Using a metal oxide sensor (MOS)-based electronic nose for discrimination of bacteria based on individual colonies in suspension

Timely and accurate determination of the presence and nature of foodborne bacterial pathogens is of utmost importance in food safety. In this paper, we investigated whether an electronic nose (based on a metal-oxide sensor array) could distinguish between E. coli and Listeria. Unlike other studies in this area, samples consisted of individual colonies transferred from agar plates, then suspended in phosphate buffered saline. Features extracted from the sensor response curves capture both static (steady state) and dynamic (slope) information. The use of a linear classifier in association with supervised dimensionality reduction, using uncorrelated linear discriminant analysis (ULDA), yielded classification accuracies of 92.4%. The proposed method has the potential to reduce the overall time required to identify bacterial pathogens. This type of sample is available relatively early in the inspection process, so discrimination based on the odour signature of single colonies has the potential to reduce time and cost by eliminating or reducing subsequent culturing stages and biochemical testing. The results presented herein suggest that further research in this area is warranted, particularly with a wider variety of bacterial species.     

A sensor array optimization method for electronic noses with sub-arrays

Sensor array configuration needs to be optimized in developing an application-specific instrument of electronic noses. Currently, sensor array optimization is commonly done by feature selection techniques. These methods could solve how to optimize a sensor array. However, they could not figure out what are the unique functions that each sensor plays in the optimized sensor array. The method proposed in this paper could solve this problem by sensor clustering and dividing the whole sample classification mission into several small recognition tasks. A measurement with a six Taguchi Gas Sensors (TGS sensor hereinafter) sensors array to classify 11 gas sorts was used in the data validation. The sensor array was optimized to three sensors with the proposed method. Each sensor in the optimized array had unique functions to solve different recognition tasks. TGS2600 had the unique functions to discriminate butanone and acetaldehyde. TGS2602 had the unique functions to discriminate benzene and cyclohexane, methanol and ethanol. TGS813 had the unique functions to discriminate cyclohexane and pentane. The combination of TGS2600 and TGS2602 had the unique functions to discriminate acetone and butanone, acetone and acetaldehyde. The proposed method might be a new generation of sensor array optimization methods.     

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.     

Chemical warfare agents simulants detection with an optimized SAW sensor array

An E-nose based on surface acoustic wave (SAW) sensors has been developed, and sensitive polymer coatings have been optimized to detect simulants of chemical warfare agents (CWAs). The polymers selected have allowed to discriminate among simulants and classify them at low concentrations in air through Pattern Recognition Methods. Good detection responses have been achieved for very low concentrations, such as 0.05 ppm for Dimethyl methylphosphonate (DMMP) and 0.5 ppm for dipropylene glycol monomethyl ether (DPGME).     

Odour quantification by a sensor array: An application to landfill gas odours from two different municipal waste treatment works

The possibility of quantifying the landfill gas (LFG) odour in terms of odour-units per cubic meter (ou/m³) using a tin oxide sensor array is investigated. The objective is to determine the most appropriate neural machines (MLP networks, RBF networks) model to perform the odour concentration approximation and evaluate the influence of multiple biogas sources modelling on the approximation quality. The structural risk minimization principle is used instead of the usual empirical risk minimization principle in the training algorithm of the neural machines. Multilayer perceptrons (MLP) networks prove to minimize best the error on the prediction of odour concentration of unknown data. The data is constituted of LFG odour samples from two municipal waste treatment works presenting different concentrations of odorous compounds. It is shown that the quality of the LFG odour approximation is in the present case influenced directly by the size of the training data set. The use of data coming from two different sources is not detrimental to the quality of the approximation.     

气体传感器阵列测试算法研究

采用人工神经网络中的BP神经网络处理阵列式气体传感器信号,气体传感器阵列与前馈神经网络模式识别技术结合而成的人工嗅觉系统被用来进行混合气体的定量分析.利用计算机模拟方法对非线性气体传感器阵列进行模拟,并运用MATLAB神经网络工具箱设计了BP网络,最后,对模拟数据进行了比较.结果表明:神经网络法具有非线性逼近能力强、识别率较高等特点.     

识别和计数微生物细胞的 伏安型生物传感器的研究

报道了一种半微分循环伏安型生物传感器，可同时完成对细胞的识别数。该传感器由一个平面热解石墨电极、铂电极及Ａｇ／ＡｇＣｌ电极的三电极系统组成。将阻留微生物细胞的滤膜紧附在工作电极表面，然后在工作电极与对极间施加一扫描电压，进行半微分循环伏安扫描，记录伏安图谱 。     

A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine

We report herein the use of a combined system for the analysis of the spoilage of wine when in contact with air. The system consists of a potentiometric electronic tongue and a humid electronic nose. The potentiometric electronic tongue was built with thick-film serigraphic techniques using commercially available resistances and conductors for hybrid electronic circuits; i.e. Ag, Au, Cu, Ru, AgCl, and C. The humid electronic nose was designed in order to detect vapours that emanate from the wine and are apprehended by a moist environment. The humid nose was constructed using a piece of thin cloth sewn, damped with distilled water, forming five hollows of the right size to introduce the electrodes. In this particular case four electrodes were used for the humid electronic nose: a glass electrode, aluminium (Al), graphite and platinum (Pt) wires and an Ag-AgCl reference electrode. The humid electronic nose together with the potentiometric electronic tongue were used for the evaluation of the evolution in the course of time of wine samples. Additionally to the analysis performed by the tongue and nose, the spoilage of the wines was followed via a simple determination of the titratable (total) acidity.     

A fully neural implementation of unitary response model for classification of gases/odors using the responses of thick film gas sensor array

In this paper, a classification scheme based on neurally implemented unitary response model (URM) for a gas/odor sensor array response has been presented. Thick-film tin-oxide sensor array responses for four gases/odors (viz. acetone, carbon tetra-chloride, ethyl methyl ketone and xylene) were first transformed into equivalent unitary responses. This transformation was carried out using a pre-trained neural ‘unitary response model pre-processor (URMP)’, called Net I_URMP. The classification of these responses in the unitary analysis space was then carried out, more accurately, using a pre-trained neural classifier called Net II_URMC. During this experiment, respective nets Net I_URMP and Net II_URMC, comprising of 12 and 8 neurons, were trained in just 23 and 09 epochs of 42 × 4 training response vectors. At stage I, the mean squared error (MSE) between neurally and mathematically obtained unitary response versions of 18 independent test responses for the considered gases/odors was 7.51 × 10⁻². At stage II, all the aforesaid test samples were correctly classified, with a MSE of 3.87 × 10⁻⁸. Further, by connecting Net I_URMP and Net II_URMC in cascade, the proposed classifier could be implemented using 16 neurons only.     

Orthogonal gas sensor arrays with intelligent algorithms for early warning of electrical fires

The feasibility of utilizing chemical sensor arrays and multivariable analyses as the basis for an early-warning combustion alarm for electrical fires was evaluated. During the pre-combustion phase of electrical fires, electronic components will heat up, resulting in an out-gassing of chemical vapors, which generally will precede the formation of smoke, scorching and fire. A variety of materials (PVC, Teflon^®, Kapton^®, and silicone rubber) that are frequently used as wire insulation were subjected to electrically induced thermal excursions, thereby simulating an electrical failure and possible pre-combustion condition. The off-gassing vapors from the various coatings can serve as chemical signatures for a pending fire and were detected by an array of chemical sensors (e.g., an electronic nose). Principal component analyses and KNN identification algorithms applied to the sensor response patterns successfully identified the various vapor sources. A 20-sensor array including electrochemical sensors, quartz microbalance (QMB) sensors with different polymer coatings, and heated metal oxide sensors (MOXs) was evaluated and the optimal performance was obtained using the electrochemical and MOXs. The use of heterogeneous orthogonal sensors increased the information content of sensor array signals and a diminutive array can still identify fire materials and extent of damage. The small, lightweight, inexpensive and low power sensors used to detect vapors during pre-fire conditions were ideal for space or commercial aircraft applications.     

Preparation, characterisation and applications of thin films for gas sensors prepared by cheap chemical method

The physical properties of pure and doped (Ni, Os, Pd and Pt) SnO₂ thin films, prepared by using a chloride-based inorganic sol–gel route, have been reported. These properties were investigated by using differential scanning calorimetry and thermogravimetric (DSC/TG), FTIR, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and atomic force microscopy (AFM) techniques. Microsensors for gas detection were also fabricated and tested in various atmospheres (nitrogen oxide, carbon monoxide, ethanol, methanol and methane). The dopants affected the coating properties depending on the formation of nanoparticles. Furthermore the gas-sensing properties towards reducing and oxidising gases were found to be dependent on the nature of doping element. An array consisting of the manufactured sensors has been arranged and examples of applications for “electronic nose” are given.     

Detection of 4-chlorobenzoate using immobilized recombinant Escherichia coli reporter strains

A test for the detection of 4-chlorobenzoic acid (4-CBA) based on reporter microorganisms is developed. A 1.7-kb DNA fragment, upstream of the dehalogenase operon from Arthrobacter sp. strain SU, is fused to the promoterless luciferase operon of Vibrio fischeri. This reporter construct is introduced into two different Escherichia coli strains (UTL2 and RFM443), which specifically respond to the presence of 4-CBA with light emission. The development and optimization of an alginate-based immobilization procedure of the microorganisms in microtiter plates is described. In this format, 4-CBA can be detected in the concentration range of 113 μM–3.6 mM. The membrane leaky mutant UTL2 of E. coli transformed with the reporter construct improves the detection limit to 28 μM when 150 mM KNO₃ is added to the medium.     

A hybrid chemiresistive sensor system for the detection of organic vapors

A five node sensor array, consisting of three films of gold nanoparticles functionalized with p-terphenylthiol, dodecanethiol and mercapto-(triethylene glycol) methyl ether, and films of poly(3-hexylthiophene) and polypyrrole, was integrated into a portable, microprocessor-based system. The system was evaluated for the detection of chloroform, diisopropyl methylphosphonate (DIMP), ethanol, hexane, methanol, and toluene vapors. Direct comparison of the five sensor films with respect to sensitivity, response time and recovery time was made by measurement of the resistance changes upon simultaneous exposure to each analyte. In general, the sensor films responded, with greatest sensitivity, to organic analyte molecules with similar chemical functionality (e.g., polarity). For example, the dodecanethiol-functionalized gold nanoparticle film sensor excelled at detecting hexane, while the mercapto-(triethylene glycol) methyl ether-functionalized nanoparticle film exhibited superb detection of ethanol and chloroform. Although the poly(3-hexylthiophene) film was very sensitive to polar analytes, including DIMP, in many cases it suffered from relatively long recovery times. Following training of the sensor system, successful differentiation and detection of the analytes were realized using a relatively simple algorithm based on “minimization of the squares of differences” method. The ability of the system to optimally differentiate these analytes is considered within the context of principal component analysis, and the effects of long-term sensor drift are discussed.     

检测大肠杆菌O157:H7的电化学阻抗谱生物传感器的研究

我们提出了用掺锡的三氧化二铟(ITO)作为工作电极,通过硅烷化固定化技术,将抗大肠杆菌O157:H7单克隆抗体固定在ITO电极表面,利用电化学阻抗谱技术来构建一种新型的免疫传感器.该新型的免疫传感器的检测限为 4×103CFU/mL,检测线性范围为4×103-4×106CFU/mL.实验研究表明,该传感器具有灵敏度较高,检测时间短,操作简单等优点,在临床医学和环境监测中具有应用价值.     

基于金属氧化物传感器阵列的小麦霉变程度检测

研制了一套由8个金属氧化物传感器组成、用于检测小麦霉变的电子鼻系统.使用该电子鼻对不同霉变程度和掺入不同百分比含量霉麦的小麦样品进行检测.通过方差分析和主成分分析优化传感器阵列并去掉冗余传感器,对优化后的数据进行主成分分析(PCA)和线性判别分析(LDA),其中PCA的前两个主成分对两类实验结果分析的总贡献率为98.30％和99.27％,LDA前两个判别因子对两类实验结果分析的总贡献率为99.68％和93.30％,且由得分图可知两种方法均能很好地区分不同的小麦样品.利用BP神经网络建立预测模型,对样品菌落总数和掺入样品中霉麦的百分比进行预测.两种预测模型的预测值和测量值之间的相关系数分别为0.91和0.94,表明预测模型具有较好预测性能.     

电子鼻信号特征提取与传感器优化的研究

采用PEN2型电子鼻系统对芝麻油的玉米油掺假进行定性鉴别和定量预测,运用主成分分析,逐步判别分析和Fisher线性判别函数变换对原始数据进行预处理,从而降低原始数据空间的维数,并用判别分析与人工神经网络对数据进行进一步分析,考察了不同的数据预处理方法的效果.判别分析结果表明,采用Fisher线性判别函数变换所得到的十个变量判别能力最强,误判率为0.61%,仅有1个样品出现误判.在BP神经网络的定量预测中,采用逐步判别分析所筛选出的十个变量作为网络输入,所得的预测结果最为理想,绝对误差个体值的95%置信区间最小,为(-4.71%,3.38%),均方误差为4.75,预测值与实际值之间有极显著的相关性,相关系数R=0.998 08.     

基于气体传感器动力学模型的细菌分类研究

对于气体传感器获得的样本数据,常规的处理方法是基于样本的表象来提取特征进行分类,具有固有的局限性.基于热电子发射理论和等温吸附理论,对气体传感器的电压响应值与温度、样本浓度等参数建立数学方程,将方程简化,构造了简易的响应动力学模型.通过主成分分析,降低样本维数.将模型向降维后的样本数据拟合,可得到模型的一组系数,作为样本的特征值.将特征值集合运用模式识别方法进行训练,测试分类性能.实验结果显示:分类预测的准确率较高.     

Wireless electronic nose system for real-time quantitative analysis of gas mixtures using micro-gas sensor array and neuro-fuzzy network

A wireless electronic nose system (WENS) is designed for the real-time quantification of ammonia (NH₃), hydrogen sulfide (H₂S), and their mixtures. The WENS hardware consists of a microcontroller for obtaining measurement data from a micro-gas sensor array, and an RF transceiver for transmitting the data sets to a master sensor node. Meanwhile, the WENS software analyses the binary gas mixtures using a fuzzy ARTMAP classifier and a fuzzy ART-based concentration estimator with multiplicative drift correction based on reference gases. A virtual instrument is developed in the LabVIEW environment for monitoring the analyzed gas mixtures. The performance of the proposed WENS is also assessed and compared with the minimum and product inference methods. The proposed WENS adopting the weighted inference method produces the best concentration estimations as regards the root mean square error.     

百病特灵可溶性粉治疗雏鸡大肠杆菌病的试验研究

选用杯碟琼脂扩散法和染菌动物药物治疗法,验证和评价新兽药百病特灵可溶性粉对鸡大肠杆菌的体外抗菌的体外抗菌效能和雏鸡大肠杆菌病的疗铲,结果证实,本药对此菌的抑菌圈直径均值,比对照药盐酸恩诺沙星大１．１６倍;饮服百病特灵可溶性粉水溶液的最适治疗浓度为３０～６ｍｇ．Ｌ＾－１,每天２次,给鸡饮服,连用３～５ｄ,对此病的治愈率均达１００％,相对增重率分别为１３８．９８％～１８０．６３％。比阳性对照组鸡死亡率     

Electronic nose and sensorial analysis: comparison of performances in selected cases

Sensorial analysis based on the utilisation of human senses, is one of the most important and straightforward investigation methods in food analysis. It provides a unique information about the ‘food–man interaction’. Nevertheless, human senses, when considered as instruments, show several problems of reproducibility, stability and difficulties of expression, making it very hard to compare results between different panels. The electronic nose has been proven to be sufficiently accurate as an artificial approximation of the human olfaction apparatus when applied to well defined problems in food analysis. In this paper results obtained for tomato pastes and milk analysis, comparing a panel of tasters and an electronic nose will be presented and discussed.