Molecularly imprinted polymers (MIPs) are artificially synthesized materials to mimic the molecular recognition process of biological macromolecules such as substrate-enzyme or antigen-antibody. The combination of these biomimetic materials with electrochemical techniques has allowed the development of advanced sensing devices, which significantly improve the performance of bare or catalyst-modified sensors, being able to unleash new applications. However, despite the high selectivity that MIPs exhibit, those can still show some cross-response towards other compounds, especially with chemically analogous (bio)molecules. Thus, the combination of MIPs with chemometric methods opens the room for the development of what could be considered a new type of electronic tongues, i.e. sensor array systems, based on its usage. In this direction, this review provides an overview of the more common synthetic approaches, as well as the strategies that can be used to achieve the integration of MIPs and electrochemical sensors, followed by some recent examples over different areas in order to illustrate the potential of such combination in very diverse applications.

Magnetic separation based on biologically-modified magnetic particles is a preconcentration procedure commonly integrated in magneto actuated platforms for the detection of a huge range of targets. However, the main drawback of this material is the low stability and high cost. In this work, a novel hybrid molecularly-imprinted polymer with magnetic properties is presented with affinity towards biotin and biotinylated biomolecules. During the synthesis of the magneto core-shell particles, biotin was used as a template. The characterization of this material by microscopy techniques including SEM, TEM and confocal microscopy is presented. The application of the magnetic-MIPs for the detection of biotin and biotinylated DNA in magneto-actuated platforms is also described for the first time. The magnetic-MIP showed a significant immobilization capacity of biotinylated molecules, giving rise to a cheaper and a robust method (it is not required to be stored at 4°C) with high binding capacity for the separation and purification under magnetic actuation of a wide range of biotinylated molecules, and their downstream application including determination of their specific targets.

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The presented manuscript reports the simultaneous detection of a ternary mixture of the benzodiazepines diazepam, lorazepam, and flunitrazepam using an array of voltammetric sensors and the electronic tongue principle. The electrodes used in the array were selected from a set of differently modified graphite epoxy composite electrodes; specifically, six electrodes were used incorporating metallic nanoparticles of Cu and Pt, oxide nanoparticles of CuO and WO3, plus pristine electrodes of epoxy-graphite and metallic Pt disk. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Multivariate examination using Principal Component Analysis (PCA) justified the choice of sensors in order to get the proper discrimination of the benzodiazepines. Next, a quantitative model to predict the concentrations of mixtures of the three benzodiazepines was built employing the set of voltammograms, and was first processed with the Discrete Wavelet Transform, which fed an artificial neural network response model. The developed model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 0.034 and 0.106 for the training and test subsets, respectively, and coefficient of correlation R ≥ 0.938 in the predicted vs. expected concentrations comparison graph.

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This work reports a rapid, simple and low-cost voltammetric sensor based on a dummy molecularly imprinted polymer (MIP) that uses 2,4-dinitrophenol (DNP) as a template for the quantification of 2,4,6-trinitrotoluene (TNT) and DNP, and the identification of related substances. Once the polymer was synthesised by thermal precipitation polymerisation, it was integrated onto a graphite epoxy composite (GEC) electrode via sol–gel immobilisation. Scanning electron microscopy (SEM) was performed in order to characterise the polymer and the sensor surface. Responses towards DNP and TNT were evaluated, displaying a linear response range of 1.5 to 8.0 µmol L−1 for DNP and 1.3 to 6.5 µmol L−1 for TNT; the estimated limits of detection were 0.59 µmol L−1 and 0.29 µmol L−1, for DNP and TNT, respectively. Chemometric tools, in particular principal component analysis (PCA), demonstrated the possibilities of the MIP-modified electrodes in nitroaromatic and potential interfering species discrimination with multiple potential applications in the environmental field.

Trinitrotoluene (TNT) is a widely employed explosive compound; for that reason, an electrochemical sensor able to perform on-field measurements could be an interesting tool. In this work, a molecularly imprinted polymer using the TNT analogue DNP as a template is developed. Next, the obtained MIP is chemically characterized towards DNP and TNT.MIPs synthesis was done following the protocol by co-precipitation using methacrylic acid (MAA) as a monomer, ethylene glycol ethylene glycol dimethylacrylate (EGDMA) as a crosslinker, azobisisobutyronitrile (AIBN) as a radical indiciator and ethanol as a solvent. Template removal was performed with a Soxhlet using MeOH:HAc. Control non-imprinted polymers (NIPs) were also synthesized for the purpose of comparison.Microscopy studies were performed to confirm similar morphologies among these polymers; the material was also characterized by a Scatchard plot to calculate the Kb (the affinity constant) and Bmax (maximum amount bound) values.The presented work reports a polymeric material able to capture TNT and DNP and its preliminary results once implemented as a recognition element in a voltammetric biosensor.

This work attempts the identification of the production year, the cultivar’s region and the aging method used in the elaboration of different Spanish red wines, all from the “tempranillo” grape variety. The identification of such characteristics relies on the use of a voltammetric electronic tongue (ET) system formed by modified graphite-epoxy electrodes (GEC) and metallic electrodes to collect a set of six voltammograms per sample, and different chemometric tools to accomplish the final identifications. A large sample set that included 199 different wine samples from commercial and own elaboration origin were analysed with the electronic tongue system, using the cyclic voltammetry technique and without any sample pre-treatment. To process the extremely complex and high-dimensionality generated data, a compression strategy was used for the acquired voltammograms, using discrete wavelet transform (DWT). This treatment reduced the information to ca. 10%, preserving significant features from the voltammetric signals. Compressed data was evaluated firstly by unsupervised methods, i.e., principal component analysis (PCA), without much success as it was found that such methods were unable to unravel the patterns contained within such complex data samples. Finally, the processed electrochemical information was evaluated by supervised methods to accomplish the proper identification; among those methods were linear discriminant analysis (LDA), supported vector machines (SVM) or artificial neural networks (ANN). The best results were obtained using artificial neural networks (ANNs), achieving 96.1% of correct classification for bottled year, 86.8% for elaboration region (protected designation of origin) and 98.6% for maturation type with or without use of wood barrel.