Point-of-care diagnosis of bacterial pathogens in vitro,utilising an electronic nose and wavelet neural networks

Current clinical diagnostics are based on biochemical, immunological or microbiological methods. However, these methods are operator dependent, time-consuming and expensive and require special skills, and are therefore not suitable for point-of-care testing. Developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. In this paper, the potential of an electronic nose as a monitoring tool in clinical microbiology is investigated through two case studies. Initially, an electronic nose based on chemoresistive sensors has been employed to identify in vitro 13 bacterial clinical isolates, collected from patients diagnosed with pathological infections in a Public Health Laboratory environment, while in a later stage, analysis was carried out for urinary tract infection-suspected cases incubated in a volatile generation test tube system for 4–5 h. Two issues have been considered the application of an advanced wavelet neural network and the concept of the fusion of multiple classifiers dedicated to specific feature parameters. The adopted wavelet neural network incorporates a “product operation” layer between wavelet functions and output layers, while the connection weights at output layer have been replaced by a local linear model. This study has shown the potential for early and fast detection of microbial contaminants in clinical samples utilising advanced learning-based algorithms and electronic nose technology.     

嗅觉神经网络在电子鼻识别多品牌绿茶中的应用研究

生物模式识别机理引入人工嗅觉系统将提高其仿生化程度,并被认为是有前途的传感阵列信息处理方法。本文尝试将一种嗅觉神经网络应用到电子鼻检测和识别多种品牌的绿茶气味。通过包含8个MOS型气敏传感器的自制电子鼻仪器,测量了来着不同地方的5种不同品牌的绿茶样品,在传感阵列信号稳态部分提取特征向量,并使用雷达图考察指纹图谱异同,验证传感阵列及特征提取方法的有效性。采用生物相似性学习算法训练该神经网络,考察了样本训练次数和识别率的关系,发现经过4～7次训练,该网络对这5种绿茶的识别率平均值都在97%以上。     

一种基于电子鼻的食醋识别新方法

采用6只掺杂纳米ZnO厚膜传感器构成的电子鼻对15种商业食醋进行了测量,采用主元分析法(PCA)对电子鼻信号与食醋种类、配料、发酵方式和产地之间的关系进行了分析,电子鼻信号在上述食醋特征上表现出很强的聚类特性。针对上述特征,结合人工神经网络(ANN)、K近邻法(KNN)对15种食醋进行了识别,识别率为98.3%。研究表明:采用特征识别的电子鼻具有良好的灵活性和强健性。     

基于随机森林的隧道建成初期累计沉降量预测

隧道建设是公路建设中一类重要的工程,沉降是隧道质量监测的一项重要指标。为预防沉降带来的安全隐患,确保后期隧道正常运行,在建成初期对隧道的沉降累积量进行实时监测并对后期沉降作出较为准确的预测是很有意义的。采用随机森林模型对隧道建成初期的隧道累计沉降量进行预测,并将该模型的预测结果与深度神经网络模型做对比分析。结果表明,随...     

基于电子鼻技术的信阳毛尖茶咖啡碱检测方法

本文采用电子鼻系统对三个等级的信阳毛尖茶进行了检测.采用Loading分析和相关分析对传感器阵列进行优化,选出四个传感器为最终的新传感器阵列,用于信阳毛尖茶的品质识别.PCA分析结果显示,可以将不同等级的茶叶完全区分开,而且效果比较好.利用PCR、MLR和QPSa方法分别建立信阳毛尖茶基于气敏传感器阵列的咖啡碱预测模型...     

Electronic nose technique potential monitoring mandarin maturity

Over the past years, electronic nose technology opened the possibility to exploit information on behavior aroma to assess fruit ripening stage. The objective in this study was to evaluate the capacity of electronic nose to monitoring the change in volatile production of mandarin during different picking-date, using a specific electronic nose device (PEN 2). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used in order to investigate whether the electronic nose was able to distinguish among different picking-date (ripeness states). The loadings analysis was used to identify the sensors responsible for discrimination in the current pattern file. The results obtained prove that the electronic nose PEN 2 can discriminate successfully different picking-date on mandarin using LDA analysis. But, electronic nose was not able to detect a clear difference in volatile profile on mandarin using PCA analysis. During external validation using LDA was obtained to classified 92% of the total samples properly. Some sensors have the highest influence in the current pattern file for electronic nose PEN 2. A subset of few sensors can be chosen to explain all the variance. This result could be used in further studies to optimize the number of sensors.     

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.     

电子鼻技术在茶叶品质检测中的应用研究

以电子鼻作为检测手段,对同类不同等级的茶叶、茶水和茶底挥发性成分进行检测,并对采集到的数据进行分析。首先通过主成分分析进行特征提取来压缩数据维数,减少数据计算量,进而优化特征向量。然后采用线性判别和BP神经网络的方法对茶叶的不同等级进行分类判别。结果显示,误判样本都发生在T60和T100之间,两种判别方法结果比较一致。相对于茶叶和茶底,以各等级茶水为研究对象时,两种方法对茶叶品质等级的判别及测试结果相对都比较好。     

基于人工神经网络的苹果气体识别方法研究

将气体传感器阵列与人工神经网络模式识别技术相结合,建立了电子鼻系统,对水果变化过程进行监控。尝试对3种不同状态(好、碰伤、坏)的苹果气体进行定性识别。实验结果表明:结合主成分分析的人工神经网络方式为模式识别、分类提供了快速准确的辨识方法,对红富士苹果进行分类时正确率在83.33%以上。     

基于遗传小波神经网络的白酒识别电子鼻

为研究不同品质白酒快速识别的电子鼻技术,利用自制的电子鼻采集四种白酒样品的气味数据,建立了BP神经网络分类模型。针对BP算法普遍存在的收敛速度慢、易陷入局部极小且网络参数需要人工设定的缺陷,提出一种将遗传算法的自适应全局优化搜索能力、小波分析的非线性逼近能力和BP算法自学习能力结合在一起的遗传小波神经网络白酒识别模型。仿真结果表明,与BP神经网络和小波神经网络相比,GA-WNN分类模型的收敛速度和分类准确率都得到了较大提高,可应用于白酒识别电子鼻。     

网络监测节点序列部署问题的占线竞争算法设计

万维网的高速发展需要在网络内部构建部署相应的网络监测系统,但由于耗资巨大,在设计网络监测系统时,网络节点部署初期往往不能一次性监测完所有的边,只能选择有限的网络节点以监测少部分的边,再逐渐增加部署新的网络监测节点.在占线理论与竞争策略的基础上,研究网络监测系统网络节点序列占线优化部署问题,给出一个竞争算法,证明了该算法具有常数竞争比,该竞争比结果优于已有的结果.     

电子鼻在食品工业中的应用研究进展

电子鼻是一种新颖的模拟人的嗅觉来分析、识别和检测复杂嗅味和挥发性成分的仪器。电子鼻由气体传感器、信号处理和模式识别系统等组成。介绍了电子鼻的组成和工作原理,综述了电子鼻在食品原料、加工、质量监测、成份检验以及农产品贮藏中的应用研究进展,并指出了电子鼻目前存在的主要问题。     

基于FastICA和神经网络的电子鼻模式识别

气体传感器阵列是电子鼻系统的重要组成部分,传感器阵列的交叉敏特性严重影响电子鼻对气体识别的准确率.将快速独立分量分析算法和BP网络相结合用于电子鼻的模式识别可以有效地改善这一问题.并由一个5个传感器组成的电子鼻系统,对10组不同体积分数的3种气体测量得到的30组数据样本进行仿真.结果表明,用快速独立分量分析对数据作预处理,可以简化计算,减少数据之间的相关性,将预处理后的数据样本作为BP网络的输入,使网络结构简化,收敛速度快.利用该方法可以提高电子鼻识别气体的准确率.     

Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals

The potential of the electronic nose to monitor Longjing tea different grade based on dry tea leaf, tea beverages and tea remains volatiles was studied. The original feature vector was obtained from the response signals of the E-nose, and was analyzed by principal component analysis (PCA). To decrease the data dimension and optimize the feature vector, the front five principal component values of the PCA were extracted as the final feature vectors by PCA. The linear discrimination analysis (LDA) and the back-propagation neural network (BPNN) were proposed to identify Longjing tea grade. The results showed that the discrimination results and testing results for the tea grade were better based on the tea beverages than those based on the tea leaf and the tea remains based on the new five feature vectors; both of the LDA and BPNN methods achieved better discrimination for the tea grades based on the tea beverages and the analysis results of the two methods were accordance.     

用于易挥发性化学品检测的实用电子鼻算法研究

由气体传感器阵列构建的电子鼻系统在易挥发性化学品的快速检测中起着重要的作用.利用12 路气体传感器阵列采集易挥发性化学品蒸气的响应曲线,通过研究具有一定抗干扰能力的具有现场实用意义的电子鼻识别算法,提出了一种基于 BP 神经网络的算法来提取特征向量.所需计算量小,稳定性高,受浓度影响小;在此基础上进行神经网络的训练并对...     

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。     

电子鼻及其在食品领域的应用

介绍了电子鼻的基本原理和组成,不同种类的电子鼻由不同的传感器阵列组成。并在应用方面可以对不同酒类的气味进行鉴别判断;对肉类进行微生物分析;对奶制品进行在线控制;还能够检测饮用水和饮料中不同微生物和重金属的含量;对不同品质的茶类能够迅速识别。     

基于神经网络的导航卫星星间链路网络态势感知模型研究

为支撑导航卫星星间链路网络的稳定运行,提升对星间链路网络的监控管理能力,解决地面系统,对星间链路网络状态感知迟滞等监视能力不足问题,以及对未来网络趋势的预测能力不足问题,提出并构建了星间链路态势感知模型,包括数据预处理、指标计算和态势预测三部分;对于监视型网络状态指标,通过对原始遥测信息进行抽取获得;对于更为复杂的分析型网络状态指标,首先利用人工规则进行数据筛选,随后利用数据挖掘算法提取有效特征,选取相关性较大的参数综合得出复杂指标;在此基础上,利用BP神经网络对星间链路网络态势进行预测;通过实验,某状态指标预测的准确率稳定在98%左右,结果表明该模型可以有效的对星间链路网络的未来状态进行感知预测,可以对星间链路网络状态异常进行及时处理,具有较高的可行性。     

自适应概率神经网络及其在白酒电子鼻中的应用

为了探索电子鼻对白酒品质鉴别的可能性,利用自制的新型无线白酒电子鼻对洋河海之蓝、今世缘省接待、安徽迎驾大曲和牛栏山陈酿进行了分析.对所采集的数据进行平滑处理后提取稳态响应值和斜率值,利用主成分分析对特征向量进行降维处理,并将获得的前2个主元得分作为概率神经网络识别模型的输入参量.针对传统概率神经网络平滑因子σ单一易导致分类错误的缺陷,利用差异演化算法优化σ参数集,建立了自适应概率神经网络识别模型.实验结果表明,DE-PNN相比BP-PNN、PSO-PNN和SVM等,识别精度更高,抗噪性能更好,同时也证明了电子鼻能有效地检出不同品牌的白酒.     

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.