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.     

近红外光谱法测定黄酒中氨基酸态氮和酒精度的研究

黄酒中氨基酸态氮和酒精度的常规测定方法步骤繁琐,时效性差.在研究黄酒的近红外光谱和化学计量学的基础上,采用偏最小二乘法建立模型,并以该模型对未知黄酒样品的氨基酸态氮和酒精度含量进行预测,验证模型可靠性.氨基酸态氮建模结果:决定系数为95.19%,均方差为0.041;酒精度建模结果:决定系数为96.82%,均方差为0.26.氨基酸态氮外部检验的预测均方差为0.063;酒精度外部检验的预测均方差为0.20.结果表明该方法应用于黄酒品质的检测,操作简便、快速、准确.     

电子鼻在危险爆炸物检测中的应用研究

利用由18个纳米氧化锌厚膜气敏传感器组成的阵列对硝铵、矿山炸药、苦味酸、2,4二硝基甲苯(DNT)这4种典型爆炸物样品进行了测量.采用动、静态相结合的采样方法考察了传感器阵列的检测能力,在动态实验中通过提取不同的特征值并利用主元分析(PCA)和聚类分析(CA)方法对数据进行了分析和识别.静态实验结果表明传感器阵列在不同浓度上对4种典型爆炸物均有不同程度的响应,该方法能检测到硝铵、矿山炸药、苦味酸的浓度低至3.34 μg/L,DNT为83.3 μg/L;动态实验结果表明提取极值为特征值对阵列进行PCA、CA分析,可使4种典型爆炸物在毫克级上能完全区分.以上结果说明电子鼻技术在危险爆炸物检测中是一种很有发展前途的实用技术.     

基于快速气相色谱和气体传感器阵列的冷冻猪肉储存期检测方法研究

猪肉在冷冻过程中质量呈现下降的趋势.但在冷冻猪肉现场检测过程中,需要先解冻后检测,这就很难评估在解冻过程中由于环境因素变化对样品产生的不利影响.本文采用快速气相色谱方法分离出冷冻猪肉样品的特异性挥发气体,以克服冷冻猪肉挥发气体浓度低的问题.采用气体传感器阵列检测气相色谱柱富集的气体,以非线性信号分析模型解析气体传感器阵列输出信号并选择特征值.将特征值与检测时间经过非线性拟合得到冷冻猪肉储存期检测模型,验证实验结果证明所构建的模型具有较高的检测精度.本方法有望在冷冻猪肉保存期分析中得到应用.     

霉变小麦的电子鼻区分及其传感器阵列选择优化

采用电子鼻区分不同霉变程度的扬麦23号样品,连续检测不同霉变程度小麦样品,并记录检测数据。将检测数据耦合到双稳态随机共振系统,调解系统参数诱发产生共振,依据系统输出信噪比特征值建立小麦霉变程度预测模型。为了提高电子鼻对霉变小麦样品区分效果,进行了电子鼻传感器负荷加载分析,对电子鼻传感器阵列进行了优化研究,结果表明传感器阵列优化可有效提高电子鼻检测小麦霉变程度的准确度。采用华麦6号样品构建验证实验,结果证明所建立的方法具有较好的应用意义,并具有普遍意义上的适用性。     

基于电子鼻的香蕉储存时间鉴别方法研究

研究了一种基于电子鼻系统的香蕉储存时间鉴别方法。实验检测了不同储存时间的香蕉样品,主成分分析方法可以较好地区分不同储存时间的香蕉样品,同时检验了样品的微生物指标以探讨电子鼻响应与微生物指标之间的关系。随机共振信噪比谱不但可以区分香蕉样品,同时基于信噪比特征值建立的香蕉储存时间鉴别模型具有较高的预测准确率。该方法具有较好的实际应用价值。     

基于电子鼻的低温贮藏草鱼品质预测方法研究

提出了一种基于电子鼻的低温贮藏(277 K)草鱼品质预测方法。实验检测了草鱼样品顶空挥发气体,实验结果表明电子鼻传感器阵列对草鱼样品的响应值随着贮藏时间的增加而增加。同时实验检测了草鱼样品的菌落总数指标,电子鼻响应与微生物指标之间具有良好的对应关系。信噪比谱方法成功区分不同贮藏时间的草鱼样品。我们依据信噪比特征值建立了样品品质预测模型,该模型可以准确的预测低温贮藏草鱼样品品质,具有较好的实用价值。     

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.     

瓷砖色差在线分类系统的研究

提出了一种瓷砖色差分类的方法,设计了基于彩色线阵CCD传感器的在线检测系统.将CCD采集的瓷砖图像进行色彩模型转换以后,经过图像处理和图像分析,计算样品色度直方图统计值并作为分类的特征值,最后采用最小距离分类原理进行样品分类决策.实验结果表明:该方法可以取得良好的分类效果.     

磁巴克豪森噪声表征铁磁性材料应力的最优特征值研究

磁巴克豪森噪声是铁磁性材料动态磁化时磁畴壁的不连续运动过程中产生的一种信号,对材料中的应力比较敏感,在应力检测方面具有广阔的应用前景。在磁巴克豪森噪声应力检测的研究中,常用的磁巴克豪森噪声特征值有均方根、均 值、振铃数、包络峰值、包络面积、峰值时间等,采用的特征值不同,其随应力变化规律也有所不同,即不同特征值检测应力的 敏感度及精度不同。因此如何选取特征值对应力检测具有重要意义。通过拉伸实验得到了拉伸应力与磁巴克豪森信号之间的关系,计算了拉伸应力与各特征值的关系曲线,分析了各特征值表征应力的敏感程度与误差大小,得到了磁巴克豪森噪声表征应力的最优特征值。     

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.     

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.     

LDA模型在红酒数据关系挖掘中的应用研究

为了调查食品尤其是包含复合香气的食品（如葡萄酒和酒精饮料等）中的气味活性化合物的构成机理，提出了一种将LDA模型应用于红酒气味与化学分子关系挖掘的方法。该方法在红酒风味数据集上，将红酒看作文档，气味和化学分子看作词语，通过LDA主题模型挖掘隐含的红酒特征；根据红酒与化学分子在红酒中的分布进行聚类，并结合Apriori算法进行关联分析，最终找出气味与化学分子之间的关系，为设计一个能够通过测试化学分子识别食品气味的电子鼻打下基础。实验数据由法国南特大学Oniris气味实验室提供，实验结果部分地证实了将LDA模型应用于红酒气味与化学分子关系挖掘的可行性。     

Fuzzy neural computing of coffee and tainted-water data from an electronic nose

In this paper we compare the ability of a fuzzy neural network and a common back-propagation network to classify odour samples that were obtained by an electronic nose employing semiconducting oxide conductometric gas sensors. Two different sample sets have been analysed: first, the aroma of three blends of commercial coffee, and secondly, the headspace of six different tainted-water samples. The two experimental data sets provide an excellent opportunity to test the ability of a fuzzy neural network due to the high level of sensor variability often experienced with this type of sensor. Results are presented on the application of three-layer fuzzy neural networks to electronic nose data. They demonstrate a considerable improvement in performance compared to a common back-propagation network.     

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.     

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.     

A recurrent Elman network in conjunction with an electronic nose for fast prediction of optimum fermentation time of black tea

Tea industries enjoy a significant position in the socio-economic ladder for any demographics, especially in India who is the largest producer as well as consumer of the agro-product. While tea ranks only next to water in the pedigree of globally consumed beverages, the imperative fermentation stage in the processing of tea leaves is conventionally monitored through olfactory perception of tea tasters. Recent advances in the field of machine olfaction have witnessed the advent of electronic nose prototypes, which provide a scientific validation to the organoleptic estimations disseminated by the tasters. However, fermentation is a continuous process requiring constant monitoring whose successful completion relies heavily on identification of distinct aroma peaks emanated at optimum instants. Since the fermentation process is integral to the final quality, it is deemed beneficial if the optimum fermentation period can be predicted at an earlier stage. Such preemptive information can mitigate constant monitoring requirements and momentary concentration lapses. Recognizing the time series nature of the data generated during the fermentation process with an electronic nose prototype, we have implemented a recurrent Elman network to predict the optimum fermentation period for different black tea samples. The results showed that the prescribed network could predict the optimum period with confidence at the halfway of the process. The minimal error between the predicted and the actual fermentation period at the halfway point suggests that the proposed model can well be integrated with an electronic nose dedicated for monitoring the fermentation process.

     

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.