基于多BP子网络的电子鼻信息融合技术

针对电子鼻技术的应用中,利用常规BP(back-propagation)网络难以对复杂的混合气味进行识别这一问题,介绍了一种采用多BP网络联合进行电子鼻多传感器数据融合处理的新方法。利用这一方法并结合气体传感器阵列在不同温度条件下的响应信号,对不同品牌的葡萄酒样本进行定性识别,仿真验证结果表明该方法的识别准确率达到100%。     

最小二乘法在电子鼻动态信息融合中的应用

基于集成气敏传感器阵列和多传感器信息融合技术的电子鼻系统,可用于气体/气味的定性定量识别.电子鼻走出实验室的关键是高效的识别算法和简化的实验装置.本文提出最小二乘法在简化的电子鼻系统中的应用模型.利用线性最小二乘法拟合传感器阵列的动态响应曲线,建立电子鼻系统的知识库并进行智能识别.利用该方法并结合气体传感器阵列在不同温度条件下的动态响应信号,对雪碧、酷儿和绿茶三种饮料样本进行定性识别,仿真验证结果表明该方法的识别准确率达到100%.     

基于集成气敏传感器阵列的电子鼻系统环境响应特性分析

基于集成气敏传感器阵列和多传感器信息融合技术的电子鼻系统 ,可用于气体 /气味的定性定量识别 .在实际应用中 ,被识别样本的浓度、样本环境的温度和湿度参数等都对电子鼻系统的响应特性有较大的影响 .本文分别利用纯净空气及乙醇或葡萄酒作为载气及分析样本 ,对电子鼻系统响应特性与气敏传感器工作温度以及样本温度、环境湿度、样本浓度间的依赖关系进行了实验分析 ,并分别给出了它们间依赖关系的实验结果 .     

基于集成气体传感器阵列的电子鼻系统

介绍一种基于集成气体传感器阵列的电子鼻系统硬件实现方法。描述了系统的一般组成,重点介绍了所采用的集成气体传感器及其信号拾取方法。该系统可实现对检测气体的自动数据采集、微机传送,具有高的灵敏性和可靠性。另外,对采用的集成气体传感器系列,该电子鼻系统具有一定的通用性。利用该系统对五种葡萄酒进行了检测,结果表明:该电子鼻系统可有效地用于葡萄酒的定性识别,识别准确率达到100%。     

基于独立分量分析和BP网络的电子鼻模式识别

为了提高电子鼻对混合气体的识别率,针对气体传感器阵列的交叉敏感特性,探讨了在电子鼻系统中基于独立分量分析(ICA)算法与BP神经网络相结合进行模式识别的可行性。并对4个气体传感器组成的电子鼻对4种气体混合物所测得的原始数据进行处理,结果表明:ICA算法对数据进行有效预分类,减少了样本之间的相关性,将生成的新样本作为BP网络的输入,使网络结构简化,在保证一定正确率的前提下,大大提高网络的学习速度。利用该方法可以提高电子鼻识别混合气体的准确率。     

基于人工神经网络的电子鼻对混合气体检测研究

在简要概述电子鼻系统原理的基础上,分析人工神经网络模式识别的特性、结构和识别原理,阐述一种基于人工神经网络的混合气体检测方法.结果证明通过人工神经网络的模式识别和气体传感器阵列技术相结合,能够有效地解决气体传感器阵列的交叉敏感问题,从而实现对混合气体的定性定量检测,并且拥有广泛的应用前景.     

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.     

用于室内有毒气体快速检测的便携式CC/SAW电子鼻

开发了一种用于室内空气质量快速检测的便携式电子鼻气体分析仪.该仪器利用毛细管分离柱(CC)将混合气体选择性分离,实现对气体的分类识别,然后借助声表面波(SAW)传感器频率响应测量每种组分对应的浓度,实现对气体的量化.实验针对17种有毒有害气体进行测试,结果表明该系统能对低浓度复杂混合气体进行快速检测,且具有较高的灵敏度,良好的选择性和重复性.这为不同应用场合下的痕量气体检测提供了一种可行的技术.     

可视嗅觉系统分析与设计

新型的气体检测装置是根据传感器敏感单元遇到不同气味所呈现的颜色差异,通过将气味的特征信息转换为图像信息识别气体,具有检测精度高和受环境湿度影响较小的优点.文章分析了传感器的工作原理和响应图像,给出了消除杂散光对图像干扰的处理方法和图像识别算法,介绍了基于ARM/Linux的嵌入式系统作为电子鼻实现平台的思路.利用该系统准确识别了环己胺、二甲基亚砜和乙腈三种有机化合物,验证了所构造的系统的有效性.     

电子鼻牛奶质量检测的研究

利用自主研制的CN e-Nose I型电子鼻气体分析仪对多种品牌、不同新鲜程度的牛奶进行检测,并通过模式识别方法分析和识别数据.牛奶各组分经由金属氧化传感器阵列采集信号,其数据及响应曲线记录在PC端.运用主元分析和人工神经网络方法识别曲线特征,通过与新鲜高质量样本的标准数据进行比较,判断出牛奶变质程度.实验结果表明,电子鼻技术对牛奶品质的识别率较高,且具有便捷、安全等特点,是一种发展前景良好的实用技术.     

Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA

Technological progresses in the gas sensor fields provide the possibility of designing and construction of Electronic nose (E-nose) based on the Biological nose. E-nose uses specific hardware and software units; Sensor array is one of the critical units in the E-nose and its types of sensors are determined based on the application. So far, many achievements have been reported for using the E-nose in different fields of application. In this work, an E-nose for handling multi-purpose applications is proposed, and the employed hardware and pattern recognition techniques are depicted. To achieve higher recognition rate and lower power consumption, the improved binary gravitational search algorithm (IBGSA) and the K-nearest neighbor (KNN) classifier are used for automatic selecting the best combination of the sensors. The designed E-nose is tested by classifying the odors in different case studies, including moldy bread recognition in food and beverage field, herbs recognition in the medical field, and petroleum products recognition in the industrial field. Experimental results confirm the efficiency of the proposed method for E-nose realization.     

不同产地和采收期的中药材电子鼻鉴别研究

不同产地和采收期的同种中药材在感官上很难区分.提出一种在自然态下采用PEN3电子鼻鉴别不同产地和采收期中药材的方法.首先通过电子鼻获取样本的气味信息,综合气味信息各方面的特征组成一个特征集合,采用逆向反馈方法提取特征,获得最能区别样品的特征子集,利用PCA+LDA算法对样本进行分类,最后运用欧式距离、马氏距离对未知样本...     

一种基于动物捕食行为的机器人气味源定位策略

提出了一种基于动物捕食行为的机器人味源定位策略;该策略融合了气体传感器、风向传感器、超声传感器等传感器信息,考虑了搜寻味源过程中的避障及重复搜索问题,并给出了确认味源的条件.在动态的室内环境下,令机器人搜寻酒精泄漏源,结果表明,这种策略具有较高的搜寻效率和成功率.     

Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools

Electronic nose (E-nose) technique was attempted to discriminate green tea quality instead of human panel test in this work. Four grades of green tea, which were classified by the human panel test, were attempted in the experiment. First, the E-nose system with eight metal oxide semiconductors gas sensors array was developed for data acquisition; then, the characteristic variables were extracted from the responses of the sensors; next, the principal components (PCs), as the input of the discrimination model, were extracted by principal component analysis (PCA); finally, three different linear or nonlinear classification tools, which were K-nearest neighbors (KNN), artificial neural network (ANN) and support vector machine (SVM), were compared in developing the discrimination model. The number of PCs and other model parameters were optimized by cross-validation. Experimental results showed that the performance of SVM model was superior to other models. The optimum SVM model was achieved when 4 PCs were included. The back discrimination rate was equal to 100% in the training set, and predictive discrimination rate was equal to 95% in the prediction set, respectively. The overall results demonstrated that E-nose technique with SVM classification tool could be successfully used in discrimination of green tea's quality, and SVM algorithm shows its superiority in solution to classification of green tea's quality using E-nose data.     

Rank extraction in tin-oxide sensor arrays

It is shown that data pre-processing by rank-order filtering can significantly improve the odor discrimination capability of an array of chemical sensors, while simultaneously reducing the amount of data to be processed. This work is a first example in feature extraction from tin-oxide sensors that both reduces the size of the data set and simultaneously improves the discrimination performance of the array. This work is aimed toward the design of remote sensor modules where bandwidth reduction and improved accuracy are both essential to system performance. The effectiveness of extracting rank from a 30-element array of tin-oxide sensors is presented. Results are extrapolated to other arrays of chemical sensors whose specificities and response characteristics overlap. Methods for processing data and extracting rank-related features from arrays of tin-oxide sensors are comparatively analyzed. Processing parameters studied include those related to temporal filtering and window-averaging, pre-scaling (to remove baseline), sample acquisition time, and the number of ranks used in rank-order filtering of the data during the transient and steady state response. Cluster analysis, including principal component analysis (PCA) and a novel method described herein, is used to determine which of these processing techniques are most effective. Artificial neural networks, specifically multi-layer perceptrons and radial basis function networks, are used to further investigate the ability to discriminate odors on the basis of the extracted features.The analysis is performed for an array of 30 tin-oxide sensors applied to detecting a sampling of breath alcohol mixtures (beer, wine, vodka) and common interferents (acetone, formaldehyde, isopropyl). Ammonia is included as a contrast substance. For the set of seven odorants studied, it is found that using rank-order filtering with 10 or more ranks improves odor recognition rate by a multi-layer perceptron neural network from 92% to 95%. If one odor (vodka) is removed from the study set, the recognition rate for the remaining odors improves from 95% (with no rank-order filtering) to 99%. Simultaneously, the dimensions of the data set for each odor are reduced from 30 sensors×18,000 time steps (12 bit samples) to N integer values, where N is the number of ranks used in the rank-order filtering.     

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。     

电子鼻在棉织物异味检测中的应用研究

基于电子鼻传感器检测技术,对棉织物中5种异味整体性质的人工智能评价进行探究,根据传感器检测数据曲线和数据主成分分析(PCA)分析,结果表明:各传感器对不同异味成分的响应性不同,PCA分析法处理数据能够有效区分布样中不同的异味组分,为纺织品异味的快速、有效、客观检测评定奠定了一定的基础.     

Mixed self-assembled lipopolymers with spacer lipids enhancing sensitivity of lipid-derivative QCMs for odor sensors

An odor sensing system using a quartz crystal microbalance (QCM) sensor array and pattern recognition technique has been for years a main research topic in our group. For the general field of artificial olfaction using acoustic-wave based sensors such as QCMs it is vital to search for novel sensing materials. Here we present recent results of our ongoing study on application of pegylated lipids as coatings for QCM odor-sensors. The method presented herein is based on self-assembling of lipids and lipid-derivatives on the QCM surfaces. The disulphide-terminated lipids and lipopolymers are co-chemisorbed onto gold electrodes of QCM sensors by simple immersion in ethanolic solutions. This creates porous supports onto which additional layers of lipopolymers are physisorbed. The method allows for fabrication of lipopolymeric QCM odor-sensors with enhanced sensitivity to odorants, capable of very good discrimination among odorant samples—according to the functional group of an odorant.     

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.