中文摘要：

针对交叉敏感性传感器阵列电子鼻图谱含样品信息全、噪声杂、信号冗余等特点,该文根据盲信号处理原理,利用独立成分分析提取油菜蜜、椴树蜜、洋槐蜜电子鼻交叉信号中不同性质分离的独立信号。将各蜜源样本电子鼻信号矩阵延响应时间轴方向展开,统一了各样品间独立成分分解顺序的一致性,并保持了混合信号矩阵中蜂蜜样本的排序。根据不同独立成分数分离信号所带来的蜜源分类效果,8个独立成分为最优成分数。结合遗传算法筛选出各独立成分中代表蜜源样本间差异而无重复信息的特征响应点20个,来富集蜂蜜样本间的整体电子鼻差异信息,并发现大部分集中于信号的吸附阶段,少量出现于解析附阶段。油菜蜜、椴树蜜、洋槐蜜蜜源判别模型采用支持向量机算法建立,通过比较原始信号、电导变化最大值、主成分分析(principal component analysis,PCA)、独立成分分析(independent component analysis,ICA)、独立成分分析结合遗传算法(genetic algorithm,GA)这5种信号处理方式,发现ICA结合GA的特征信号挖掘效果最优,预测集判别率(95.0%)最高,其中油菜蜜、椴树蜜、洋槐蜜的预测集判别力分别为24/25、16/17、36/38;且与训练集判别率(96.3%)最接近,说明模型稳定性高、泛化能力强。结果表明该方法可以准确提取电子鼻信号中能代表蜂蜜蜜源差异信息的特征信号,并在保证蜜源分类效果的前提下,大幅减小电子鼻信号的数据量。该研究为去除电子鼻交叉信号中的冗余成分,并挖掘掩藏其下的差异信息提供了很好的指导意义,同时也拓宽了ICA的应用范围。

英文摘要：

Focusing on the characteristics of electronic nose (e-nose), including fully, noisy and redundant, Independent Components Analysis (ICA) is proposed to extract differential signals of e-nose in the honey nectar detection (rape honey, linden honey and acacia honey). However, in order to match the principle of ICA (each vector standing for one observing signal) and overcome the shortage of ICA (the randomness of independent components), some transforms of ICA are needed to be carried out. Referring to the methods applied in brain image analysis, the research extends the signals of different samples in time direction. In this case, the order of independent components was assured. After the comparison of different number of ICA, which is evaluated by the accuracy of pattern recognition with the support vector machine model, the optimum number is confirmed as 8. Although the quantity data has been narrowed down dramatically, still 960 points are included, 120 points for each components. To the further simplification, Genetic Algorithm (GA) is used to select the characteristic points to remove the redundant information. 20 points, many of which are located in the absorption phase are selected. The results of GA selection show that although most of special points are located in the absorption part, there are still a part of points which are emerged in desorption part. In this case, only selecting the values in the peak is defective, which may lead to the ignorance of some special information which is included in other parts, like the desorption part. To testify the effective of the method, it is compared with other common processing methods, including raw data without being processed, maximum conductance of the original signals, the Principle Components Analysis (PCA), and data only processed by ICA without GA. The data are processed by these five different processing methods. After that, the Support Vector Machine (SVM) is employed as the pattern recognition method. Compared with other models, SVM model is built based