A simple, fast, and low-cost pre-concentration methodology based on the application of multi-template molecularly imprinted polymers (mt-MIP) in a solid-phase extraction system coupled with capillary electrophoresis was developed for the determination of naproxen, diclofenac, and ibuprofen in environmental water samples. A systematic study of the mt-MIP composition was conducted using a second-order simplex lattice experiment design (fraction of the functional monomer methacrylic acid (MAA), the total moles of functional monomers, and the total moles of the cross-linker agent). The optimal mt-MIP, consisting of 0.025 mmol of each analyte, with 2.40 mmol of methacrylic acid (MAA) and 3.60 mmol of 4-vinylpyridine (4VP) and 23.00 mmol of the cross-linker agent (EGDMA), was coupled to an SPE system under the optimal conditions: pH = 3.5; 20 mg of mt-MIP; and an eluent (MeOH/NaOH [0.001]). This methodology provides limits of detection from 3.00 to 12.00 µg L⁻¹ for the studied NSAIDs. The methodology’s precision was evaluated in terms of inter- and intra-day repeatability, with %RSD < 10% in all cases. Finally, the proposed method can be successfully applied in the analysis of environmental water samples (bottle, tap, cistern, well, and river water samples), which demonstrates the developed method’s robustness.

Glucose is the principal source of energy for humans and its quantification in physiological samples can diagnose or prevent diseases. Commonly, glucose determination is based on spectrophotometric-enzymatic techniques, but since at least a decade ago, electroanalytical strategies have emerged as promising alternatives providing accuracy and precision in the determination of biomolecules. This review focuses on the development of non-enzymatic methodologies based on modified electrochemical sensors with molecularly imprinted polymers (MIPs) for glucose detection sensors in physiological samples (blood, saliva, and urine). The trends in the construction of non-enzymatic sensors base on MIP combine with materials such as carbonaceous materials, metal nanoparticles, and polymers improving their electrocatalytic properties and analytical parameters of the electro-analytical methodologies developed. Glassy carbon electrodes, carbon paste electrodes, and screen-printed electrodes are the main transductors modified with MIP for the electrochemical oxidation of glucose, and the maximum anodic peak current is taken to the analytical signal. In all reported non-enzymatic sensors, the presence of the MIP improved glucose determination compared to the bare working electrode. The reported results demonstrated that this electroanalytical approach represents a viable alternative for fast and confident analysis of the glucose molecule overcoming the drawbacks presented by enzymatic sensors.

Aflatoxin B1 (AFB₁) is one of the most dangerous mycotoxins found in food, necessitating the development of precise and reliable methodologies for its detection. In this study, a novel electrochemical sensor based on a molecularly imprinted polymer (MIP) integrated with a carbon-paste electrode was developed for the voltammetric determination of AFB₁. The innovative aspect of this work lies in the use of methacrylic acid (MAA) as the functional monomer, which enhances the sensor’s selectivity and binding affinity. The developed electrochemical sensor exhibited a linear response range from 20.8 to 80 ng/L, with a limit of detection (LOD) of 2.31 ng/L and a sensitivity of 19.83 µA (ng/L)⁻¹ cm⁻². The sensor demonstrated outstanding analytical performance, with reproducibility and repeatability yielding relative standard deviations (RSDs) of 3.24% and 1.41%, respectively. To validate the sensor’s practical applicability, its performance was tested in real samples of corn and wheat using the standard addition method. Samples were prepared following official Mexican standard methods. Detected AFB₁ concentrations were 0.0147 μg/L and 0.0138 μg/L for corn and wheat, respectively. A statistical comparison using the Student’s t-test confirmed no significant matrix effects, underscoring the high selectivity and accuracy of the MIP-modified sensor. This work introduces a highly selective, sensitive, and reproducible methodology for AFB₁ detection, which could significantly advance food safety monitoring.

En este artículo se discuten algunos de los métodos de análisis químico más utilizados actualmente para cuantificar principios activos (fármacos) y otras sustancias presentes en los medicamentos comerciales. La importancia de realizar estas cuantificaciones se basa en lo que se llama el “control de calidad de los medicamentos”, en el que se determinan las cantidades de los principios activos en estos para que puedan considerarse seguros y eficaces en el tratamiento de pacientes. Otro punto importante es que las técnicas y metodologías químicas usadas en control de calidad farmacéutico, también pueden usarse en el control de las cantidades de fármacos que se incorporan a los cuerpos de agua al momento de que son desechados por el organismo o como residuos de la misma industria farmacéutica, hospitales y hasta centros de investigación. Esto es de vital importancia para el cuidado del medio ambiente, dado que algunos medicamentos se usan ampliamente y causan problemas ecológicos.

Sodium diclofenac is a widely used anti-inflammatory drug that can cause heart diseases if consumed constantly in high doses. Consequently, it is essential to have strict control of the amounts of this active principle in pharmaceutical products. The combination of electroanalytical techniques with advanced chemometrics has risen as a viable alternative for the exact and precise determination of active principles even in the presence of chemical interferences. In this research, an artificial neural network (ANN) for the voltammetric quantification of diclofenac in the presence of paracetamol, pyridoxine, and caffeine is presented, using a carbon paste electrode modified with multilayer carbon nanotubes and titanium dioxide nanoparticles. Cyclic voltammetry is performed to study the effect of the interferences on diclofenac response. Subsequently, a set of diclofenac standards and interferents was prepared using a fractional factorial design to build the response model and perform differential pulse voltammetry to produce the data of the input layer of the ANN. The ANN developed was able to predict the concentration of diclofenac even in the presence of the interferences, since multiple correlation coefficients of 0.9917 and 0.8387 were obtained for training and test data in the analysis of pharmaceutical samples with a recovery percentage of 95.9%.

This work describes the development of an electroanalytical methodology for the indirect determination of aflatoxin B1 using a non-enzymatic sensor based on a carbon paste electrode (CPE) modified with copper oxide nanoparticles (CuONPs). The electrochemical characterization of the proposed sensor was performed by cyclic voltammetry (CV). An intense anodic peak associated with the oxidation of CuO to CuOOH was observed, which is favored under the working conditions. In the presence of Aflatoxin B1 (AFB1), a passivation of this anodic peak was observed, directly related to the AFB1 concentration. This indicates that AFB1 interferes with electron transfer at the electrode surface required for CuO oxidation, enabling its indirect determination. Therefore, the difference in anodic peak current intensities between a blank solution and an AFB1-containing solution was established as the analytical signal. To maximize this signal, CV was used to optimize parameters such as pH, CuO percentage, and scan rate. The quantification was performed by CV. The optimized electrochemical method presented linearity and sensitivity, with statistically determined detection and quantification limits of 10.075 ng L-1 and 30.532 ng L-1, respectively. The methodology was successfully applied to determine AFB1 in corn and peanut samples using the standard addition method, yielding satisfactory recovery percentages.