A novel electronic tongue combined MLAPS with stripping voltammetry for environmental detection

A kind of integrated electronic tongue device has been developed, which includes a multiple light-addressable potentiometric sensor (MLAPS) and two groups of electrochemical electrodes. MLAPS is based on chalcogenide thin film for the simultaneous detection of Fe(III) and Cr(VI) ions while two groups of electrochemical electrodes, respectively, detect other heavy metals using stripping voltammetry (SV) including anodic and cathode stripping voltammetry. The methods are more convenient for the simultaneous detection of heavy metal in wastewater or seawater and can improve the detection speed and measurement accuracy to many heavy metals. Accordingly a kind of potable electronic tongue instrument to work on-line is also developed.     

Combined electronic nose and tongue for a flavour sensing system

We present a novel, smart sensing system developed for the flavour analysis of liquids. The system comprises both a so-called “electronic tongue” based on shear horizontal surface acoustic wave (SH-SAW) sensors analysing the liquid phase and a so-called “electronic nose” based on chemFET sensors analysing the gaseous phase. Flavour is generally understood to be the overall experience from the combination of oral and nasal stimulation and is principally derived from a combination of the human senses of taste (gustation) and smell (olfaction). Thus, by combining two types of microsensors, an artificial flavour sensing system has been developed. Initial tests conducted with different liquid samples, i.e. water, orange juice and milk (of different fat content), resulted in 100% discrimination using principal components analysis; although it was found that there was little contribution from the electronic nose. Therefore further flavour experiments were designed to demonstrate the potential of the combined electronic nose/tongue flavour system. Consequently, experiments were conducted on low vapour pressure taste-biased solutions and high vapour pressure, smell-biased solutions. Only the combined flavour analysis system could achieve 100% discrimination between all the different liquids. We believe that this is the first report of a SAW-based analysis system that determines flavour through the combination of both liquid and headspace analysis.     

用于绍兴黄酒总糖含量预测的电子鼻系统构建及其实验研究

构建了电子鼻检测系统,用于绍兴黄酒总糖含量的快速预测,采用具有8种气体传感器的电子鼻系统检测了元红、花雕、善酿、香雪4种经典黄酒样品,同时实验检测了黄酒样品的总糖含量,采用非线性双重叠加随机共振提取电子鼻检测数据的特征值,采用特征值结合黄酒样品总糖含量检验结果建立了总糖含量预测模型.该模型不但可以预测黄酒样品的总糖含量,而且可以实现黄酒样品的类型检测.该方法在黄酒品质分析领域具有广阔的应用前景.     

基于调幅脉冲扫描法的电子舌及其在酒类识别中的应用

为了能够对酒类进行区分识别,提出了一种基于调幅脉冲扫描方法的电子舌系统.应用由丝网印刷电极、检测电路和调幅扫描脉冲等组成的高性能电子舌对六种不同品牌的干红葡萄酒进行了测量,并通过特征值提取和主成分分析对数据进行了分析处理和识别研究.特征值提取减少了系统的处理数据量,主成分分析表明该电子舌系统能够很好的区分不同品牌的葡萄酒.为研制酒类质量检测仪器和酒类品质评判系统提供了新方法和新构架.     

用于食醋品质预评价的电子鼻研究

设计了面向食用醋品质预评价的电子鼻系统,它包括:由5种TGS系列和1种MQ系列半导体氧化物气体传感器构建的传感器阵列、以LPC2138微处理器为核心的采集电路等硬件部分。通过USB接口将所采集的数据上传至计算机,利用上位机LabVIEW软件进行数据采集的实时显示,并利用主成分分析(PCA)法和线性判别式分析(LDA)法对数据进行分析。使用该设计的电子鼻对生活中常见的5种食醋进行测量,并对食醋种类、配料关系进行了分析。结果表明,电子鼻技术在食醋品质预评价中是可行的。     

基于电子鼻系统的不同风味绍兴黄酒区分方法研究

黄酒具有独特的香气和丰富的营养价值,兼具饮用和药用价值.不同风味黄酒产品挥发性香气成分也不同,是黄酒品评考察的主要指标之一.本文选取元红、加饭、善酿、香雪4种典型风味绍兴黄酒为检测样品,采用电子鼻系统检测记录了不同黄酒样品的香气数据,并且以随机共振信噪比分析方法提取黄酒香气特征值.结果表明,检测样品酒特征值顺序依次为善...     

Fabrication and characterization of a miniaturized planar voltammetric sensor array for use in an electronic tongue

A planar voltammetric sensor array for use in an electronic tongue was fabricated using a combination of microfabrication techniques. The techniques of e-beam evaporation and pulsed laser deposition were applied to prepare a device that contained all of the electrodes integrated on a silicon die (6 mm × 6 mm). The working electrodes were metals gold, platinum, iridium and rhodium. They were characterized by SEM and EDX, and by electrochemical investigation of the packaged dies with cyclic voltammetry in solutions of sulfuric acid and of ferrocene carboxylic acid in aqueous buffer solution. The robustness and reproducibility of the devices were assessed by potential cycling in acid solution.     

An electronic nose system based on a micro-machined gas sensor array to assess the freshness of sardines

An electronic nose system based on a four-element, integrated, micro-machined, metal oxide gas sensor array is used to assess, in an objective manner, the evolutionary stages of freshness in sardine samples stored up to 1-week at 4 °C. The sensors developed were based on tin oxide doped with Pt or Pd or Bi, and on tungsten oxide doped with Au. The selection of the gas sensitive materials was based on a previous identification and quantification of characteristic compounds found in the headspace of sardines determined by solid phase micro-extraction gas chromatography coupled to mass spectrometry. Principal component analysis performed on the responses of the sensor array revealed that sardine samples could be classified in three freshness states. This was in good agreement with the results of a microbiological analysis. A support vector machine-based classifier reached a 100% success rate in the identification of sardine freshness. The stability of the electronic nose classification ability was assessed by correctly classifying measurement databases gathered 1-month apart. By building and validating quantitative partial least squares models, which employed as input data the gas sensor responses, it was possible to predict with good accuracy the total viable counts (TVC) of aerobic bacteria present in sardine samples. For the validation dataset, the correlation coefficient between actual and predicted TVC was 0.91, which indicates that the electronic nose system developed is a simple and rapid technique for evaluating sardine freshness.     

基于BP神经网络的猪肉新鲜度检测方法

猪肉在储藏、加工和运输过程中因为腐败会挥发出氨气、硫化氢等,据此选择8个金属氧化物半导体气敏传感器构成检测阵列,运用改进的BP神经网络算法建立猪肉新鲜度智能检测的数学模型,从而构建了猪肉新鲜度检测电子鼻系统。通过检测实验构建样本数据集,并对识别模型进行训练、测试,结果表明该模型对猪肉新鲜度的预测结果与用理化分析方法所得实际结果具有很好的吻合度,预测准确率大于90%。     

A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

A portable electronic tongue has been developed using an array of eighteen thick-film electrodes of different materials forming a multi-electrode array. A microcontroller is used to implement the pattern recognition. The classification of drinking waters is carried out by a Microchip PIC18F4550 micro-controller and is based on neural networks algorithms. These algorithm are initially trained with the multi-electrode array on a Personal Computer (PC) using several samples of waters (still, sparkling and tap) to obtain the optimum architecture of the networks. Once it is trained, the computed data are programmed into the microcontroller, which then gives the water classification directly for new unknown water samples. A comparative study between a Fuzzy ARTMAP, a Multi-Layer Feed-Forward network (MLFF) and a Linear Discriminant Analysis (LDA) has been done in order to obtain the best implementation on a microcontroller.     

Hybrid electronic tongue for the characterization and quantification of grape variety in red wines

A multisensor system combined with multivariate analysis was applied to the characterization of red wines and to the quantification of the grape variety percentage. The proposed system, known as hybrid electronic tongue, consists of a colorimetric optofluidic system and an array of electrochemical sensors. Three monovarietal red wines were studied: Pinot Noir, Merlot and Cabernet Sauvignon. Homemade mixtures were elaborated from these wines according to a Simplex experimental design with 60 samples. The data obtained were treated using advanced chemometric tools like Principal Component Analysis (PCA) and Soft Independent Modeling Class Analogy (SIMCA) for the classification of the wine mixtures and Partial Least Squares (PLS) regression for the quantification of the grape variety composition. The results have shown a good classification of the grape varieties and the identification of the mixtures with Pinot Noir up to 75%. Besides, using the PLS regression, the system has demonstrated a high potential for quantifying the percentage of each grape variety.     

Use of electronic nose technology for identifying rice infestation by Nilaparvata lugens

Plant-emitted volatiles can change after herbivore attack. Monitoring the change in volatile profiles can offer a non-destructive method for determining plant health. An electronic nose (E-nose) equipped with a headspace sampling unit was used to discriminate between volatile profiles emitted by uninfested rice plants and those emitted by rice plants exposed to different numbers of Nilaparvata lugens adults. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the E-nose was able to distinguish among the different pest treatments. The results indicate that it is possible to separate differently treated rice plants using E-nose signals. The stepwise discriminant analysis (SDA) and a 3-layer back-propagation neural network (BPNN) were developed for pattern recognition models. Calculations show that the discrimination rates were over 92.5% for the training data set and 70% for the testing set using SDA. The correlation coefficient between predicted and real numbers of the pest was found to be over 0.78 using BPNN. Moreover, gas chromatography–mass spectrometry (GC–MS) analysis confirmed the E-nose results. These studies demonstrate that the E-nose technology has clear potential for use as an effective insect monitoring method.     

Aging fingerprint characterization of beer using electronic nose

In this work, attempts were made in order to characterize the change of aroma of alcoholic and non alcoholic beers during the aging process by use of a metal oxide semiconductor based electronic nose. The aged beer samples were statistically characterized in several classes. Linear techniques as principal component analysis (PCA) and Linear Discriminant Analaysis (LDA) were performed over the data that revealed non alcoholic beer classes are separated except a partial overlapping between zones corresponding to two specified classes of the aged beers. A clear discrimination was not found among the alcoholic beer classes showing the more stability of such type of beer compared with non alcoholic beer. In this research, to classify the classes, two types of artificial neural networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Backpropagation (BP) learning method. The classification success was found to be 90% and 100% for alcoholic and non alcoholic beers, respectively. Application of PNN showed the classification accuracy of 83% and 100%, respectively for the aged alcoholic and non alcoholic beer classes as well. Finally, this study showed the capability of the electronic nose system for the evaluation of the aroma fingerprint changes in beer during the aging process.     

Semi-quantitative and quantitative analysis of soft drinks using an electronic tongue

A potentiometric electronic tongue with 36 cross-sensibility lipo/polymeric membranes was built and applied for semi-quantitative and quantitative analysis of non-alcoholic beverages. A total of 16 commercial fruit juices (e.g., orange, pineapple, mango and peach) from five different brands were studied. In the semi-quantitative approach, the signal profiles recorded by the device were used together with a stepwise linear discriminant analysis to differentiate four beverage groups with different fruit juice contents: >30%, 14-30%, 6-10% and <4%. The model, with two discriminant functions based on the signals of only four polymeric membranes, explained 99% of the total variability of experimental data and was able to classify the studied samples into the correct group with an overall sensibility and specificity of 100% for the original data, and greater than 93% for the cross-validation procedure.The signals were also used to obtain MLR and PLS calibration models to estimate and predict the concentrations of fructose and glucose in the soft drinks. The linear models established were based on the signals recorded by 16 polymeric membranes and were able to estimate and predict satisfactorily (cross-validation) the concentrations of the two sugars (R² greater than 0.96 and 0.84, respectively).     

Identification of different alcoholic beverages by electronic nose coupled to GC

In this work, 21 different alcoholic beverages (beer, wines and spirits) were analyzed by an electronic nose after a dehydration and dealcoholization procedure. Principal component analysis (PCA) and discriminant factorial analysis (DFA) allowed to clearly identify differences between these various alcoholic beverages and to classify them independent of their ethanol content. The PCA showed that the sample discrimination was carried out according to the content in aroma compounds. The discrimination quality was evaluated using the leave-one out method, which allowed to quantify the percentage of samples classed correctly for a determined number of sensors.     

Quality control of oral herbal products by an electronic tongue—Case study on sage lozenges

Quality control of complex herbal extracts remains a challenge for analytical procedures as only representative ingredients can be characterized, but not the whole extract. A promising alternative are electronic tongues which are, due to sensors’ non-specificity, able to globally characterize complex mixtures. This article elaborates the application of an electronic tongue as an alternative method in quality control of herbal lozenges as a case study. The Insent taste sensing system SA402B was used equipped with 7 sensors imitating the taste stimuli and sensory signals umami, saltiness, astringency, bitterness and sourness. The results were compared to well-established analytical procedures like high performance liquid chromatography and gas chromatography. Further, a human taste panel (n = 13) was used as reference method.A measurement protocol was developed for quality control and validated according to ICH guideline Q2. The validation showed that all sensors could be used. After measuring different samples, especially the sensors for saltiness and astringency showed adequate results in comparison to the HPLC and GC analysis. Two samples were found to have different taste properties which could be confirmed by low cineol content determined by gas chromatography. In comparison to human taste assessment the electronic tongue was more sensitive to distinguish between specified and outlying taste. Data evaluation based on univariate statistics as well as multivariate could be used to detect the differing samples. Therefore, the Insent taste sensing system seems to be a promising analytical tool for quality control of complex herbal mixtures.     

基于电子鼻技术的烤烟过程气味变化研究

为了掌握烟叶烘烤过程中气味变化的规律,用金属氧化物半导体传感器阵列组成的电子鼻对烤烟烟气进行实时监测。详细阐述了自制的电子鼻系统和实验过程,在对样本数据进行预处理后,采用主成分分析法对样本进行分析。分析结果表明:利用电子鼻技术得出的气味综合曲线能够真实有效地反映烟叶烘烤过程中的气味整体变化趋势,并且分析得出的烤烟过程中气味变化规律与现有烟叶烘烤理论较好地吻合。     

基于DOE的电子鼻实验优化方法

传统的电子鼻实验方法仅凭经验设定操作参数,难以保证实验的可重复性和最大有效性。将实验设计（DOE）方法引入电子鼻的实验研究中,利用中心复合设计快速确定实验点,以模糊修正综合响应最大为目标,对预富集-电子鼻系统的关键参数进行优化。理论分析与实验结果表明：该优化方法有利于电子鼻实验的可重复性开展,并能以最小的实验代价获取最大的实验信息。     

医用电子鼻传感器系统的发展

医用电子鼻是一种特殊的电子鼻系统,与通用电子鼻一样,传感器系统是它的核心。现有的气体传感器主要有金属氧化物半导体型、电化学型、导电聚合物型、石英微天平等几种,但将它们用于医用电子鼻都还有不足,因为医用电子鼻对传感器系统有更高的要求,它要求气体传感器谱度更广、检测限更低、灵敏度和稳定性更好。     

Fish freshness analysis using metallic potentiometric electrodes

One of the fields where the idea of developing artificial electronic mammalian sense devices might be widely applied is in food analysis. In this field, one very complex attribute yet of great importance is that of freshness. For instance, freshness is one of the most significant parameters of fish quality in most markets. The state of fish freshness can be assessed by various indicators but most of them are costly, time-consuming and lack of aptness for in situ or on site monitoring. This makes that the development of simple and undemanding systems for the evaluation of fish freshness is still an unresolved goal. Based on this demand, we focused our attention on the use of unselective sensors for the potential development of low-cost and easy-to-use devices for the estimation of freshness. After a preliminary study with a number of different electrodes, simple Au and Ag wires were used for the analysis of the evolution of sea (Sparus aurata) bream fish stored throughout time. PCA and neuronal network analyses showed that it is possible to determine the post-mortem time elapsed in minced gilthead sea bream using simple potentiometric measurements, suggesting that this method could be used for the analysis and evaluation of fish freshness. In order to somehow validate the potentiometric protocol for freshness assessment, the method has been compared with the determination of fish freshness based on the analysis of the concentration of certain bio-molecules as a function of time after the death of the fish. Among different possibilities the K₁ index that simultaneously measures the variation in the ATP metabolites inosine 5′-phosphate (IMP), inosine (Ino) and hypoxanthine (Hx) was used. A remarkable correlation has been found between the K₁ index determined during 7 days and the potentials of the Au and Ag electrodes.