Electronic tongue for pharmaceutical analytics: quantification of tastes and masking effects

The organoleptic aspects of pharmaceutical formulations affect their acceptability to the patient and hence can have an important effect on concordance with treatment. Objective evaluation of these aspects, particularly the taste of the formulation and the drug substance it contains, is difficult. Whilst volunteer taste panels can be used to good effect their utility is limited, particularly during very early stage development when the toxicological profile of the active pharmaceutical ingredient (API) is yet to be established in detail. A potentiometric electronic tongue has been applied to analyse a variety of 41 individual substances and mixtures of particular interest for pharmaceutical research and development. The electronic tongue (ET) was capable of discriminating between substances with different taste modalities and could also distinguish different substances eliciting the same basic taste; the ET is promising in terms of quantifying the content of each substance and has an ability to detect nuances of the basic taste (e.g. lingering or short-lived). After calibration the electronic tongue was successfully applied to predicting bitterness strength of binary mixtures with a sweetener in terms of apparent or perceived quinine content. In order to render a formulation palatable it is often necessary to mask the (usually bitter) taste of the API by the addition of masking agents such as sweeteners and flavours. The ET proved capable of distinguishing between formulations with different levels of sweetener and/or flavour in a manner that was consistent with their masking efficiency as perceived by a small human taste panel. A suitably calibrated ET could have the benefit of providing the pharmaceutical formulator with reliable data concerning the taste of the product quickly and with a reduced need to ask volunteers to taste active pharmaceutical samples. Early development activities could be facilitated when human tasting is usually not possible in the absence of the required toxicological data.     

Assessment of bitter taste of pharmaceuticals with multisensor system employing 3 way PLS regression

The application of the potentiometric multisensor system (electronic tongue, ET) for quantification of the bitter taste of structurally diverse active pharmaceutical ingredients (API) is reported. The measurements were performed using a set of bitter substances that had been assessed by a professional human sensory panel and the in vivo rat brief access taste aversion (BATA) model to produce bitterness intensity scores for each substance at different concentrations. The set consisted of eight substances, both inorganic and organic – azelastine, caffeine, chlorhexidine, potassium nitrate, naratriptan, paracetamol, quinine, and sumatriptan. With the aim of enhancing the response of the sensors to the studied APIs, measurements were carried out at different pH levels ranging from 2 to 10, thus promoting ionization of the compounds. This experiment yielded a 3 way data array (samples × sensors × pH levels) from which 3wayPLS regression models were constructed with both human panel and rat model reference data. These models revealed that artificial assessment of bitter taste with ET in the chosen set of API's is possible with average relative errors of 16% in terms of human panel bitterness score and 25% in terms of inhibition values from in vivo rat model data. Furthermore, these 3wayPLS models were applied for prediction of the bitterness in blind test samples of a further set of API's. The results of the prediction were compared with the inhibition values obtained from the in vivo rat model.     

Instrumental measurement of bitter taste in red wine using an electronic tongue

An electronic tongue (ET) based on potentiometric chemical sensors was assessed as a rapid tool for the quantification of bitterness in red wines. A set of 39 single cultivar Pinotage wines comprising 13 samples with medium to high bitterness was obtained from the producers in West Cape, South Africa. Samples were analysed with respect to a set of routine wine parameters and major phenolic compounds using Fourier transform infrared-multiple internal reflection spectroscopy (WineScan) and high-performance liquid chromatography, respectively. A trained sensory panel assessed the bitterness intensity of 15 wines, 13 of which had a bitter taste of medium to high intensity. Thirty-one wine samples including seven bitter-tasting ones were measured by the ET. Influence of the chemical composition of wine on the occurrence of the bitter taste was evaluated using one-way analysis of variance. It was found that bitter-tasting wines had higher concentrations of phenolic compounds (catechin, epicatechin, gallic and caffeic acids and quercetin) than non-bitter wines. Sensitivity of the sensors of the array to the phenolic compounds related to the bitterness was studied at different pH levels. Sensors displayed sensitivity to all studied compounds at pH 7, but only to quercetin at pH 3.5. Based on these findings, the pH of wine was adjusted to 7 prior to measurements. Calibration models for classification of wine samples according to the presence of the bitter taste and quantification of the bitterness intensity were calculated by partial least squares-discriminant analysis (PLS-DA) regression. Statistical significance of the classification results was confirmed by the permutation test. Both ET and chemical analysis data could discriminate between bitter and control wines with the correct classification rates of 94% and 91%, respectively. Prediction of the bitterness intensity with good accuracy (root mean square error of 2 and mean relative error of 6% in validation) was possible only using ET data.     

Screening of Synthetic Cathinones by Potentiometric Sensor Array and Chemometrics

This work demonstrates the analytical applicability of single ion-selective membranes (ISMs) and potentiometric sensor array to distinguish and detect cathinone derivatives. Potentiometric data from ISMs based on cation exchanger and varying content of calix[4]arene derivative were processed by principal component analysis (PCA). Such a combination of methods allowed discriminating various individual synthetic cathinones and their recognition from the mixture comprising primary amines (substituted amphetamines+aminoindane). Analytical parameters of ISM containing 1wt % of calix[4]arene derivative were sufficient to detect 1.0×10⁻⁴ mol.l⁻¹ 1-(4-fluorophenyl)-2-(ethylamino)butan-1-one and 2-(methylamino)-1-phenylbutan-1-one (buphedrone) in both model and saliva samples.     

人工智能味觉系统：概念、结构与方法

本文将传统的味觉传感器、人工味觉、电子舌等研究统一纳入人工智能味觉的框架中，提出了人工智能味觉的概念、结构及方法学问题。在传统分析科学中，非特异性感应是一个极力避免的现象，而交互感应传感器阵列则是将其作为基本出发点，设计构建成一个优化的组合，使得人工智能化学感受在技术上得以实现，交互感应理论和技术也因此成为其核心概念。作为一种仿生感觉技术，人工智能味觉系统是由交互感应电极及阵列、自学习专家数据库和智能模式识别三部分构成；电位法和伏安法是两类主要的传感方法；传感器材料包括贵金属及稀土金属、类脂/聚合物、硫属玻璃、导电聚合物以及其它如碳糊、酞菁、普鲁士蓝等；主成分分析和人工神经网络是智能识别过程中主要的模式识别工具。导电聚合物修饰电化学传感器和多技术联用分析方法目前已成为人工智能味觉系统研究的两个重要发展方向。     

Quantitative Analysis of Active Pharmaceutical Ingredients (APIs) Using a Potentiometric Electronic Tongue in a SIA Flow System

An 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.     

Molecularly Imprinted Photonic Polymers as Sensing Elements for the Creation of Cross‐Reactive Sensor Arrays

By combining molecular imprinting and colloidal crystal templating, molecularly imprinted inverse‐opal photonic polymers (MIPPs) acting as sensing elements have been exploited to create sensor arrays for the first time. With this new strategy, abundant sensing elements with differential sensing abilities were easily accessible. Because of the unique hierarchical porous structure integrated in each sensing element, high sensitivity and selectivity, fast response and self‐reporting (label‐free) detection could be simultaneously achieved. All these fascinating features indicate that MIPPs are ideal sensing elements for creating sensor arrays. By integrating the individual sensing elements on a substrate, the formed array chip delivers better portability and high‐throughput capability. As a demonstration, six kinds of contaminants were selected as analytes. The detection and discrimination of these analytes and even their mixtures in a wide range of concentrations, particularly trace amounts of analyte against a high background of other components, could be achieved, indicating the powerful capability of MIPPs‐based sensor array for sensing. These results suggest that the described strategy opens a new route for sensor array creation and should find important applications in a wide range of areas.     

Simultaneous determination of caffeine,paracetamol, and ibuprofen in pharmaceutical formulations by high‐performance liquid chromatography with UV detection and by capillary electrophoresis with conductivity detection

Paracetamol, caffeine and ibuprofen are found in over‐the‐counter pharmaceutical formulations. In this work, we propose two new methods for simultaneous determination of paracetamol, caffeine and ibuprofen in pharmaceutical formulations. One method is based on high‐performance liquid chromatography with diode‐array detection and the other on capillary electrophoresis with capacitively coupled contactless conductivity detection. The separation by high‐performance liquid chromatography with diode‐array detection was achieved on a C₁₈ column (250×4.6 mm², 5 μm) with a gradient mobile phase comprising 20–100% acetonitrile in 40 mmol L^?1 phosphate buffer pH 7.0. The separation by capillary electrophoresis with capacitively coupled contactless conductivity detection was achieved on a fused‐silica capillary (40 cm length, 50 μm i.d.) using 10 mmol L^?1 3,4‐dimethoxycinnamate and 10 mmol L^?1 β‐alanine with pH adjustment to 10.4 with lithium hydroxide as background electrolyte. The determination of all three pharmaceuticals was carried out in 9.6 min by liquid chromatography and in 2.2 min by capillary electrophoresis. Detection limits for caffeine, paracetamol and ibuprofen were 4.4, 0.7, and 3.4 μmol L^?1 by liquid chromatography and 39, 32, and 49 μmol L^?1 by capillary electrophoresis, respectively. Recovery values for spiked samples were between 92–107% for both proposed methods.     

Nano‐Graphene Oxide Based Multichannel Sensor Arrays towards Sensing of Protein Mixtures

Optical array‐based sensors are attractive candidates for the detection of various bio‐analytes due to their convenient fabrication and measurements. For array‐based sensors, multichannel arrays are more advantageous and used frequently in many electronic sensors. But most reported optically array based sensors are constructed on a single channel array. This difficulty is mainly instigated from the overlap in optical responses. In this report we have used nano‐graphene oxide (nGO) and suitable fluorophores as sensor elements to construct a multichannel sensor array for the detection of protein analytes. By using the optimized multichannel array we are able to detect different proteins and mixtures of proteins with 100 % classification accuracy at sub‐nanomolar concentration. This modified method expedites the sensing analysis as well as minimizes the use of both analyte and sensor elements in array‐based protein sensing. We have also used this system for the single channel array‐based sensing to compare the sensitivity and the efficacy of these two systems for other applications. This work demonstrated an intrinsic trade‐off associated with these two methods which may be necessary to balance for array‐based analyte detections.     

Novel ibuprofen potentiometric membrane sensors based on tetraphenylporphyrinato indium(III).

Two novel potentiometric membrane sensors responsive to the ibuprofen drug have been developed. These incorporate poly(vinyl chloride) and polyurethane matrix membranes containing 5,10,15,20-tetraphenylporphrinato (TPP) indium(II) ionophore plasticized with dibutylsebacate. The sensors show a near-Nernstian response with anionic slopes of -53 and -55 mV decade(-1), over the concentration range of 4.2 x 10(-6)-1.0 x 10(-2) and 3.3 x 10(-6)-1.0 x 10(-2) M ibuprofen within pH ranges of 4-9 and 5-9 for PVC and PU matrix membranes, respectively. A sensor based on a polyurethane membrane displays a lower detection limit and a wider linear working range, and a sensor based on a PVC membrane exhibits a better overall selectivity, especially in the presence of lipophilic organic anions. Both sensors are used for the quantification and quality-control assessment of ibuprofen in pharmaceutical preparations. The average recoveries are 99.1+/-0.3% and 99.3+/-0.3% for TPP In(III)-PVC and TPP In(III)-PU based membrane sensors, respectively. High selectivities towards ibuprofen in the presence of many anions, drug excipients and diluents are offered by both sensors, which exhibit a non-Hofmeister selectivity pattern.     

Leveraging Material Properties in Fluorescence Anion Sensor Arrays: A General Approach

As the demand for probes suitable for sensor development increases, investigation of approaches that utilize known successful receptors gains in general importance. This study describes a two‐prong approach that can be used as a guide to developing sensors from known receptors. First, the conversion of a simple receptor, calix[4]pyrrole, into a fluorescent probe to establish a ratiometric signal is described. Secondly, the sensors that employ an output from a single ratiometric calix[4]pyrrole probe are fabricated by using poly(ether‐urethane) hydrogel copolymers. These hydrogels are designed to absorb, internalize and transport aqueous electrolytes. A sensor array of ten different poly(ether‐urethane) matrices with varying comonomer proportions were doped with a single probe and were exposed to eight different anions: acetate, benzoate, fluoride, chloride, phosphate, pyrophosphate, hydrogen sulfide, and cyanide, eight urine samples and anti‐inflammatory drugs (NSAIDs). The poly(ether‐urethane) matrices comprise different proportions of anion‐binding urethane moieties and different hydrophilicity given by the ratio between ethylene glycol ether and butylene glycol ether. This diversity in the hydration behavior provides different environment polarity, in which the recognition and self‐assembly processes display enough diverse behavior to allow for unique response of the probe to the analytes. Furthermore, a single probe is shown to recognize eight different aqueous anions and eight urine samples when embedded in ten different polyurethanes in an array that displays 100 % classification accuracy. To demonstrate the potential of the concept for quantitative studies, an estimation of non‐steroidal anti‐inflammatory drugs ibuprofen and diclofenac in water and in saliva was performed. A limit of detection of 0.1 ppm and a dynamic range of 0.1–0.6 and 0.05–60 ppm was observed, respectively. Given the general difficulty of chemosensors to recognize aqueous anions, the fact that one probe recognizes eight different analytes attests to an enormous effect of the polymer environment on the recognition process. This method could be used to generate a variety of sensor arrays for various analyses including species that are difficult to recognize, such as small‐molecule‐ and inorganic anions.     

A Rapid and Efficient Way to Dynamic Creation of Cross‐Reactive Sensor Arrays Based on Ionic Liquids

Based on the simple counterion exchange of ionic liquids, a rapid, facile, and efficient strategy to create a cross‐reactive sensor array with a dynamic tunable feature was developed, and exemplified by the construction of a sensor array for the identification and classification of nitroaromatics and explosives mimics. To achieve a good sensing system with fast response, good sensitivity, and low detection limit, the synthesized ionic liquid receptors were tethered onto a silica matrix with a macro‐mesoporous hierarchical structure. Through the facile anion exchange approach, abundant ionic‐liquid‐based individual receptors with diversiform properties, such as different micro‐environments, diverse molecular interactions, and distinctive physico‐chemical properties, were easily and quickly synthesized to generate a distinct fingerprint of explosives for pattern recognition. The reversible anion exchange ability further endowed the sensor array with a dynamic tunable feature as well as good controllability and practicality for real‐world application. With the assistance of statistical analysis, such as principal component analysis (PCA) and linear discrimination analysis (LDA), an optimized‐size array with a good resolution was rationally established from a large number of IL‐based receptors. The performed experiments suggested that the ionic‐liquid‐based sensing protocol is a general and powerful strategy for creating a cross‐reactive sensor array that could find a wide range of applications for sensing various analytes or complex mixtures.     

Synthesis and Molecular Recognition of Molecularly Imprinted Polymer with Ibuprofen as Template

Ibuprofen and ketoprofen are chemically similar non‐steroidal anti‐inflammatory drugs widely used in the treatment of arthritis. Using a molecular imprinting technique, a simple and rapid method was developed for the simultaneous separation and determination of ibuprofen and ketoprofen. Molecular imprinting introduces artificial binding sites into a synthetic polymer matrix, allowing it to exhibit selective rebinding of template molecules. Imprinted polymers can be regarded as an HPLC stationary phase, important for pharmaceutical analysis. Most molecularly imprinted polymers (MIPs) are synthesized by free radical polymerization of functional monomers, resulting in an excess of crosslinking monomers. In this study, MIPs have been prepared with a ibuprofen template, which can form intramolecular hydrogen bonds. Methacrylic acid (MAA) and ethyleneglycol dimethacrylate (EGDMA) were used as the functional monomer and cross‐linker, respectively. Bulk polymerization was carried out at 4 °C under UV radiation. The resulting MIP was ground into 25?44 μm particles, which were slurry‐packed into analytical columns. Template molecules were removed by methanol‐acetic acid (9:1, v/v). We evaluated the template binding performance of the MIP using HPLC, with ultraviolet (UV) detection at 234 nm. Chromatographic resolution of ibuprofen and ketoprofen on the MIPs were appraised using buffer/acetonitrile (45/55, v/v) as the mobile phase. Results show that the MIPs prepared using ibuprofen as the template had a significant molecular imprinting effect. The method was successfully applied to the separation and analysis of ibuprofen and ketoprofen in pharmaceuticals.     

Imprinted Photonic Hydrogels for the Size‐ and Shell‐Selective Recognition of Nanoparticles

Sensors based on responsive photonic hydrogels have recently attracted considerable attention for visual medical diagnostics, pharmaceutical bioassays, and environmental monitoring. However, the use of these promising materials for the detection of nanoparticles (NPs) has never been explored so far, although the sensing of nanoobjects is a rapidly evolving area of research. To address this issue, we have combined the concepts of inverse‐opal hydrogels and nanoparticle‐imprinted polymers. In this way, we could obtain a NP‐imprinted photonic hydrogel consisting of a three‐dimensional, highly ordered poly(methacrylic acid) macroporous array, in which nanocavities complementary to the target NPs, in this case colloidal quantum dots, are distributed. This novel type of NP‐imprinted photonic hydrogel sensor was shown to display high sensitivity and selectivity, thus opening new prospects for the development of equipment‐free and cost‐efficient sensing devices for NPs.     

A Novel Thin‐Film Copper Array Based on an Organic/Inorganic Sensor Hybrid: Microfabrication,Potentiometric Characterization,and Flow‐Injection Analysis Application

A first step towards the microfabrication of a thin‐film array based on an organic/inorganic sensor hybrid has been realized. The inorganic microsensor part incorporates a sensor membrane based on a chalcogenide glass material (Cu‐Ag‐As‐Se) prepared by pulsed laser deposition technique (PLD) combined with an PVC organic membrane‐based organic microsensor part that includes an o‐xylyene bis(N,N‐diisobutyl‐dithiocarbamate) ionophore. Both types of materials have been electrochemically evaluated as sensing materials for copper(II) ions. The integrated hybrid sensor array based on these sensing materials provides a linear Nernstian response covering the range 1×10^?6–1×10^?1 mol L^?1 of copper(II) ion concentration with a fast, reliable and reproducible response. The merit offered by the new type of thin‐film hybrid array includes the high selectivity feature of the organic membrane‐based thin‐film microsensor part in addition to the high stability of the inorganic thin‐film microsensor part. Moreover, the thin‐film sensor hybrid has been successfully applied in flow‐injection analysis (FIA) for the determination of copper(II) ions using a miniaturized home‐made flow‐through cell. Realization of the organic/inorganic thin‐film sensor hybrid array facilitates the development of a promising sophisticated electronic tongue for recognition and classification of various liquid media.     

Deep Learning-Enhanced Potentiometric Aptasensing with Magneto-Controlled Sensors

Bioelectronic sensors that report charge changes of a biomolecule upon target binding enable direct and sensitive analyte detection but remain a major challenge for potentiometric measurement, mainly due to Debye Length limitations and the need for molecular-level platforms. Here, we report on a magneto-controlled potentiometric method to directly and sensitively measure the target-binding induced charge change of DNA aptamers assembled on magnetic beads using a polymeric membrane potentiometric ion sensor. The potentiometric responses of the negatively charged aptamer, serving as a receptor and reporter, were dynamically controlled and modulated by applying a magnetic field. Based on a potentiometric array, this non-equilibrium measurement technique combined with deep learning algorithms allows for rapidly and reliably classifying and quantifying diverse small molecules using antibiotics as models. This potentiometric strategy opens new modalities for sensing applications.     

Targeted Protein Surface Sensors as a Tool for Analyzing Small Populations of Proteins in Biological Mixtures

Optical cross‐reactive sensor arrays (the so‐called chemical “noses/tongues”) have recently been demonstrated as a powerful tool for high‐throughput protein detecting and analysis. Nevertheless, applying this technology to biomarker detection is complicated by the difficulty of non‐selective sensors to operate in biological mixtures. Herein we demonstrate a step toward circumventing this limitation by using self‐assembled fluorescent receptors consisting of two distinct recognition motifs: specific and non‐specific. When combined in an array, binding cooperatively between the specific and non‐specific protein binders enables the system to discriminate among closely related isoform biomarkers even in the presence of serum proteins or within human urine.     

基于液体阵列味觉仿生传感器鉴别白酒香型的新方法

通过模拟哺乳动物的味觉系统, 建立了交叉响应的液体阵列传感器, 为鉴别白酒香型提供了新方法. 选用7种染料和1种卟啉化合物作为传感单元, 构建液体阵列传感器, 集合8个传感单元的光谱响应信号构成分析物的指纹图谱, 达到识别的目的. 使用96孔板酶标仪采集响应数据, 结合主成分分析(PCA)、分层聚类分析(HCA)和判别分析(LDA)等模式识别方法进行数据处理, 对9种具有代表性的不同香型白酒样品进行了鉴别分析. PCA结果表明, 该方法对于白酒的检测主要基于酒体微量成分, 其中酸类物质对识别的贡献最大(贡献率达54.3%), 芳香类物质贡献率为18.6%; 同时, 仅用63.4%的数据信息量即可对白酒香型进行区分. HCA结果表明, 平行样均正确归类, 各白酒之间的相似程度在聚类图上得到体现. LDA结果表明, 该阵列对于9种白酒样品香型识别的准确率达到100%.     

A Layer‐by‐Layer Film of Chitosan in a Taste Sensor Application

Chitosan is alternated with sulfonated polystyrene (PSS) to build layer‐by‐layer (LBL) films that are used as sensing units in an electronic tongue. Using impedance spectroscopy as the principle method of detection, an array using chitosan/PSS LBL film and a bare gold electrode as the sensing units was capable of distinguishing the basic tastes – salty, sweet, bitter, and sour – to a concentration below the human threshold. The suitability of chitosan as a sensing material was confirmed by using this sensor to distinguish red wines according to their vintage, vineyard, and brands.

PCA Plot for red wine samples obtained from impedance measurements at 1 kHz for the sensor array comprising a 3‐bilayer chitosan/PSS film and a bare gold electrode.     

Electronic tongue for quality assessment of ethanol, vodka and eau-de-vie

Rapid quality assessment of alcoholic beverages, including brand identification and detection of products of unacceptable quality or counterfeits is an important practical task. In the present work the multisensor electronic tongue system (ET), based on array of potentiometric chemical sensors was applied to recognition and classification of spirits such as vodka and ethanol used for vodka production and also for eau-de-vie in cognac production. The ET system was capable of detecting presence of contaminant substances in vodka in concentrations exceeding allowed levels as well as of distinguishing vodka complying and not complying with state quality standards. Ten brands of vodka produced at the same distillery using water and ethanol of different purity and various taste additives were discriminated using the instrument. The ET could distinguish synthetic and alimentary grain ethanol as well as alimentary ethanol of different grades (i.e. different degree of purification). A feasibility study was run on several eau-de-vie samples, which included fresh and aged eau-de-vie as well as samples produced using different distillation technology and samples kept in contact with different kinds of oak. The electronic tongue showed a promise as an analytical instrument for rapid quality assessment of spirits.