Comparison of methods for the processing of voltammetric electronic tongues data

Towards the development of a portable device based on modified-voltammetric sensors for the detection of illicit drugs and seized samples

Illicit drugs are a global burden, not only for society, but also for the various control authorities for which its rapid on-site detection remains a challenge. In this context, the potential of a voltammetric electronic tongue (ET) for the analysis of different drugs is evaluated herein. Concretely, the discrimination and identification of cocaine, heroin, 3,4-Methylenedioxymethamphetamine (MDMA), methamphetamine and ketamine in self-prepared and real samples were attempted. For its analysis, an array of three carbon-based screen-printed electrodes (SPE) was prepared, and their responses towards the different drugs and some of the more common cutting agents and adulterants were assessed by means of square wave voltammetry (SWV). To this aim, a tiny amount (ca. 3 mg) of the drug powder was added to the electrochemical cell containing phosphate buffer (pH 12), shaken, and measured directly without any other pre-treatment than its dilution. Next, to identify their characteristic fingerprint, obtained voltammograms were submitted to linear discriminant analysis (LDA), which allowed to correctly identify the different drugs regardless of the presence of the different cutting agents and other possible interfering compounds, or their concentration. Satisfactory results were obtained both for the synthetic and the "street" seized samples, with a classification rate of 100 % for the external test subset of the latter (n = 10).

Towards the rapid detection of haze-forming proteins

Protein haze in white wine can be a serious quality defect because consumers perceive hazy wines as "spoiled". Unfortunately, a specific method for the detection, or selective treatment, of such proteins in affected wines does not exist. Herein we investigate on the development of an easy-to-use sensor device that allows detection of haze-forming proteins (HFPs). Such a device is expected to overcome the limitations of the "heat test" currently used to assess the protein content in wine and the amount of bentonite needed to remove such proteins. To this aim, three different approaches were explored. Firstly, an impedimetric immunosensor against chitinases was developed and its performance assessed. Secondly, the exploitation of the dual role of HFPs as biorecognition element and analyte to develop an impedimetric biosensor was evaluated, in what can be considered a very unique strategy, representing a new paradigm in biosensing. Lastly, Fourier transform infrared (FT-IR) spectra were collected for various wine samples and chemometric tools such as discrete wavelet transform (DWT) and artificial neural networks (ANNs) were used to achieve the quantification of HFPs. Detection of HFPs at the μg/L level was achieved with both impedimetric biosensors, whereas the FT-IR-based approach allowed their quantification at the mg/L level in wine samples directly. The sensitivity of the developed methods may enable the rapid assessment of wine protein content.

Dual Microfluidic Sensor System for Enriched Electrochemical Profiling and Identification of Illicit Drugs On-Site

Electrochemical sensors have emerged as a new analytical tool for illicit drug detection to facilitate ultrafast and accurate identification of suspicious compounds on-site. Drugs of abuse can be identified using their unique voltammetric fingerprint at a given pH. Today, the right buffer solution is manually selected based on drug appearance, and in some cases, a consecutive analysis in two different pH solutions is required. In this work, we present a disposable microfluidic multichannel sensor system that automatically records fingerprints in two pH solutions (e.g., pH 5 and pH 12). This system has two advantages. It will overcome the manual selection of a buffer solution at the right pH, decrease analysis time, and minimize the risk of human errors. Second, the combination of two fingerprints, the superfingerprint, contains more detailed information about the samples, which enhances the selectivity of the analytical technique. First, real-time pH measurements proved that the sample can be brought to the desired pH within a minute. Subsequently, an electrochemical study on the microfluidic platform with 1 mM illicit drug standards of MDMA, cocaine, heroin, and methamphetamine showed that the characteristic voltammetric fingerprints and peak potentials are reproducible, also in the presence of common cutting agents. Finally, the microfluidic concept was validated with real confiscated samples, showing promising results for the user-friendly identification of drugs of abuse. In short, this paper presents a successful proof-of-concept study of a multichannel microfluidic sensor system to enrich the fingerprints of illicit drugs at pH 5 and pH 12, thus providing a low-cost, portable, and rapid identification system of illicit drugs with minimal user intervention.

A priori tailored selection of sensor arrays for electronic tongues

Electronic tongues (ETs) are multisensor systems based on the coupling of different sensors with high stability and cross-sensitivity towards different species, along with an appropriate chemometric tool that allow the classification of samples and/or the quantification of selected analytes. Despite the great advantages that the data processing stage may offer (e.g. allowing to offset matrix effects from the sample itself, or to resolve the presence of interferences, drifts or non-linearities obtained with the sensors), the stability of the sensors response together with its varied cross-sensitivity are of utmost importance. In this regard, despite the progress that has been made over the last years and the increasing number of publications dealing with ETs, researchers have focused mainly on the developed application itself or on the data treatment stage, but very few report on the choice of the sensors. In this direction, herein we explore on the development of a simple methodology that allows the a priori selection of the optimal sensor array to carry out a specific application. Concretely, the usage of principal component analysis (PCA) in combination with different clustering indices is proposed as a simple approach to simultaneously assess the cross-response and the reproducibility of the different sensors in a single step, what allows to define which sensors may constitute an ET array for a given qualitative or quantitative application. The above ideas have been applied to the study case of aromatic essential oils.

Electronic tongue applications for wastewater and soil analysis

Assessment of water and soil quality is critical for the health, economy, and sustainability of any community. The release of a range of life-threatening pollutants from agriculture, industries, and the residential communities themselves into the different water resources and soil requires of analytical methods intended for their detection. Given the challenge that represents coping with the monitoring of such a diverse and large number of compounds (with over 100,000 chemicals registered, yet in continuous increase), holistic solutions such as electronic tongues (ETs) are emerging as a promising tool for a sustainable, simple, and green monitoring of soil and water resources. In this direction, this review aims to present and critically provide an overview of the basic concepts of ETs, followed by some relevant applications recently reported in the literature in environmental analysis, more specifically, the monitoring of water and wastewater, their quality and the detection of water pollutants as well as soil analysis.

A novel electronic tongue using electropolymerized molecularly imprinted polymers for the simultaneous determination of active pharmaceutical ingredients

The combination of chemometrics and electrochemical sensors modified with molecularly imprinted polymers (MIPs) towards the development of MIP-based electronic tongues (ETs) was explored herein. To demonstrate the potential of such an approach, the simultaneous determination of paracetamol, ascorbic acid and uric acid mixtures in pharmaceutical samples was evaluated. To this aim, MIP-based sensors for the different compounds were prepared by in situ electropolymerization of pyrrole in the presence of p-toluenesulfonate anion (pTS^-), which acted as functional doping ion of the polypyrrole (PPy) MIP backbone. Morphological characterization of the MIPs was done by scanning electron microscopy (SEM), while functionalization of the electrodes was monitored electrochemically. Under the optimized measuring conditions, the developed sensors showed a good performance, with good linearity at the μM level (R² > 0.992, limits of detection between 1 and 24 μM) as well as good repeatability (intra- and inter-day RSD values between 3 and 6% over 30 consecutive measurements). Finally, the quantification of the individual substances in different pharmaceutical samples was achieved by an artificial neural networks (ANNs) model, showing satisfactory agreement between expected and obtained values (R² > 0.987).

AI powered electrochemical multi-component detection of insulin and glucose in serum

Multi-component detection of insulin and glucose in serum is of great importance and urgently needed in clinical diagnosis and treatment due to its economy and practicability. However, insulin and glucose can hardly be determined by traditional electrochemical detection methods. Their mixed oxidation currents and rare involvement in the reaction process make it difficult to decouple them. In this study, AI algorithms are introduced to power the electrochemical method to conquer this problem. First, the current curves of insulin, glucose, and their mixed solution are obtained using cyclic voltammetry. Then, seven features of the cyclic voltammetry curve are extracted as characteristic values for detecting the concentrations of insulin and glucose. Finally, after training using machine learning algorithms, insulin and glucose concentrations are decoupled and regressed accurately. The entire detection process only takes three minutes. It can detect insulin at the pmol level and glucose at the mmol level, which meets the basic clinical requirements. The average relative error in predicting insulin concentrations is around 6.515%, and that in predicting glucose concentrations is around 4.36%. To verify the performance and effectiveness of the proposed method, it is used to determine the concentrations of insulin and glucose in fetal bovine serum and real clinical serum samples. The results are satisfactory, demonstrating that the method can meet basic clinical needs. This multi-component testing system delivers acceptable detect limit and accuracy and has the merits of low cost and high efficiency, holding great potential for use in clinical diagnosis.

Optimization of Sensors to be Used in a Voltammetric Electronic Tongue Based on Clustering Metrics

Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.

Voltammetric electronic tongue for vinegar fingerprinting

The application of a voltammetric electronic tongue (ET) towards the classification and authentication of vinegar is reported. Vinegar samples of different varieties were analysed with a three-sensor array, without performing any sample pre-treatment, but only an electrochemical cleaning stage between sample measurements to avoid fouling onto the electrode surfaces. Next, the use of discrete cosine transform (DCT) for the compression and reduction of signal complexity in voltammetric measurements was explored, and the number of coefficients was optimized through its inverse transform. Finally, the obtained coefficients were analysed by principal component analysis (PCA) to attempt the discrimination of the different vinegars and by linear discriminant analysis (LDA) to build a model that allows its categorization. Satisfactory results were obtained overall, with a classification rate of 100% for the external test subset (n = 15).

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.

Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika's phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

Joint Voltammetry Technology with a Multi-electrode Array for Four Basic Tastes

Background:

Rapid and easy technology which can mimic the tongue for the simultaneous perception of several tastes based on sensory analysis and mathematical statistics is sorely needed.

Methods:

Joint voltammetry technology was developed to qualitatively and quantitatively analyze four basic tastes namely sweetness, saltiness, sourness and bitterness with the multi-electrode array. Four taste stimuli were corresponded to four tastes. Cyclic Voltammetry (CV), Differential Pulse Voltammetry (DPV) and Square Wave Voltammetry (SWV) were employed. The original voltammetric signals were transformed by Continuous Wavelet Transform (CWT) in order to reveal more feature information for sensing taste stimuli. Joint voltammetry was applied via the combination of voltammetry. The data of feature points from the transformed signal as the input were used for neural network model.

Results:

Layer-Recurrent neural network (LRNN) could effectively identify the types of stimuli. The accuracies of the training set and test set by joint voltammetry were both higher than that of regular voltammetry, confirming that Back Propagation neural network (BPNN) could quantitatively predict single taste stimulus of the mixture.

Conclusion:

Joint voltammetry technology had a strong ability to sense basic tastes as human tongue.

Characterization and classification of Spanish paprika (Capsicum annuum L.) by liquid chromatography coupled to electrochemical detection with screen-printed carbon-based nanomaterials electrodes

Screen-printed electrodes based on graphite, carbon nanotubes, carbon nanofibers, and graphene were tested as amperometric detectors for the determination of phenolic compounds by high performance liquid chromatography (HPLC). The chromatographic performance as well as the obtained sensitivity, detection and quantification limits suggest that carbon nanofibers modified screen-printed electrode (SPCE-CNF) is the amperometric sensor that provides the best analytical performance. Upon this confirmation, chromatographic data obtained using SPCE-CNF were exploited by means of linear discriminant analysis (LDA) to successfully characterize and classify 96 Spanish paprika (Capsicum annuum L.) samples with different origin and type: from La Vera (including sweet, bittersweet and spicy types) and from Murcia (including sweet and spicy types).

Electronic tongues to assess wine sensory descriptors

This work reports the application of an electronic tongue as a tool towards the analysis of wine in tasks such as its discrimination based on the maturing in barrels or the prediction of the global scores assigned by a sensory panel. To this aim, red wine samples were first analysed with the voltammetric sensor array, without performing any sample pretreatment. Afterwards, obtained responses were preprocessed employing fast Fourier transform (FFT) for the compression and reduction of signal complexity, and obtained coefficients were then used as inputs to build the qualitative and quantitative models employing either linear discriminant analysis (LDA) or partial least squares regression (PLS), respectively. Satisfactory results were obtained overall, with a classification rate of 100% in the discrimination of the type of barrel used during wine maturing, a normalized NRMSE of 0.077 in the estimation of ageing time (months) or 0.11 in the prediction of the scores (0-10) from a trained sensory panel (all for the external test subset).

Visualized attribute analysis approach for characterization and quantification of rice taste flavor using electronic tongue

This paper deals with a novel visualized attributive analysis approach for characterization and quantification of rice taste flavor attributes (softness, stickiness, sweetness and aroma) employing a multifrequency large-amplitude pulse voltammetric electronic tongue. Data preprocessing methods including Principal Component Analysis (PCA) and Fast Fourier Transform (FFT) were provided. An attribute characterization graph was represented for visualization of the interactive response in which each attribute responded by specific electrodes and frequencies. The model was trained using signal data from electronic tongue and attribute scores from artificial evaluation. The correlation coefficients for all attributes were over 0.9, resulting in good predictive ability of attributive analysis model preprocessed by FFT. This approach extracted more effective information about linear relationship between electronic tongue and taste flavor attribute. Results indicated that this approach can accurately quantify taste flavor attributes, and can be an efficient tool for data processing in a voltammetric electronic tongue system.

Electronic tongue for nitro and peroxide explosive sensing

This work reports the application of a voltammetric electronic tongue (ET) towards the simultaneous determination of both nitro-containing and peroxide-based explosive compounds, two families that represent the vast majority of compounds employed either in commercial mixtures or in improvised explosive devices. The multielectrode array was formed by graphite, gold and platinum electrodes, which exhibited marked mix-responses towards the compounds examined; namely, 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), pentaerythritol tetranitrate (PETN), 2,4,6-trinitrotoluene (TNT), N-methyl-N,2,4,6-tetranitroaniline (Tetryl) and triacetone triperoxide (TATP). Departure information was the set of voltammograms, which were first analyzed by means of principal component analysis (PCA) allowing the discrimination of the different individual compounds, while artificial neural networks (ANNs) were used for the resolution and individual quantification of some of their mixtures (total normalized root mean square error for the external test set of 0.108 and correlation of the obtained vs. expected concentrations comparison graphs r>0.929).

Bioelectronic tongues: New trends and applications in water and food analysis

Over the last years, there has been an increasing demand for fast, highly sensitive and selective methods of analysis to meet new challenges in environmental monitoring, food safety and public health. In response to this demand, biosensors have arisen as a promising tool, which offers accurate chemical data in a timely and cost-effective manner. However, the difficulty to obtain sensors with appropriate selectivity and sensitivity for a given analyte, and to solve analytical problems which do not require the quantification of a certain analyte, but an overall effect on a biological system (e.g. toxicity, quality indices, provenance, freshness, etc.), led to the concept of electronic tongues as a new strategy to tackle these problems. In this direction, to improve the performance of electronic tongues, and thus to spawn new application fields, biosensors have recently been incorporated to electronic tongue arrays, leading to what is known as bioelectronic tongues. Bioelectronic tongues provide superior performance by combining the capabilities of electronic tongues to derive meaning from complex or imprecise data, and the high selectivity and specificity of biosensors. The result is postulated as a tool that exploits chemometrics to solve biosensors' interference problems, and biosensors to solve electronic tongues' selectivity problems. The review presented herein aims to illustrate the capabilities of bioelectronic tongues as analytical tools, especially suited for screening analysis, with particular emphasis in water analysis and the characterization of food and beverages. After briefly reviewing the key concepts related to the design and principles of electronic tongues, we provide an overview of significant contributions to the field of bioelectronic tongues and their future perspectives.

Discrimination of Rice with Different Pretreatment Methods by Using a Voltammetric Electronic Tongue

In this study, an application of a voltammetric electronic tongue for discrimination and prediction of different varieties of rice was investigated. Different pretreatment methods were selected, which were subsequently used for the discrimination of different varieties of rice and prediction of unknown rice samples. To this aim, a voltammetric array of sensors based on metallic electrodes was used as the sensing part. The different samples were analyzed by cyclic voltammetry with two sample-pretreatment methods. Discriminant Factorial Analysis was used to visualize the different categories of rice samples; however, radial basis function (RBF) artificial neural network with leave-one-out cross-validation method was employed for prediction modeling. The collected signal data were first compressed employing fast Fourier transform (FFT) and then significant features were extracted from the voltammetric signals. The experimental results indicated that the sample solutions obtained by the non-crushed pretreatment method could efficiently meet the effect of discrimination and recognition. The satisfactory prediction results of voltammetric electronic tongue based on RBF artificial neural network were obtained with less than five-fold dilution of the sample solution. The main objective of this study was to develop primary research on the application of an electronic tongue system for the discrimination and prediction of solid foods and provide an objective assessment tool for the food industry.

Electronic tongue formed by sensors and biosensors containing phthalocyanines as electron mediators: Application to the analysis of red grapes

An electronic tongue formed by voltammetric sensors and biosensors containing phthalocyanines has been developed and used to analyze grapes of different varieties. The sensors are prepared using the carbon paste technique and have been chemically modified with different metallophthalocyanines. In turn, biosensors consist of carbon paste electrodes modified with phthalocyanines combined with tyrosinase or glucose oxidase. The response of the individual sensors towards model solutions of glucose and catechol have demonstrated that the voltammetric responses depend on the nature of the phthalocyanine, evidencing the important role of the electron mediator in the performance of the sensors. The capability of the system to discriminate grapes according to their sugar and their polyphenolic content has been evidenced using Principal Component Analysis. It has been demonstrated that the proposed array of sensors combines the advantages of classical phthalocyanine based sensors — that provide global information about the sample —, with the specificity of the enzyme substrate reaction typical of biosensors. For this reason, the selectivity of the multisensor system and its capability of discrimination is clearly improved when biosensors containing glucose oxidase or tyrosinase are included in the array.

A combination of dynamic measurement protocol and advanced data treatment to resolve the mixtures of chemically similar analytes with potentiometric multisensor system

Data processing techniques and measuring protocol are very important parts of the multisensor systems methodology. Complex analytical tasks like resolving the mixtures of two components with very similar chemical properties require special attention. We report on the application of non-linear (artificial neural networks, ANNs) and linear (projections on latent structures, PLS) regression techniques to the data obtained from the flow cell with potentiometric multisensor detection of neighouring lanthanides in the Periodic System of the elements (samarium, europium and gadolinium). Quantification of individual components in mixtures is possible with reasonable precision if dynamic components of the response are incorporated thanks to the use of an automated sequential injection analysis system. The average absolute error in prediction of lanthanides with PLS was around 1 × 10(-4)mol/L, while the use of ANNs allows the lowering of prediction errors down to 2 × 10(-5)mol/L in certain cases. The suggested protocol seems to be useful for other analytical applications where simultaneous determination of chemically similar analytes in mixtures is required.