基于电子鼻技术的湄潭春茶产地溯源研究

为实现不同产地湄潭春茶的快速、客观判别，基于电子鼻与多元统计分析定性判别不同产地湄潭春茶间的差异，并定量预测其产地。对电子鼻信号进行分析，发现其响应信号在传感器S7、S9、S6和S2的强度均较明显；方差分析发现湄潭春茶产地对传感器响应影响均显著；基于第80 s数据进行主成分分析(PCA)和典则判别分析(CDA)，发现其基本能区分不同产地的湄潭春茶，且数据点的分布均与各产地地理位置分布呈现一定特征规律性，但区分效果不够理想。为进一步提高对不同乡镇茶叶的区分效果，利用不同特征值(平均值、曲线面积、最大值、斜率、主成分分析优选参数、loading优选参数)进行判别分析，其中平均值、曲线面积、最大值判别结果优于80 s时的判别结果；斜率、主成分分析优选参数、loading判别结果比80 s时的判别结果差；以响应曲线的平均值进行判别时，可达到100%正确识别。多层神经网络分析(MLP)作为效果最佳、决定系数最高(R_c²=0.985 5,R_p²=0.994 1)的茶叶产地预测模型，可以实现对不同产地湄潭春茶的有...     

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...     

主成分分析与神经网络结合的黄山毛峰茶品质检测

研究了利用主成分分析与BP神经网络结合的方法对黄山毛峰茶进行品质检测。首先应用主成分分析法对反映茶叶香气信息的原始特征变量进行分析,提取出前5个主成分,再以这些主成分作为BP神经网络的输入,建立3层BP神经网络预测模型。试验结果表明,该模型相对于未经过主成分分析的BP神经网络模型,建模效率大大提高,判别准确率也由92.5%提高到97.5%。说明主成分分析与BP神经网络结合应用于黄山毛峰茶品质检测是有效的。     

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.     

基于传感器阵列多特征优化融合的茶叶品质检测研究

为提高电子鼻对不同品质茶叶的识别能力,分别提取电子鼻传感器信号的总体平均值、上升阶段斜率平均值和相对稳态平均值作为特征值,对电子鼻传感器阵列进行多特征数据融合优化.首先对原始数据进行归一化处理,统一值的量纲和数量级;通过因子载荷分析,去除各个象限内主成分投影较小和投影重叠的因子,对多特征向量矩阵进行优化;最后采用单因素方差分析,缩小不同品质茶叶组内间距,增大组间间距,更利于实现茶叶品质的区分.结果显示,主成分分析(PCA)可有效区分3种不同等级茶叶,因子载荷优化使各品质茶叶组内间距减小,单因素方差优化使一级与二级茶叶区分效果更明显;线性判别分析(LDA)效果要优于PCA分析,3个不同等级的茶叶可得到极为明显的区分.研究结果表明,用多特征优化融合可有效提取电子鼻对茶叶的响应信息,有利于对不同品质茶叶进行识别.     

茶叶光谱与叶绿素、茶氨酸、茶多酚含量关系分析

利用茶叶反射光谱数据分析茶叶中有机物组分的含量,能够快速、无损地进行茶叶品质鉴定。选择浙江省丽水市松阳县和绍兴市越城区的4个茶叶品种的新叶、成熟叶和老叶的样本,进行了光谱测量和3种有机物组分(叶绿素、茶氨酸和茶多酚)的含量检测。然后利用最佳指数法选出了估算这3种有机物的最佳波段,建立了估算3种有机物含量的多元回归线性模型。最后,比较了多元散射校正变换、标准归一化变换、结合Savitzky-Golay滤波的一阶导数变换这3种不同的光谱数据预处理方法的估算结果的差异,并分析了敏感波段的产生原因。研究结果表明:利用茶叶光谱估算叶绿素含量的效果最好,模拟值与实测值之间R~2大于0.9;估算茶氨酸的效果次之,模拟值与实测值之间R~2约为0.7;估算茶多酚的效果最差,模拟值与实测值之间R~2仅为0.65左右。     

苹果近红外光谱的特征提取和分类研究

采用近红外光谱分析法对不同种类的苹果样品进行分类,提出一种基于非相关判别转换的苹果近红外光谱定性分析新方法。实验分别采用主成分分析、Fisher判别分析和非相关判别转换三种方法对苹果光谱数据进行特征提取,并使用K-近邻分类算法建立三种苹果分类识别模型,最后使用"留一"交叉验证法进行模型检验。结果表明,使用非相关判别转换方法建立的模型正确识别率优于使用主成分分析和Fisher判别分析建立的模型。     

主成分—费歇尔判别模型在煤与瓦斯突出等级预测中的应用

针对现有煤与瓦斯突出预测方法存在计算过程较复杂、预测主观性强、预测精度较低等问题,构建了主成分-费歇尔判别模型,并将其应用于某煤矿的煤与瓦斯突出等级预测。从瓦斯因素、煤体结构及地质构造方面分析得出了影响该矿煤与瓦斯突出的因素包括瓦斯压力、瓦斯含量及瓦斯放散初速度等指标。以影响该矿煤与瓦斯突出的23组实测数据为基础,首先利用主成分分析模型对影响该矿的煤与瓦斯突出因素进行降维,提取与指标相关度较高的5个主成分,然后将5个主成分输入费歇尔判别模型,并根据判别函数对样本进行煤与瓦斯突出等级预测。应用结果表明:主成分-费歇尔判别模型具有较高的可信性,能对煤与瓦斯突出等级进行准确预测,训练样本的正确率为100%,待测样本的预测结果也与该矿煤与瓦斯突出的实际情况相符,误判率为0,为准确预测煤与瓦斯突出提供了一种新方法。     

百草油鉴别分类的电子鼻实现方法研究

采用便携式电子鼻PEN3对4组不同生产批次的中成药(百草油)进行检测,并对采样得到的数据进行分析.结合主成分分析(PCA)和线性判别分析(LDA)算法的特点,采用了PCA+LDA分析方法.该方法首先通过PCA分析来压缩特征数据的维数,减少数据计算量,进而优化特征向量,继而采用LDA分析实现对不同批次百草油产品的鉴别分类.结果表明:对4组样本的最终判别结果达到了87.5%的正确判别率,误判的待测样本只发生在p0705和p0801之间.     

基于方波伏安型电子舌的鸡蛋蛋清检测方法研究

鸡蛋作为一种普遍的营养品,其储存期和销售等级的快速准确检测对消费者和生产者均至关重要。利用方波伏安型电子舌对鸡蛋蛋清溶液进行检测,并利用哈夫单位的测定建立鸡蛋不同储存期和不同储存期下的等级与鸡蛋哈夫单位的对应关系。提取第一峰电流、第二峰电流、第二极小值电流作为特征值。分别利用主成分分析（PCA）和判别函数分析（DFA）对数据信息进行分析,DFA比PCA能较好地区分鸡蛋的储存期和等级,电子舌能够检测出鸡蛋储存期和销售等级。     

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.     

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.     

An improved LDA approach

Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness. The first weakness is that not all the discrimination vectors that are obtained are useful in pattern classification. Second, it remains computationally expensive to make the discrimination vectors completely satisfy statistical uncorrelation. The third weakness is that it is necessary to select the appropriate principal components. In this paper, we propose to improve discrimination technique in these three areas and to that end present an improved LDA (ILDA) approach which synthesizes these improvements. Experimental results on different image databases demonstrate that our improvements on LDA are efficient, and that ILDA outperforms other state-of-the-art linear discrimination methods.     

自组织特征映射神经网络——用于茶叶分类

介绍了自组织特征映射神经网络的结构和算法 ,将其用于中国茶叶的分类 ,并和传统方法进行了比较。认为组织特征映射神经网络优于传统方法。     

一种基于电子舌技术的绿茶分类方法

提出了一种基于对铜电极的循环伏安信号的主成分分析来进行绿茶分类的方法.绿茶中含有蛋白质,脂类,碳水化合物及氨基酸等.实验结果显示在强碱性溶液中铜电极对这些物质具有丰富的响应.我们采用主成分分析法对五种不同的绿茶在铜电极上的循环伏安信号进行分析,结果显示在第一和第二主成分的得分图上,五种不同绿茶可以清楚地区分开.同时也进一步研究了一种带窗的时间切割法对原始的循环伏安信号的数据压缩.     

Identification of green and dry vegetation components with a cross-correlogram spectral matching technique

A cross-correlogram spectral matching technique is proposed for discrimination of green and dry vegetation. By examining the reflectance spectra of fresh green leaves, dry leaves, dry bark, and dry wood, it was found that dry leaves had a diagnostic absorption feature near 1730 nm and the chlorophyll absorption feature near 680 nm was the main diagnostic feature in green leaves. By calculating the cross correlation between a reference spectrum and test spectra at different match positions in these two spectral regions, it was possible to separate green leaf, dry leaf, and dry bark components with 100 per cent accuracy.     

基于电子鼻系统的番茄苗不同种类损伤的区分效果研究

通过对早疫病病害番茄苗、灰霉病病害番茄苗、机械损伤番茄苗和对照番茄苗的电子鼻响应信号的对比,可以看出不同处理的番茄苗样本电子鼻的响应信号是不同的,表明用电子鼻响应信号对番茄苗不同种类损伤进行预测是可行的.从PCA结果来看,早疫病病害的番茄苗和灰霉病病害的番茄苗能很好区分开,机械损伤的番茄苗和正常处理的番茄苗产生了重叠现...     

改进的遗传BP神经网络数据挖掘算法及应用

介绍了数据挖掘的定义和常用方法,研究了基于遗传BP神经网络的数据挖掘算法,并对其交叉算子进行了改进,提高算法训练速度。实验结果表明,将该方法应用于油气识别中,效果良好,具有一定的实际应用价值。