RESUMEN

Mefloquine (MQ) is an antimalarial medication prescribed to treat or malaria prevention.. When taken by children, vomiting usually occurs, and new doses of medication frequently need to be taken. So, developing pediatric medicines using taste-masked antimalarial drug complexes is mandatory for the success of mefloquine administration. The hypothesis that binding mefloquine to an ion-exchange resin (R) could circumvent the drug's bitter taste problem was proposed, and solid-state 13C cross-polarization magic angle spinning (CPMAS) NMR was able to follow MQ-R mixtures through chemical shift and relaxation measurements. The nature of MQ-R complex formation could then be determined. Impedimetric electronic tongue equipment also verified the resinate taste-masking efficiency in vitro. Variations in chemical shifts and structure dynamics measured by proton relaxation properties (e.g., T1ρH) were used as probes to follow the extension of mixing and specific interactions that would be present in MQ-R. A significant decrease in T1ρH values was observed for MQ carbons in MQ-R complexes, compared to the ones in MQ (from 100-200 ms in MQ to 20-50 ms in an MQ-R complex). The results evidenced that the cationic resin interacts strongly with mefloquine molecules in the formulation of a 1:1 ratio complex. Thus, 13C CPMAS NMR allowed the confirmation of the presence of a binding between mefloquine and polacrilin in the MQ-R formulation studied.

RESUMEN

Grains germinate, dry, and then undergo crushing before being combined with hot water to yield a sweet and viscous liquid known as wort. To enhance flavor and aroma compounds while maintaining a lower alcohol content, cold water is utilized during wort production without increasing its density. Recent years have witnessed a surge in demand for beverages with reduced alcohol content, reflecting shifting consumer preferences towards healthier lifestyles. Notably, consumers of low-alcohol beers seek products that closely mimic traditional beers. In response, batches of low-alcohol beer were meticulously crafted using a cold extraction method with room temperature water, resulting in a beer with 1.11% alcohol by volume (ABV). Sensory evaluations yielded a favorable score of 27 out of 50, indicating adherence to style standards and absence of major technical flaws. Furthermore, electronic taste profiling revealed a striking similarity between the low-alcohol beer and the benchmark International Pale Lager style, exemplified by commercial beers (5 and 0.03% ABV). Notably, the reduced-alcohol variant boasted lower caloric content compared to both standard and non-alcoholic counterparts. Consequently, the cold extraction approach emerges as a promising technique for producing low-alcohol beers within the International Pale Lager style, catering to evolving consumer preferences and health-conscious trends.

Asunto(s)

RESUMEN

As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.

Asunto(s)

RESUMEN

Self-healing materials inspire the next generation of multifunctional wearables and Internet of Things appliances. They expand the realm of thin film fabrication, enabling seamless conformational coverage irrespective of the shape complexity and surface geometry for electronic skins, smart textiles, soft robotics, and energy storage devices. Within this context, the layer-by-layer (LbL) technique is versatile for homogeneously dispersing materials onto various matrices. Moreover, it provides molecular level thickness control and coverage on practically any surface, with poly(ethylenimine) (PEI) and poly(acrylic acid) (PAA) being the most used materials primarily employed in self-healing LbL structures operating at room temperature. However, achieving thin film composites displaying controlled conductivity and healing ability is still challenging under ambient conditions. Here, PEI and PAA are mixed with conductive fillers (gold nanorods, poly(3,4-ethylene dioxythiophene): polystyrenesulfonate (PEDOT:PSS), reduced graphene oxides, and multiwalled carbon nanotubes) in distinct LbL film architectures. Electrical (AC and DC), optical (Raman spectroscopy), and mechanical (nanoindentation) measurements are used for characterizing composite structures and properties. A delicate balance among electrical, mechanical, and structural characteristics must be accomplished for a controlled design of conductive self-healing composites. As a proof-of-concept, four LbL composites were chosen as sensing units in the first reported self-healing e-tongue. The sensor can easily distinguish basic tastes at low molar concentrations and differentiate trace levels of glucose in artificial sweat. The formed nanostructures enable smart coverages that have unique features for solving current technological challenges.

RESUMEN

The use of sensors in different applications to improve the monitoring of a process and its variables is required as it enables information to be obtained directly from the process by ensuring its quality. This is now possible because of the advances in the fabrication of sensors and the development of equipment with a high processing capability. These elements enable the development of portable smart systems that can be used directly in the monitoring of the process and the testing of variables, which, in some cases, must evaluated by laboratory tests to ensure high-accuracy measurement results. One of these processes is taste recognition and, in general, the classification of liquids, where electronic tongues have presented some advantages compared with traditional monitoring because of the time reduction for the analysis, the possibility of online monitoring, and the use of strategies of artificial intelligence for the analysis of the data. However, although some methods and strategies have been developed, it is necessary to continue in the development of strategies that enable the results in the analysis of the data from electrochemical sensors to be improved. In this way, this paper explores the application of an electronic tongue system in the classification of liquor beverages, which was directly applied to an alcoholic beverage found in specific regions of Colombia. The system considers the use of eight commercial sensors and a data acquisition system with a machine-learning-based methodology developed for this aim. Results show the advantages of the system and its accuracy in the analysis and classification of this kind of alcoholic beverage.

Asunto(s)

RESUMEN

In this work, a conductive ink based on microfibrillated cellulose (MFC) and multiwalled carbon nanotubes (MWCNTs) was used to produce transducers for rapid liquid identification. The transducers are simple resistive devices that can be easily fabricated by scalable printing techniques. We monitored the electrical response due to the interaction between a given liquid with the carbon nanotube-cellulose film over time. Using principal component analysis of the electrical response, we were able to extract robust data to differentiate between the liquids. We show that the proposed liquid sensor can classify different liquids, including organic solvents (acetone, chloroform, and different alcohols) and is also able to differentiate low concentrations of glycerin in water (10-100 ppm). We have also investigated the influence of two important properties of the liquids, namely dielectric constant and vapor pressure, on the transduction of the MFC-MWCNT sensors. These results were corroborated by independent heat flow measurements (thermogravimetric analysis). The proposed MFC-MWCNT sensor platform may help paving the way to rapid, inexpensive, and robust liquid analysis and identification.

RESUMEN

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Asunto(s)

RESUMEN

The aim of this research is to develop burger patties from fungal protein. For this purpose, to maximize fungal biomass production, an optimization of the growth medium was initially carried out by testing different carbon sources and its proportion with nitrogen. Subsequently, for the design of the fungal patties, the effect of different flours, binders, and colorants on the properties of texture, water retention capacity, and color were tested, with a traditional animal-based burger patty as a control. Based on the first results, two optimal formulations were chosen and analyzed using an electronic tongue with the same control as reference. The conditions that maximized biomass production were 6 days of incubation and maltodextrin as a carbon source at a concentration of 90 g/L. In terms of product design, the formulation containing quinoa flour, carboxymethylcellulose, and beet extract was the most similar to the control. Finally, through shelf-life analysis, it was determined that the physical characteristics of the fungal meat substitute did not change significantly in an interval of 14 days. However, the product should be observed for a longer period. In addition, by the proximate analysis, it was concluded that fungal patties could have nutritional claims such as rich content in protein and fiber.

RESUMEN

The diagnosis of cancer and other diseases using data from non-specific sensors - such as the electronic tongues (e-tongues) - is challenging owing to the lack of selectivity, in addition to the variability of biological samples. In this study, we demonstrate that impedance data obtained with an e-tongue in saliva samples can be used to diagnose cancer in the mouth. Data taken with a single-response microfluidic e-tongue applied to the saliva of 27 individuals were treated with multidimensional projection techniques and non-supervised and supervised machine learning algorithms. The distinction between healthy individuals and patients with cancer on the floor of mouth or oral cavity could only be made with supervised learning. Accuracy above 80% was obtained for the binary classification (YES or NO for cancer) using a Support Vector Machine (SVM) with radial basis function kernel and Random Forest. In the classification considering the type of cancer, the accuracy dropped to ca. 70%. The accuracy tended to increase when clinical information such as alcohol consumption was used in conjunction with the e-tongue data. With the random forest algorithm, the rules to explain the diagnosis could be identified using the concept of Multidimensional Calibration Space. Since the training of the machine learning algorithms is believed to be more efficient when the data of a larger number of patients are employed, the approach presented here is promising for computer-assisted diagnosis.

Asunto(s)

RESUMEN

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

Asunto(s)

RESUMEN

The assessment of drug taste is crucial for pediatric treatments so that formulations can be developed to enhance their effectiveness. In this study, in vivo and in vitro methods were applied to evaluate the taste of tablets of three drugs administered to children without taste-masking excipients to treat tropical diseases, namely artesunate-mefloquine (ASMQ), praziquantel (PZQ), and benznidazole (BNZ). In the first method, a model of rat palatability was adapted with recirculation to ensure sample dispersion, and the data were analyzed using ANOVA (single factor, 95%). The taste assessment results (in vivo) indicated an aversion to the three medicines, denoted by the animals retracting themselves to the bottom of the box after the first contact with the drugs. For the placebo samples, the animals behaved normally, indicating that taste perception was acceptable. The second method was based on the in vitro analysis of capacitance data from a homemade impedimetric electronic tongue. Consistent with the in vivo taste assessment results, the data points obtained with PZQ, ASMQ, and BNZ were far away from those of their placebos in a map built with the multidimensional projection technique referred to as Interactive Document Mapping (IDMAP). A combined analysis of the results with the two methods allowed us to confirm the bitterness of the three drugs, also pointing to electronic tongues as a promising tool to replace in vivo palatability tests.

Asunto(s)

RESUMEN

The use of polymeric blends is a potential strategy to obtain novel nanotechnological formulations aiming at drug delivery systems. Saquinavir, an antiretroviral drug, was chosen as a model drug for the development of new stable liquid formulations with unpleasant taste masking properties. Three formulations containing different polymeric ratios (1:3, 1:1 and 3:1) were prepared and properly characterized by particle size distribution, zeta potential, pH, drug content and encapsulation efficiency measurements. The stability was verified by monitoring the zeta potential, particle size distribution, polydispersity index and drug content by 90 days. The light backscattering analysis was used to early identify possible phenomena of instability in the formulations. The in vitro drug release and saquinavir cytotoxicity were evaluated. The in vitro and in vivo taste masking properties were studied using an electronic tongue and a human sensory panel. All formulations presented nanometric sizes around 200 nm and encapsulation efficiency above 99%. The parameters evaluated for stability remained constant throughout 90 days. The in vitro tests showed a controlled drug release and absence of toxic effects on human T lymphocytes. The electronic tongue experiment showed taste differences for all formulations in comparison to drug solutions, with a more pronounced difference for the formulation with higher polycaprolactone content (3:1). This formulation was chosen for in vivo sensory panel evaluation which results corroborated the electronic tongue experiments. In conclusion, the polymer blend nanoformulation developed herein showed the promising application to incorporate drugs aiming at pharmaceutical taste-masking properties.

Asunto(s)

RESUMEN

The palatability of medications is an essential factor for children's adherence to drug treatment. Several methods for drug taste assessment have been developed. The aim of this review is to explore the literature reports of the main methods for the evaluation of medicines taste, named electronic tongue (e-tongue, in vitro) and human sensory panel. A systematic search was performed up to March 2020 and a total of 88 articles were selected. The e-tongue (57.5%) has been more frequently described than the sensory panel (10.3%), while some articles (32.2%) used both techniques. 74.7% of the articles mentioned 'pediatric', 'paediatric' or 'children' in the text, but only 19.5% developed formulations targeting pediatric audience and sensory testing in children is rarely seen. The e-tongue has predominance of use in the taste evaluation of pediatric medicines probably since it is fast, easy to perform and risk free, besides presenting less imprecise data and no fatigue. The human panel is more realistic, despite its intrinsic variability. In this sense, it is proposed the use of e-tongue as a fast way to select the most promising sample(s) and, after that, the sensory panel should be applied in order to confirm the taste masking.

Asunto(s)

RESUMEN

Incorporating electronic tongues into microfluidic devices brings benefits as dealing with small amounts of sample/discharge. Nonetheless, such measurements may be time-consuming in some applications once they require several operational steps. Here, we designed four collinear electrodes on a single printed circuit board, further comprised inside a straight microchannel, culminating in a robust e-tongue device for faster data acquisition. An analog multiplexing circuit automated the signal's routing from each of the four sensing units to an impedance analyzer. Both instruments and a syringe pump are controlled by dedicated software. The automated e-tongue was tested with four Brazilian brands of liquid sucralose-based sweeteners under 20 different flow rates, aiming to systematically evaluate the influence of the flow rate in the discrimination among sweet tastes sold as the same food product. All four brands were successfully distinguished using principal component analysis of the raw data, and despite the nearly identical sucralose-based taste in all samples, all brands' significant distinction is attributed to small differences in the ingredients and manufacturing processes to deliver the final food product. The increasing flow rate improves the analyte's discrimination, as the silhouette coefficient reaches a plateau at ~3 mL/h. We used an equivalent circuit model to evaluate the raw data, finding a decrease in the double-layer capacitance proportional to improvements in the samples' discrimination. In other words, the flow rate increase mitigates the formation of the double-layer, resulting in faster stabilization and better repeatability in the sensor response.

RESUMEN

This research has aimed to improve the stability and taste-masking properties by developing nanostructured dosage forms containing Saquinavir. Liquid formulations were developed using Eudragit RS100® and Pullulan as polymers. The physicochemical characteristics, stability, in vitro drug release, morphology, mucoadhesion and taste masking capacity were evaluated. The Saquinavir-nanoparticles had average diameters between 136 and 158 nm, with a Span below 1.4. These formulations presented a drug content above 80%, a high encapsulation efficiency (>97%), slightly acidic pH levels, low dynamic viscosity and controlled drug release. Electron microscopy revealed irregular spherical nanoparticles. The formulations prepared with higher amounts of Eudragit RS100® had greater mucoadhesion. Both polymers were able to improve drug stabilization, taste-masking properties and protection against drug cytotoxicity. The Saquinavir-nanoparticles exhibited stability and control releasing properties, thus making it a promising liquid dosage form with taste-masking properties intended for application in pediatric treatment.

Asunto(s)

RESUMEN

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.

RESUMEN

Antibiotics are considered emerging pollutants which indiscriminate use has led to the development of antibiotic-resistant bacteria, while their improper disposal has caused adverse effects to the environment and human health. Thus, the development of devices or techniques capable of detecting antibiotics with high sensitivity, low detection limits, and reasonable cost becomes of prime importance. In this work, an electronic tongue (e-tongue) based on molybdenum disulfide (MoS2) and graphene oxide (GO) was developed and employed to detect four distinct antibiotics, namely cloxacillin benzathine, erythromycin, streptomycin sulfate, and tetracycline hydrochloride. The five sensing units of the e-tongue were obtained using the drop-casting method to modify gold interdigitated electrodes with MoS2 and GO. Using Principal Component Analysis to process the experimental data allowed the e-tongue to recognize samples contaminated with distinct antibiotics at varied concentrations from 0.5 to 5.0 nmol L-1. Analyses with real samples were also performed using river water and human urine and the electronic tongue was able to differentiate the samples at a nanomolar level. The proposed system represents a sensitive and low-cost alternative for antibiotic analyses in different liquid media.

Asunto(s)

RESUMEN

BACKGROUND: The growing need to classify the origin of honey in a simple way is leading to the development of affordable analytical equipment that is in-line and manageable, enabling rapid on-site screening. The aim of this work was therefore to evaluate whether an electronic tongue (made of four metallic electrodes: Ir, Rh, Pt, Au), based on potential multistep pulse voltammetry with electrochemical polishing, is able to differentiate between honey samples from Spain, Honduras, and Mozambique. RESULTS: It was demonstrated, for the first time, that automatic pulse voltammetry, in combination with principal component analysis (PCA) statistical analysis, was able to differentiate honey samples from these three countries. A partial least squares (PLS) analysis predicted the level of certain physicochemical parameters, the best results being for conductivity and moisture with correlation coefficients of 0.948 and 0.879, whereas the weakest correlation was for the sugars. CONCLUSION: The tool proposed in this study could be applied to identify the country origin of the three types of multifloral honey considered here. It also offers promising perspectives for expanding knowledge of the provenance of honey. All of this could be achieved when a comprehensive database with the information generated by this electronic tongue has been created. © 2019 Society of Chemical Industry.

Asunto(s)

RESUMEN

An electronic tongue (e-tongue) is a multisensory system employed in the analysis of liquid samples, transforming the raw data into specific recognition patterns through computational and statistical analysis. Distinct types of e-tongues have been reported in the literature, with a plethora of applications in several areas of research. Recently, e-tongues have been integrated into microfluidic devices, which offer advantages such as the use of continuous flow for faster and more accurate analysis, and reduction in size of the devices and volumes for sampling and discharge, which in turn reduces waste and cost. Here we describe the procedures and methodologies recently used in our research group in the development of a microfluidic e-tongue sensing system.

Asunto(s)

RESUMEN

BACKGROUND: Coffee samples adulterated with roasted corn and roasted soybean were analyzed using a voltammetric electronic tongue equipped with a polypyrrole sensor array. METHODS: Coffee samples were adulterated in concentrations of 2%, 5%, 10% and 20% of roasted corn and roasted soybean; 5 replicates of each were used. The discrimination capacity of a voltammetric electronic tongue elaborated with a polypyrrole sensor array, was evaluated by principal component analysis and cluster analysis, while the capacity to perform quantitative determinations was carried out by partial least squares. RESULTS: The results obtained by the application of principal component analysis showed an excellent ability to discriminate adulterated samples. Additionally, the classifications obtained by cluster analysis was concordant with those obtained by principal component analysis. On the other hand, the evaluation of the ability to quantitatively analyze the adulterated samples showed that the polypyrrole sensor array provides sufficient information to allow quantitative determinations by partial least squares regression. CONCLUSIONS: It could be concluded that the voltammetric electronic tongue used in this work allows the suf- ficient analysis of coffee samples adulterated with roasted corn and roasted soybean.

Asunto(s)