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

Application of an electronic tongue towards the analysis of brandies

This work reports the application of a voltammetric Electronic Tongue (ET) in the analysis of brandies, specifically in its classification according to the scores given by a skilled sensory panel and in the discrimination of different ageing methods. For this purpose, spirits were analyzed with no other pretreatment than its dilution with a saline solution to ensure enough conductivity. Recorded voltammetric signals produced by an array of six modified epoxy-composite sensors were preprocessed employing Fast Fourier Transform in order to reduce the complexity of the input signals while preserving the relevant information. Then, using the obtained coefficients, responses were evaluated using Linear Discriminant Analysis (LDA) as the pattern recognition model used to carry out the classification tasks. In both cases, good prediction ability was attained by the ET (classification rates of 100% and 97%, respectively), therefore permitting the correct classification of the different samples under study. Furthermore, two Artificial Neural Network models were also trained for the semi-quantitative identification of some undesired compounds markers of some brandy defects upper certain levels (namely butan-2-ol, ethyl acetate, acetaldehyde and butan-1-ol; r>0.975) and the quantification of polyphenol index I280(r=0.977

Fundamentals and application of voltammetric electronic tongues in quantitative analysis

Altres ajuts: acords transformatius de la UABElectronic tongues (ETs) are bioinspired analytical tools based on the synergies between (bio)sensors and chemometrics. Through the application of chemometrics, it is possible to infer underlying relationships between the measured analytical signals and the chemical properties of the samples, both for descriptive and predictive purposes, otherwise impossible to decipher. Research in voltammetric ETs during the last two decades has demonstrated the benefits derived from the use of sensor arrays with complementary response, together with advanced data treatment methods to enhance their overall performance. In this direction, the different approaches followed when developing voltammetric ETs and some relevant applications in quantitative analysis are reviewed herein

A Sensor Array Based on Molecularly Imprinted Polymers and Machine Learning for the Analysis of Fluoroquinolone Antibiotics

Fluoroquinolones (FQs) are one of the most important types of antibiotics in the clinical, poultry, and aquaculture industries, and their monitoring is required as the abuse has led to severe issues, such as antibiotic residues and antimicrobial resistance. In this study, we report a voltammetric electronic tongue (ET) for the simultaneous determination of ciprofloxacin, levofloxacin, and moxifloxacin in both pharmaceutical and biological samples. The ET comprises four sensors modified with three different customized molecularly imprinted polymers (MIPs) and a nonimprinted polymer integrated with Au nanoparticle-decorated multiwall carbon nanotubes (Au-fMWCNTs). MWCNTs were first functionalized to serve as a supporting substrate, while the anchored Au nanoparticles acted as a catalyst. Subsequently, MIP films were obtained by electropolymerization of pyrrole in the presence of the different target FQs. The sensors' morphology was characterized by scanning electron microscopy and transmission electron microscopy, while the modification process was followed electrochemically step by step employing [Fe(CN)] 3-/4- as the redox probe. Under the optimal conditions, the MIP(FQs)@Au-fMWCNT sensors exhibited different responses, limits of detection of ca. 1 μM, and a wide detection range up to 300 μM for the three FQs. Lastly, the developed ET presents satisfactory agreement between the expected and obtained values when used for the simultaneous determination of mixtures of the three FQs (R 2 ≥0.960, testing subset), which was also applied to the analysis of FQs in commercial pharmaceuticals and spiked human urine samples

Llengües (bio)electròniques aplicades a begudes alcohòliques

Aquesta tesi, llegida al Departament de Química de la UAB, descriu el desenvolupament i aplicació de diferents llengües (bio)electròniques per a l'anàlisi i caracterització de diferents tipus de begudes alcohòliques, tant per assolir-ne la seva classificació com per la quantificació de diferents paràmetres analítics. Concretament, s'ha estudiat l'aplicació d'aquestes eines en el cava, el brandi, la cervesa i el vi.Esta tesis, leída en el Departamento de Química de la UAB, describe el desarrollo y aplicación de diferentes lenguas (bio)electrónicas para el análisis y caracterización de diferentes tipos de bebidas alcohólicas, tanto para alcanzar su clasificación como para la cuantificación de diferentes parámetros analíticos. Concretamente, se ha estudiado la aplicación de estas herramientas en el cava, el brandy, la cerveza y el vino

Resolution of opiate illicit drugs signals in the presence of some cutting agents with use of a voltammetric sensor array and machine learning strategies

Altres ajuts: Acord transformatiu CRUE-CSICIn the present work, the resolution and quantification of mixtures of different opiates compounds in the presence of common cutting agents using an electronic tongue (ET) is evaluated. More specifically, ternary mixtures of heroin, morphine and codeine were resolved in the presence of caffeine and paracetamol. To this aim, an array of three carbon screen-printed electrodes were modified with different ink-like solutions of graphite, cobalt (II) phthalocyanine and palladium, and their responses towards the different drugs were characterized by means of square wave voltammetry (SWV). Developed sensors showed a good performance with good linearity at the µM level, LODs between 1.8 and 5.33 µM for the 3 actual drugs, and relative standard deviation (RSD) ca. 2% for over 50 consecutive measurements. Next, a quantitative model that allowed the identification and quantification of the individual substances from the overlapped voltammograms was built using partial least squares regression (PLS) as the modelling tool. With this approach, quantification of the different drugs was achieved at the μM level, with a total normalized root mean square error (NRMSE) of 0.084 for the test subset

Quantitative analysis of active pharmaceutical ingredients (APIs) using a potentiometric electronic tongue in a SIA flow system

Research funding: National Science Centre. Grant Numbers: DEC-2013/09/B/ST4/00957, LIDER/17/202/L-1/09/NCBiR/2010An advanced potentiometric electronic tongue and Sequential Injection Analysis (SIA) measurement system was applied for the quantitative analysis of mixtures containing three active pharmaceutical ingredients (APIs): acetaminophen, ascorbic acid and acetylsalicylic acid, in the presence of various amounts of caffeine as interferent. The flow-through sensor array was composed of miniaturized classical ion-selective electrodes based on plasticized PVC membranes containing only ion exchangers. Partial Least Squares (PLS) analysis of the steady-state sensor array responses, measured in API mixtures prepared by the SIA system permitted a correct quantitative analysis of acetylsalicylic acid and ascorbic acid. Further optimization using multiway PLS fed by dynamic responses without additional feature extraction did not improve significantly the resolution of acetaminophen. Lastly, the chemometric treatment, involving the extraction of dynamic components of the transient response employing the Wavelet transform, the removal of less-significant coefficients by means of Causal Index pruning and training of an Artificial Neural Network (ANN) with the selected coefficients, allowed the simultaneous determination of all the three studied APIs, while counterbalancing any interference due to caffeine

Beer classification by means of a potentiometric electronic tongue

In this work, an Electronic Tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) is presented for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, Principal Component Analysis (PCA) and Linear Discriminant Analysis(LDA) in order to achieve the correct recognition of samples variety. In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. Finally, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show a quantitative application, alcohol content was predicted from the array data employing an Artificial Neural Network model

Llengües (bio)electròniques aplicades a l'anàlisi i caracterització de begudes

En els darrers anys, s'ha observat una creixent demanda de nous mètodes analítics capaços d'oferir el màxim de informació analítica amb el mínim "esforç" possible, els quals són necessaris per tal d'assolir els nous reptes en camps tals com el monitoratge ambiental, la seguretat alimentària i la salut pública. En aquest sentit, la indústria està molt interessada en el desenvolupament de mètodes ràpids i de baix cost que puguin ser utilitzats de forma rutinària com a cribratge (screening analysis) per tal de detectar qualsevol adulteració o contaminació del producte ja sigui durant la seva elaboració o per comprovar que compleixen els estàndards de control de qualitat. En aquest sentit, les línies clàssiques de investigació en el camp dels sensors químics s'han centrat en el desenvolupament de dispositius cada vegada més selectius envers espècies determinades, i al mateix temps amb una major sensibilitat i menor límit de detecció. Malauradament, es disposa de molts pocs sensors químics que operin de forma òptima i sense cap interferència o efecte matriu en les condicions requerides en els casos de mostres reals. Precisament, la dificultat per obtenir sensors amb una selectivitat i sensibilitat apropiada per qualsevol analit, ha estat la que ha donat pas a l'aparició de noves estratègies com és el cas de les llengües electròniques per tal de donar resposta a aquests problemes. Aquests sistemes analítics consisteixen en la imitació del sentit de gust dels mamífers, on uns pocs receptors són capaços de respondre a una gran varietat de substàncies, un principi que juntament amb una etapa complexa de tractament de la informació anàloga a la que té lloc en el cervell, permet la quantificació o classificació d'un gran nombre de substàncies. Aquests sistemes biomimètics, en oposició als sistemes clàssics, estan basats en la combinació de matrius de sensors amb una baixa selectivitat i/o una resposta creuada per tal d'obtenir un valor afegit en la generació de la informació analítica. Un dels darrers avanços en el disseny de llengües electròniques ha estat la incorporació de biosensors per tal de fer front a nous camps d'aplicació o millorar els ja existents. Aquests sistemes, coneguts com a llengües bioelectròniques, només es distingeixen dels convencionals en la incorporació d'un o més biosensors en la matriu de sensors que formarà la llengua electrònica, normalment compartint el mateix principi de detecció per tal de facilitar la compatibilitat entre ells. En aquest marc, el present treball pretén demostrar l'aplicabilitat d'aquests sistemes en l'anàlisi i caracterització de begudes, en el sector vinícola i de les begudes alcohòliques, tant per l'extracció de informació qualitativa i identificació de classes com per la quantificació de paràmetres d'interès analític, responent en ambdós casos a les necessitats corresponents en cada sector. Concretament, s'ha estudiat l'aplicació d'aquestes eines en el cava, el brandi, la cervesa i el vi; quatre dels sectors més importants en quant a begudes alcohòliques en el nostre país. A més, donada la importància que han assolit els compostos fenòlics en els darrers anys degut a la seva activitat antioxidant, beneficiosa per a la salut, s'ha abordat la quantificació d'aquests tant a nivell global com la discriminació individual; abordant la seva detecció a partir d'una llengua electrònica clàssica i d'una llengua bioelectrònica, comparant així els beneficis derivats de la incorporació de biosensors en la matriu de la llengua. Per últim, donada la dificultat que genera el tractament de les dades generades amb aquests sistemes, especialment en el cas dels sensors voltamperomètrics, també s'ha treballat en el desenvolupament i aplicació de noves estratègies de processament per tal de reduir-ne la complexitat i millorar els resultats obtinguts; comparant a més les diferents estratègies proposades entre elles.Over the last years, there has been an increasing demand of new analytical methods with the high sensitivity and selectivity, and fast response needed to meet new challenges in environmental monitoring, food safety and public health. In this fashion, industry is increasingly focused on fast-response and low-cost methods, as those based on chemical sensors, that may be used for screening or detecting any adulteration or contamination of the products, either during or after its production, or to assess they guarantee quality control standards. In this sense, classical research lines in the field of chemical sensors have focused on the development of ever more selective devices towards a particular species, and sensitive to lower concentrations at the same time. Unfortunately, there are few optimally operating chemical sensors that function without any interference or matrix effect in the required conditions when dealing with real samples analysis. Precisely, the difficulty to obtain sensors with appropriate selectivity and sensitivity for a given analyte has led to the appearance of new strategies such as electronic tongues in order to tackle these problems. These analytical systems are inspired by the sensory ability of taste in mammals, where a few receptors can respond to a large variety of substances. This principle is coupled with complex data treatment analogous to the applied in the brain, which allows to quantify or to classify a large amount of substances. These biomimetic systems, opposed to conventional approaches, are directed towards the combination of low selectivity sensors array response (or cross response features) in order to obtain some added value in the generation of analytical information. One of the recent advances in the design of electronic tongues has been the incorporation of biosensors, in order to tackle new application fields or to improve existing ones. These bioelectronic tongues, as they have been named, are only distinguished from conventional ones in the incorporation of one or several biosensors into the sensor array, normally sharing the same transduction principle to facilitate compatibility. In this context, the work presented herein aims to demonstrate the applicability of these systems towards the analysis and characterization of beverages, specifically towards wine and alcoholic beverages, either for the extraction of qualitative information and its classification or the quantification of analytical parameters of interest, responding in both cases to the needs of each sector. Concretely, its application towards cava wine, brandy, beer and wine has been studied; the most important sectors in terms of alcoholic beverages in our country. Additionally, given the importance that phenolic compounds have achieved in the recent years due to its antioxidant activity, with huge health benefits, the quantification of these compounds has been addressed from both points of view: its global content and the individual discrimination; tackling it using either a classical electronic tongue and a bioelectronic tongue, comparing the benefits of the incorporation of biosensors in the e-tongue array. Lastly, given the difficulties derived in the treatment of the data generated with such systems, specially in the case of voltammetric sensors, much attention has been paid to the development and application of novel processing strategies in order to reduce its complexity and improve the obtained results; besides comparing the different proposed strategies between each other

Hybrid Electronic Tongue based on Multisensor Data Fusion for Discrimination of Beers

This paper reports the use of a hybrid Electronic Tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modifiedgraphite- epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles and LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types