仿生嗅觉技术在微生物代谢产物气味检测中的应用研究进展

针对微生物方面的检测方法具有耗时长、昂贵、操作复杂等不足,经过多年的发展,仿生嗅觉技术已经能够应用实际问题中,并且没有传统检测方法的弊端。介绍了仿生嗅觉技术之一:电子鼻的主要三个组成部分(气体采集系统、气敏传感器阵列和模式识别系统),综述了电子鼻在食品和疾病微生物代谢产物检测中的应用。随着传感器技术和模式识别技术的发展,电子鼻在微生物代谢产物检测中有着很高的可行性,跟传统检测方法相比,电子鼻技术有着明显的优势。并对电子鼻的应用前景和发展趋势进行了展望。     

烃类混合气体的神经网络模型检测

八十年代末科学家模仿生物鼻研制一种传感器阵列与计算机模式识别的气体检测系统．传感器阵列相当于生物鼻的嗅觉细胞，计算机模式识别系统相当于嗅泡和大脑「‘］．传感器阵列对气体的响应是一个多维空间的响应模式，这种响应模式经过一定的数学处理后可以实现气体的种类识别或浓度检测［’－‘j．传感器的响应和混合气体浓度之间呈非线性关系，这一特性给定量检测多组分气体混合物造成很大的限制．应用人工神经元网络技术（ANN）可以克服这一缺陷，并使检测气体的选择性大大提高．本工作运用ANN中的反向传播（BP）算法识别由16个不同…     

压电晶体微天平阵列传感器识别毒剂的研究

压电晶体微天平(QCM)阵列传感器在毒剂侦检领域具有广泛的应用前景。本研究建立了QCM阵列传感器毒剂检测系统,以氢键酸性共聚硅氧烷(BSP3)、聚表氯醇(PECH)和乙基纤维素(ECEL)为膜材料制备了对毒剂敏感的QCM阵列传感器,对沙林、芥子气、甲基膦酸二甲酯进行了定量检测,并结合模式识别方法对检测结果进行了分析处理,识别率达到98%以上,为探索QCM阵列传感器对毒剂的定性定量分析提供了方法依据。     

新型甲醛多孔硅复合传感器的制备

构建了一种简便、快速检测甲醛的新型钯-多孔硅(Pd-PS)复合传感器.采用水热腐蚀法制备多孔硅,通过扫描电镜表征其表面微结构.对多孔硅腐蚀条件进行了优化,得出多孔硅的最佳制备条件.多孔硅经化学浸渍法在其表面掺杂金属钯,进而制成了钯-多孔硅复合传感器.当此传感器被置于含甲醛的混合气体中时,可高选择性结合甲醛气体分子,并产生电信号,其强度与甲醛浓度相关,通过万用表检测其电信号,进而分析其气敏性能.检测结果表明,此传感器对甲醛气体敏感,且表现出良好的选择性,对乙醇、氨气、甲醇和丙酮不敏感.此传感器对甲醛浓度的检测范围在0.1～ 6.0 mg/m3之间,检出限为0.1 mg/m3,检测时间为3 min.     

模式识别在食品质量控制方面的应用进展

本文介绍了食品质量研究中常用的一些化学模式识别方法的基本原理,并介绍了模式识别结合红外、原子吸收、原子发射、气相色谱、液相色谱、质谱、电子鼻传感器等检测技术在食品质量控制中的应用.对化学计量学在食品质量控制中的应用前景作了展望.     

基于随机共振和微纳传感器阵列的六氟化硫检测方法研究

建立了一种基于随机共振和微纳传感器阵列的六氟化硫气体检测方法.使用两个360 μm极间距离的微纳传感器构成的阵列结合随机共振算法,在室温常压,相对湿度70%的条件下检测3.0～24 g/m3 SF6气体及纯SF6气体,以信噪比极大值实现各浓度SF6气体的区分.传感器工作于微电离平衡状态,检测过程中无有害气体产生,安全性较高.为检验该方法的实用性,依据国家标准在温度20 ℃、相对湿度70%和常压条件下模拟现场检测3.0～12 g/m3SF6气体,结果表明: 本方法具有较好的重复性和实用性.     

空心纳米金在甲醛气体传感器中的应用研究

通过牺牲模板法合成了具有空心结构的纳米金催化剂,并进行了TEM、 SEM和XRD等物理表征.把该催化剂作为工作电极的活性物质,以1 mol/L KOH为电解质,组装了电流型甲醛气体传感器.在甲醛气体浓度为0～2.23×10-6 mol/L范围内对传感器进行了性能测试,传感器响应信号y(A)与气体浓度x(mol/L)线性回归方程为y=16.63x+4.063×10-7, r=0.9989.该传感器灵敏度高于同载量实心金纳米催化剂组装的传感器70%左右,达到了降低贵金属用量的目的.因其具有较快的响应时间、 良好的重现性和良好的线性关系等优点,可用于适当浓度范围内的甲醛气体检测.     

室内甲醛含量可自测

一种便携式气体检测仪新近被引入厦门市场,它不仅能在工矿企业发挥作用,也能用在寻常百姓家庭,随时判断瓦斯、甲醛是否超标。这种检测仪结构简单,主要由气体采集器与检测管组成。其工作原理是利用手动抽气装置抽入需要检测的气体,检测剂一旦和被检测气体发生反应,会出现鲜明且稳定的颜色变化层,通过该变色层与玻璃管上的刻度相对照,来表示特定气体的浓度。这种检测仪还特别“灵敏”,可以直接对400多种气体进行检测,覆盖生产生活的大部分领域。由于这个方法简便、准确,在许多发达国家已将其确定为法定的检测方法之一。由于携带方便,这种检测仪同样可以成为居家生活的一部分。厨房里瓦斯有没有泄漏,新装修的房屋是否甲醛超标,车内气味不好是不是苯多了,通过这个检测仪就一目了然了。可以说,该便携式检测仪实现了检测的平民化。目前该便携式气体检测仪报价1000多元。室内甲醛含量可自测@林     

气体传感器和电子鼻

通过实例介绍了气体传感器的原理及应用，并对以气体传感器为基础，模拟生物体嗅觉系统的智能型传感器———电子鼻，做了简单介绍。     

二氧化锡气体传感器对有机磷农药残留的动态检测

研究了一种快速检测和识别农药敌百虫、乙酰甲胺磷和乐果气体的新方法,即动态检测方法。这种方法利用单个SnO2气体传感器而非阵列在方波温度调制的状态下可实现3种农药气体的快速检测。实验结果表明：在0．02Hz的调制频率和250—300℃的温度调制范围,传感器表现出高的选择性和稳定性;利用FFT频谱和极坐标构建完成对敌百虫、乙酰甲胺磷和乐果的定性和定量检测。     

Electronic Noses Using Quantitative Artificial Neural Network

The present paper covers a new type of electronic nose (e-nose) with a four-sensor array, which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error (RE) in changing quantitative analysis into the artificial neural network (ANN). Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, iiquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.     

Electronic Noses Using Quantitative Artificial Neural Networ

The present paper covers a new type of electronic nose(e-nose) with a four-sensor array,which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error(RE) in changing quantitative analysis into the artificial neural network (ANN).. Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, liquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.     

Quantitative fuzzy neural network for analytical determination

A quantitative fuzzy neural network (Q-FNN) for pattern recognition in analytical determination is reported in this paper. The fuzzy neural network (FNN) combines a fuzzy logic system with an artificial neural network (ANN) so that it has both advantages of a high training speed and strong anti-interference. Importantly, the analytical concept of relative error (RE) in quantitative determination has been integrated into FNN so that the Q-FNN provides a very good quantitative capability in chemical analysis, and prevents the system from an over-fitting problem. The logarithm curve with noise in terms of analytical response versus concentration is calibrated by trained FNN and a close approximation to the ideal one without noise is obtained. The Q-FNN has been applied to the concentration determination of freon in the presence of interference gases. The prediction error for a test set in quantification is less than 10% while no qualitative mistake is observed, implying that the quantitative FNN has sustained the feature of pattern recognition. The results indicate that the Q-FNN has obvious advantages not only in converging speed, but also in the quantitative accuracy over the ANN.     

Quantitative determination of Freon gas using an electronic nose

This paper reports a quantitative electronic nose (enose) for the quantitative determination of Freon gas within the concentration range 0-1000 ppm in the presence of interfering gases such as water, lubricant and petrol vapours. This quantitative enose is a new type of Freon detection system, composed of an array of four sensors. The artificial neural network (ANN) and fuzzy logic type of ANN (FNN), in combination with the relative error concept in analytical chemistry, are integrated for both quantification and discrimination. The predicted results are satisfied with a pass rate of > 80% within the permitted relative errors. The results show that the Freon enose developed in this study is reliable for both the qualitative and quantitative determination of Freon gas and exhibits the merits of high sensitivity, anti-interference and accuracy.     

Enhancing Classification Performance of Multisensory Data through Extraction and Selection of Features

Linear discriminant analysis (LDA) has been widely used in the classification of multi sensor data fusion. This paper discusses the performance of LDA when the classifications were performed based on feature extraction and feature selection methods. Comparisons were also made based on single sensor modality. These strategies were studied using a honey dataset along with two types of sugar concentration collected from two types of sensors namely electronic nose (e-nose) and electronic tongue (e-tongue). Assessment of error rate was achieved using the leave-one-out procedure.     

Threshold detection of aromatic compounds in wine with an electronic nose and a human sensory panel

An electronic nose (e-nose) based on thin film semiconductor sensors has been developed in order to compare the performance in threshold detection and concentration quantification with a trained human sensory panel in order to demonstrate the use of an e-nose to assess the enologists in an early detection of some chemical compounds in order to prevent wine defects. The panel had 25 members and was trained to detect concentration thresholds of some compounds of interest present in wine. Typical red wine compounds such as whiskeylactone and white wine compounds such as 3-methyl butanol were measured at different concentrations starting from the detection threshold found in literature (in the nanograms to milligrams per liter range). Pattern recognition methods (principal component analysis (PCA) and neural networks) were used to process the data. The results showed that the performance of the e-nose for threshold detection was much better than the human panel. The compounds were detected by the e-nose at concentrations up to 10 times lower than the panel. Moreover the e-nose was able to identify correctly each concentration level therefore quantitative applications are devised for this system.     

Kinetic determination of formaldehyde and hexamethylenetetramine with a cyanide-selective electrode

An automatic potentiometric reaction-rate method is described for the determination of formaldehyde and hexamethylenetetramine. The formaldehyde reacts with cyanide, and the reaction rate is followed with a cyanide-selective electrode. The time required for the reaction to consume a fixed amount of cyanide, and therefore for the potential to increase by a preselected amount (8.0 mV), is measured automatically and related directly to the formaldehyde concentration. The average error for the determination of 60–300 μg of formaldehyde in a sample volume of 1.00 ml was about 1.3%. Amounts of hexamethyl-enetetramine in the range 50–250 μg in a sample volume of 0–050 ml were determined after acid hydrolysis to formaldehyde with an average error of about 1.6%. Measurement times were in the range 18–80 s for both determinations. The method has been applied to the determination of hexamethylenetetramine in pharmaceutical preparations.     

Analytical methods for the quantification of volatile aromatic compounds

The investigation of odorants is not an easy task, which needs to be undertaken in the context of fit-for-purpose quality systems. To date, great attention has been paid to determination of the volatile fractions of odorants, since they are responsible for the attributes of global flavor [i.e. a combination of olfactory (aroma) and gustatory (taste) sensations produced by chemicals]. This kind of determination can be carried out by analytical techniques [e.g., gas chromatography (GC) combined with mass spectrometry and/or olfactometric GC]. Methods complementary to GC analysis are available, allowing assessment of the olfactory impact by an electronic nose (e-nose) or a panel of selected individuals. Also, we consider some innovative analytical techniques to study the effects of odorants in food during consumption.     

Fluorometric determination of formaldehyde

A simple, selective and sensitive fluorometric method is presented for the determination of formaldehyde in water based on its reaction with 3,4-diaminoanisole in alkaline ethanol-water solution to give a strongly fluorescing Schiff base. The dependence of the fluorescence intensity on the solvent composition, quenching by acetic and sulphuric acid, heating time and interference by other compounds is discussed. The detection limit of the method is 0.6 μg/L. The recovery of formaldehyde spiked into river water is 93% with an R.S.D of 6.05% at a concentration level of 10 μg/L.     

基于气液相化学发光技术的臭氧在线检测方法

基于鲁米诺(luminol) 化学发光体系,采用自主研发的在线臭氧浓度检测仪,建立了一种实时在线检测臭氧浓度的方法,用于分析测定痕量浓度水平的臭氧气体。 考察了鲁米诺、氢氧化钾、部分醇类化合物和表面活性剂等因素对化学发光强度的影响。 结果表明,在鲁米诺(0.005 mol/L)、氢氧化钾(0.05 mol/L)体系中加入乙二醇(体积分数1.5%)、甲醇(体积分数1.5%)、乙醇(体积分数1.0%)、丙三醇(体积分数3.0%)能显著增强鲁米诺体系检测O₃的化学发光信号,而甲醛溶液 (体积分数3.0%)能有效抑制NO₂信号的干扰。 同时,测得检测臭氧的检出限为1.26 μg/m³、相对标准偏差为0.32%,相对误差为0.75%。 利用该体系测定臭氧,具有信号稳定、精密度好、准确度高、检出限低等优点,适用于大气中微量O₃的在线连续检测。