperform commercial analogue in terms of elelctrochemical sensitivity and selectivity. We have highlighted certain problems with numerical selectivity assessment in case when primary and interfering ions have different charges. The counterintuitive results yielded by conventional selectivity quantification methods implies the relevance of using alternative approach – visualizing sensor response curves towards primary ion in the presence of interfering ions. Novel sulfate sensors were applied for direct potentiometric measurements of sulfate content in river water samples demonstrating real applicability of the developed sensor membranes.

Sensor Selection for an Electronic Tongue for the Rapid Detection of Paralytic Shellfish Toxins: A Case Study

The performance of an electronic tongue can be optimized by varying the number and types of sensors in the array and by employing data-processing methods. Sensor selection is typically performed empirically, with sensors picked up either by analyzing their characteristics or through trial and error, which does not guarantee an optimized sensor array composition. This study focuses on developing a method for sensor selection for an electronic tongue using simulated sensor data and Lasso regularization. Simulated sensor responses were calculated using sensor parameters such as sensitivity and selectivity, which were determined in the individual analyte solutions. Sensor selection was carried out using Lasso regularization, which removes redundant or highly correlated variables without much loss of information. The objective of the optimization of the sensor array was twofold, aiming to minimize both quantification errors and the number of sensors in the array. The quantification of toxins belonging to one of the groups of marine toxins—paralytic shellfish toxins (PSTs)—using arrays of potentiometric chemical sensors was used as a case study. Eight PSTs corresponding to the toxin profiles in bivalves due to the two common toxin-producing phytoplankton species, G. catenatum (dcSTX, GTX5, GTX6, and C1+2) and A. minitum (STX, GTX2+3), as well as total sample toxicity, were included in the study. Experimental validation with mixed solutions of two groups of toxins confirmed the suitability of the proposed method of sensor array optimization with better performance obtained for the a priori optimized sensor arrays. The results indicate that the use of simulated sensor responses and Lasso regularization is a rapid and efficient method for the selection of an optimized sensor array.

Pb2+ potentiometric chemical sensors based on lead and silver doped thioaresenate glasses

Lead doped chalcogenide glass compositions, in the pseudo-ternary AgI-PbS-As2S3, were examined as active membrane materials for the construction of Pb(II) ion-selective sensors, Pb(II)−ISE. The basic glass properties of the synthesised bulk samples such as vitreous state, macroscopic, thermal and electric properties were studied. To this end, several techniques were used, including X-ray powder diffraction, density measurements, differential scanning calorimetry, scanning electron microscopy and impedance complex spectroscopy. It is found that the density increases with increased AgI and PbS contents. The glass transition temperatures for the studied compositions were also reported. The electrical measurement show the conductivity increases with increasing AgI content. Meanwhile, for the glasses with same AgI content and increasing PbS content, the conductivity decreases. For the fabricated sensors, several key analytical parameters were investigated to evaluate the different glass compositions as active membranes in Pb(II)−ISE, including detection limit, sensitivity, linearity, ionic selectivity (in the presence of K+, Na+, Ni2+, Ca2+, Cd2+, Cu2+ and Hg2+ interfering cations), reproducibility and optimal pH-range.

Lignosulfonate-Based Conducting Flexible Polymeric Membranes for Liquid Sensing Applications.

In this study, lignosulfonate (LS) from the acid sulfite pulping of eucalypt wood was used to synthesize LS-based polyurethanes (PUs) doped with multiwalled carbon nanotubes (MWCNTs) within the range of 0.1–1.4% w/w, yielding a unique conducting copolymer composite, which was employed as a sensitive material for all-solid-state potentiometric chemical sensors. LS-based PUs doped with 1.0% w/w MWCNTs exhibited relevant electrical conductivity suitable for sensor applications. The LS-based potentiometric sensor displayed a near-Nernstian or super-Nernstian response to a wide range of transition metals, including Cu(II), Zn(II), Cd(II), Cr(III), Cr(VI), Hg(II), and Ag(I) at pH 7 and Cr(VI) at pH 2. It also exhibited a redox response to the Fe(II)/(III) redox pair at pH 2. Unlike other lignin-based potentiometric sensors in similar composite materials, this LS-based flexible polymeric membrane did not show irreversible complexation with Hg(II). Only a weak response toward ionic liquids, [C2mim]Cl and ChCl, was registered. Unlike LS-based composites comprising MWCNTs, those doped with graphene oxide (GO), reduced GO (rGO), and graphite (Gr) did not reveal the same electrical conductivity, even with loads up to 10% (w/w), in the polymer composite. This fact is associated, at least partially, with the different filler dispersion abilities within the polymeric matrix.

(Invited) Strategies for Calibration Cost Reduction in Heterogeneous Chemical Sensor Arrays

Introduction Heterogeneous gas sensor arrays coupled with machine learning algorithms have been proposed for a wide range of applications. However, inherent sensor variability degrades the performance of calibration models when directly transferred to new replicates of the original system [1]. As a result, in order to fulfill industry requirements, very often, calibration needs to be performed individually for each unit, limiting mass-deployment [2]. Recently, calibration methodologies have been proposed to reduce calibration costs and extend lifetime of the models. Briefly, these strategies make use of calibration models built for a primary system, incorporate new information to the calibration model, or take advantage of nearby units. Calibration Transfer Calibration transfer relies on a primary unit, for which a calibration model is built using a full set of calibration samples. Then, the sensor space of the secondary unit is mapped to the space of the primary unit using a set of transfer samples. Finally, using the transfer function, the calibration model built for the primary can be used with the secondary instrument. Hence, the cost reduction can be estimated from the ratio between the full set of calibration measurements and the transfer samples.Different methodologies have been proposed to obtain the transfer function, with different assumptions. For example, Single Signal Standardization assumes that the differences between units are linear, with no shifts in the signal. On the contrary, Direct Standardization (DS) constructs the mapping between all signals and can capture shifts in the signal. DS, however, may incorporate unwanted sources of variability and may face highly underdetermined systems [3]. Piecewise Direct Standardization (PDS) operates locally and can overcome the computational limitations of DS. The mentioned techniques have been tested on heterogeneous MOX gas sensor arrays [4,5]. Results showed that variations in sensor operating temperature can be corrected by means of calibration transfer techniques. Moreover, aging and sensor drift can be also alleviated with calibration transfer methods [6]. Calibration Update Calibration update, instead, enriches the calibration dataset with new calibration examples (update samples). The model incorporates the update samples using a hyperparameter that controls the weight of the new samples. It can also be combined with system regularization, in which case the regularization parameter needs to be selected for each task. The method showed successful results when applied to potentiometric chemical sensors [7,8]. Nevertheless, the regularization parameter and the weight of the new samples significantly changed between calibration updates. After some time, new samples dominate the calibration model such that the original calibration dataset contributes scarcely to the model. Although calibration update was designed to adapt the calibration model of a sensing unit affected by drift, it can also be applied to other sensing units since, after some time, any sensing unit behaves like another unit. In-the-field calibration A background mismatch between laboratory calibration and in-the-field operation may render calibration models inefficient. In-the-field calibration takes advantage of on-site reference instrumentation to perform calibration and ensure same background during calibration phase and deployment. Usually, linear models are built, which can include additional terms to account for temperature, humidity and time effects [9]. Collocation, however, requires the installation of the sensing unit next to the reference instrumentation, time during which the system is not operational.Multi-hop calibration enables node-to-node calibration such that data stream is not interrupted (see Figure). This strategy is particularly appropriate in scenarios with moving units that encounter each other regularly. It provided higher accuracy than single collocation when implemented in public vehicles equipped with electrochemical sensors [10]. However, as the number of hops increases, the resulting calibration becomes less reliable. It has been suggested that a node redundancy of about 30% can keep calibration error contained [11]. Conclusions Very different methodologies have been proposed to reduce calibration costs and increase lifetime of heterogeneous chemical systems. However, the suitability of each method depends on the sensor technology and the required task. Further benchmark is still required to elucidate the methods that provide best results and under which conditions and environments. This work provides a discussion on the calibration methodologies that aim at reducing calibration costs and encourages researchers to share datasets for the benchmark of the different calibration strategies.

(Invited) Strategies for Calibration Cost Reduction in Heterogeneous Chemical Sensor Arrays

Introduction Heterogeneous gas sensor arrays coupled with machine learning algorithms have been proposed for a wide range of applications. However, inherent sensor variability degrades the performance of calibration models when directly transferred to new replicates of the original system [1]. As a result, in order to fulfill industry requirements, very often, calibration needs to be performed individually for each unit, limiting mass-deployment [2]. Recently, calibration methodologies have been proposed to reduce calibration costs and extend lifetime of the models. Briefly, these strategies make use of calibration models built for a primary system, incorporate new information to the calibration model, or take advantage of nearby units. Calibration Transfer Calibration transfer relies on a primary unit, for which a calibration model is built using a full set of calibration samples. Then, the sensor space of the secondary unit is mapped to the space of the primary unit using a set of transfer samples. Finally, using the transfer function, the calibration model built for the primary can be used with the secondary instrument. Hence, the cost reduction can be estimated from the ratio between the full set of calibration measurements and the transfer samples.Different methodologies have been proposed to obtain the transfer function, with different assumptions. For example, Single Signal Standardization assumes that the differences between units are linear, with no shifts in the signal. On the contrary, Direct Standardization (DS) constructs the mapping between all signals and can capture shifts in the signal. DS, however, may incorporate unwanted sources of variability and may face highly underdetermined systems [3]. Piecewise Direct Standardization (PDS) operates locally and can overcome the computational limitations of DS. The mentioned techniques have been tested on heterogeneous MOX gas sensor arrays [4,5]. Results showed that variations in sensor operating temperature can be corrected by means of calibration transfer techniques. Moreover, aging and sensor drift can be also alleviated with calibration transfer methods [6]. Calibration Update Calibration update, instead, enriches the calibration dataset with new calibration examples (update samples). The model incorporates the update samples using a hyperparameter that controls the weight of the new samples. It can also be combined with system regularization, in which case the regularization parameter needs to be selected for each task. The method showed successful results when applied to potentiometric chemical sensors [7,8]. Nevertheless, the regularization parameter and the weight of the new samples significantly changed between calibration updates. After some time, new samples dominate the calibration model such that the original calibration dataset contributes scarcely to the model. Although calibration update was designed to adapt the calibration model of a sensing unit affected by drift, it can also be applied to other sensing units since, after some time, any sensing unit behaves like another unit. In-the-field calibration A background mismatch between laboratory calibration and in-the-field operation may render calibration models inefficient. In-the-field calibration takes advantage of on-site reference instrumentation to perform calibration and ensure same background during calibration phase and deployment. Usually, linear models are built, which can include additional terms to account for temperature, humidity and time effects [9]. Collocation, however, requires the installation of the sensing unit next to the reference instrumentation, time during which the system is not operational. Multi-hop calibration enables node-to-node calibration such that data stream is not interrupted (see Figure). This strategy is particularly appropriate in scenarios with moving units that encounter each other regularly. It provided higher accuracy than single collocation when implemented in public vehicles equipped with electrochemical sensors [10]. However, as the number of hops increases, the resulting calibration becomes less reliable. It has been suggested that a node redundancy of about 30% can keep calibration error contained [11]. Conclusions Very different methodologies have been proposed to reduce calibration costs and increase lifetime of heterogeneous chemical systems. However, the suitability of each method depends on the sensor technology and the required task. Further benchmark is still required to elucidate the methods that provide best results and under which conditions and environments. This work provides a discussion on the calibration methodologies that aim at reducing calibration costs and encourages researchers to share datasets for the benchmark of the different calibration strategies.

Nanocomposite Polymeric Materials Based on Eucalyptus Lignoboost® Kraft Lignin for Liquid Sensing Applications.

This study reports the synthesis of polyurethane–lignin copolymer blended with carbon multilayer nanotubes to be used in all-solid-state potentiometric chemical sensors. Known applicability of lignin-based polyurethanes doped with carbon nanotubes for chemical sensing was extended to eucalyptus LignoBoost® kraft lignin containing increased amounts of polyphenolic groups from concomitant tannins that were expected to impart specificity and sensitivity to the sensing material. Synthesized polymers were characterized using FT-MIR spectroscopy, electrical impedance spectroscopy, scanning electron microscopy, thermogravimetric analysis, and differential scanning calorimetry and are used for manufacturing of all solid-state potentiometric sensors. Potentiometric sensor with LignoBoost® kraft lignin-based polyurethane membrane displayed theoretical response and high selectivity to Cu (II) ions, as well as long-term stability.

A Carbamoylase-Based Bioassay for the Detection of Paralytic Shellfish Poisoning Toxins.

Out of control proliferation of toxic phytoplankton, called harmful algal blooms (HABs), have a significant economic impact on bivalve aquaculture and harvesting in coastal waters. Some phytotoxins, such as paralytic shellfish toxins (PSTs), are of concern due to the life-threatening symptoms they can cause. Development of rapid and low-cost screening tools would be a welcome addition to the laboratory methodologies employed in routine monitoring programs. However, most of the assays and biosensors for the screening of PSTs, are restricted to a single target, saxitoxin (STX), which is the most potent PST. The present study aimed at developing an assay for the detection of N-sulfocarbamoyl PST—GTX5, which is one of the most abundant toxins in bivalves during G. catenatum blooms as found on the Portuguese coast. Enzymatic assay employing PSTs’ transforming enzyme—carbamoylase—was proposed. Carbamoylase was extracted and purified from the surf clam S. solida. Carbamoylase displayed similar specificity to both carbamate (STX) and N-sulfocarbamate toxins (GTX5 and C1+2) converting them into decarbamoyl saxitoxin (dcSTX) and decarbamoyl gonyautoxins 2+3 (dcGTX2+3), respectively. The enzymatic assay involved hydrolysis of GTX5 by carbamoylase and quantification of the product of enzymatic reaction, dcSTX, using a potentiometric chemical sensor. A potentiometric sensor with plasticized PVC membrane that displayed sensitivity to dcSTX and selectivity in the presence of GTX5 was employed. Enzymatic assay allowed determination of GTX5 in the concentration range from 0.43 to 3.30 µmolL−1, which encompasses levels of GTX5 in contaminated bivalve extracts with toxicities above PSTs regulatory limits. The feasibility of the carbamoylase-based potentiometric assay for detection of GTX5 was demonstrated.

Determination of paralytic shellfish toxins using potentiometric electronic tongue

Paralytic shellfish toxins (PSTs) are monitored in commercial bivalves in several countries in the world due to their toxicity to human consumers. The present work examines the application of an electronic tongue based on potentiometric chemical sensors to the quantification of PSTs in mussel extracts. The electronic tongue comprised six miniaturized sensors with solid inner contact and plasticized polyvinylchloride membranes. Calibration models were calculated by PLS regression using measurements in sixteen model mixed solutions containing four PSTs commonly found in bivalves from the Portuguese coast. Transfer of the calibration models to sample matrix was done by joint-PLS regression using measurements in five mussel extracts spiked with PST standards. Quantification of PSTs in extracts of naturally contaminated mussels, using the electronic tongue and updated calibration model, was in agreement with values of the chromatographic reference method. Those sensors alone or combined in an electronic tongue are useful tools for rapid screening of PST in bivalves.

Potentiometric chemical sensors for the detection of paralytic shellfish toxins

Potentiometric chemical sensors for the detection of paralytic shellfish toxins have been developed. Four toxins typically encountered in Portuguese waters, namely saxitoxin, decarbamoyl saxitoxin, gonyautoxin GTX5 and C1&C2, were selected for the study. A series of miniaturized sensors with solid inner contact and plasticized polyvinylchloride membranes containing ionophores, nine compositions in total, were prepared and their characteristics evaluated. Sensors displayed cross-sensitivity to four studied toxins, i.e. response to several toxins together with low selectivity. High selectivity towards paralytic shellfish toxins was observed in the presence of inorganic cations with selectivity coefficients ranging from 0.04 to 0.001 for Na+ and K+ and 3.6*10−4 to 3.4*10−5 for Ca2+. Detection limits were in the range from 0.25 to 0.9 μmolL−1 for saxitoxin and decarbamoyl saxitoxin, and from 0.08 to 1.8 μmolL−1 for GTX5 and C1&C2, which allows toxin detection at the concentration levels corresponding to the legal limits. Characteristics of the developed sensors allow their use in the electronic tongue multisensor system for simultaneous quantification of paralytic shellfish toxins.

Zinc Oxide-Based Self-Powered Potentiometric Chemical Sensors for Biomolecules and Metal Ions.

Advances in the miniaturization and portability of the chemical sensing devices have always been hindered by the external power supply problem, which has focused new interest in the fabrication of self-powered sensing devices for disease diagnosis and the monitoring of analytes. This review describes the fabrication of ZnO nanomaterial-based sensors synthesized on different conducting substrates for extracellular detection, and the use of a sharp borosilicate glass capillary (diameter, d = 700 nm) to grow ZnO nanostructures for intracellular detection purposes in individual human and frog cells. The electrocatalytic activity and fast electron transfer properties of the ZnO materials provide the necessary energy to operate as well as a quick sensing device output response, where the role of the nanomorphology utilized for the fabrication of the sensor is crucial for the production of the operational energy. Simplicity, design, cost, sensitivity, selectivity and a quick and stable response are the most important features of a reliable sensor for routine applications. The review details the extra- and intra-cellular applications of the biosensors for the detection and monitoring of different metallic ions present in biological matrices, along with the biomolecules glucose and cholesterol.

Measurements of the effects of wine maceration with oak chips using an electronic tongue.

The use of oak products as a cheaper alternative to expensive wood barrels was recently permitted in Europe, which led to a continuous increase in the use of oak chips and staves in winemaking. The feasibility of the potentiometric electronic tongue as a tool for monitoring the effects of wine maceration with oak chips was evaluated. Four types of commercially available oak chips subjected to different thermal treatments and washing procedures and their mixture were studied. Ethanolic extracts of the chips were analysed using electrospray mass spectrometry and 28 phenolic and furanic compounds were identified. The electronic tongue comprising 22 potentiometric chemical sensors could distinguish artificial wine solutions and Cabernet Sauvignon wine macerated with different types of oak chips, quantify total and non-flavonoid phenolic content, as well as the concentrations of added oak chips. Using measurements at two pH levels, 3.2 and 6.5, improved the accuracy of quantification.

Development of a Potentiometric Chemical Sensor for the Rapid Detection of Carbofuran Based on Air Stable Lipid Films with Incorporated Calix[4]arene Phosphoryl Receptor Using Graphene Electrodes

AbstractThe present article describes a miniaturized potentiometric carbofuran chemical sensor on graphene nanosheets with incorporated lipid films. The graphene electrode was used for the development of a very selective and sensitive chemical sensor for the detection of carbofuran by immobilizing an artificial selective receptor on stabilized lipid films. The artificial receptor was synthesized by transformation of the hydroxyl groups of resorcin[4]arene receptor into phosphoryl groups. This chemical sensor responded for the wide range of carbofuran concentrations with fast response time of ca. 20 s. The presented potentiometric carbofuran chemical sensor is easy to construct and exhibits good reproducibility, reusability, selectivity, rapid response times, long shelf life and high sensitivity of ca. 59 mV/decade over the carbofuran logarithmic concentration range from 10−6 to 10−3 M.

Astringency quantification in wine: comparison of the electronic tongue and FT-MIR spectroscopy

Electronic tongue based on potentiometric chemical sensors and Fourier transform mid-infrared spectroscopy (FT-MIR) were evaluated as rapid tools for the prediction of gelatin index of red, white and rose wines. Gelatin index was measured using modified procedure, which was applicable not only to red but also to white and rose wines. Relationship between gelatin index and a set of chemical parameters, electronic tongue and FT-MIR data have been investigated using multi-block analysis method called common component and specific weight analysis (CCSWA). Calibration models for gelatin index prediction were calculated using partial least square (PLS) regression. Variable selection for the PLS models was done using Variable Importance in Prediction criterion. Quantification of gelatin index in red and rose wine was possible using both electronic tongue and FT-MIR data with adjusted R2 in prediction of 0.75 and 0.89 for rose and 0.83 and 0.87 for red wine, respectively.

Tracing floral and geographical origins of honeys by potentiometric and voltammetric electronic tongue

A potentiometric electronic tongue (PE-tongue) and a voltammetric electronic tongue (VE-tongue) were used as rapid techniques to classify and predict the honey samples from different floral and geographical origins. The PE-tongue, which was named α-ASTREE, was developed by Alpha M.O.S. (Toulouse, France), and it comprises seven potentiometric chemical sensors. The VE-tongue was self-developed at Zhejiang University and comprises six metallic working sensors. Four types of honey of different floral origins (acacia, buckwheat, data, and motherwort) and four types of acacia honey of different geographical origins were classified by both multisensor systems. Multivariate statistical data analysis techniques such as principal component analysis (PCA) and discriminant function analysis (DFA) were used to classify the honey samples. Both types of electronic tongue have good potential to classify the honey samples, and the positions of the data point for the samples in the PCA score plots based on the VE-tongue were much more closely grouped. Three regression modes, principal component regression (PCR), partial least squares regression (PLSR), and least squared-support vector machines (LS-SVM), were applied for category forecasting. These regression models exhibited a clear indication of the prediction ability of the two types of electronic tongue, and a positive trend in the prediction of the floral and geographical origin of honey was found. Moreover, the performance of these regression models for predicting the four types of honey of different geographical origins by the VE-tongue is very stable.

Detection of copper, lead, cadmium and iron in wine using electronic tongue sensor system

An array of 10 potentiometric chemical sensors has been applied to the detection of total Fe, Cu, Pb and Cd content in digested wine. As digestion of organic matter of wine is necessary prior to the trace metal detection using potentiometric sensors, sample preparation procedures have been optimized. Different variants of wet and microwave digestion and dry ashing, 14 conditions in total, have been tested. Decomposition of organic matter was assessed using Fourier transform mid-infrared spectroscopy and total phenolic content. Dry ashing was found to be the most effective method of wine digestion. Measurements with sensors in individual solutions of Fe(III), Cu(II), Pb(II) and Cd(II) prepared on different backgrounds have shown that their detection limits were below typical concentration levels of these metals in wines and, in the case of Cu, Pb and Cd below maximum allowed concentrations. Detection of Fe in digested wine samples was possible using discrete iron-sensitive sensors with chalcogenide glass membranes with RMSEP of 0.05mmolL−1 in the concentration range from 0.0786 to 0.472mmolL−1. Low concentration levels of Cu, Pb and Cd in wine and cross-sensitivity of respective sensors resulted in the non-linearity of their responses, requiring back-propagation neural network for the calibration. Calibration models have been calculated using measurements in the model mixed solutions containing all three metals and a set of digested wine sample. RMSEP values for Cu, Pb and Cd were 3.9, 39 and 1.2μmolL−1 in model solutions and 2, 150 and 1μmolL−1 in digested wine samples.

G3 Assisted Rational Design of Chemical Sensor Array Using Carbonitrile Neutral Receptors

Combined computational and experimental strategies for the systematic design of chemical sensor arrays using carbonitrile neutral receptors are presented. Binding energies of acetonitrile, n-pentylcarbonitrile and malononitrile with Ca(II), Mg(II), Be(II) and H+ have been investigated with the B3LYP, G3, CBS-QB3, G4 and MQZVP methods, showing a general trend H+ > Be(II) > Mg(II) > Ca(II). Hydrogen bonding, donor-acceptor and cation-lone pair electron simple models were employed in evaluating the performance of computational methods. Mg(II) is bound to acetonitrile in water by 12.5 kcal/mol, and in the gas phase the receptor is more strongly bound by 33.3 kcal/mol to Mg(II) compared to Ca(II). Interaction of bound cations with carbonitrile reduces the energies of the MOs involved in the proposed σ-p conjugated network. The planar malononitrile-Be(II) complex possibly involves a π-network with a cationic methylene carbon. Fabricated potentiometric chemical sensors show distinct signal patterns that can be exploited in sensor array applications.