中文摘要：

为了提高电子鼻检测玉米霉变程度的正确率,该文探究了电子鼻信号不同特征组合的表征对霉变玉米鉴别结果的影响。首先,运用电子鼻对霉变玉米的5组样本训练集与测试集进行测试,获得测试信号。其次,分别提取测试信号的积分值（integral value,INV）、平均微分值（average differential value,ADV）、相对稳态平均值（relative steady-state average value,RSAV）作为特征值,5组训练集与测试集均分别采用3种单一的特征值或其组合特征值来表征。然后,运用Fisher判别分析（fisher discriminant analysis,FDA）分别对5组训练集进行判别分析,并用对应的测试集进行检验。FDA分析结果指出,电子鼻测试信息分别在单一特征和2个特征组合表征下,不同霉变程度玉米是不能有效分开的,但在2个特征组合表征下的鉴别正确率比单一特征有所提高;当用3个特征组合来表征测试信息时,FDA鉴别能力得到提高,鉴别正确率在96%以上。另外,借助于WilksΛ统计量考察了电子鼻中每个传感器测试信号表征的差异性,对3个特征组合的表征情况进行了表征变量筛选。FDA分析结果显示,筛选前后的鉴别结果非常相近,最低鉴别正确率均在96%以上,这说明不同传感器需要不同的特征表征,以体现其差异性,由此也减少了计算的复杂性。研究结果表明,用多特征融合模式可更有效地表征电子鼻对霉变玉米的响应信息,有利于提高霉变玉米的鉴别正确率。同时,该研究成果也不失一般性,为电子鼻信号表征提供了一种新思路。

英文摘要：

In this paper, in order to improve correct rate of discrimination result of different moldy degrees of maize using the electronic nose（E-nose）, the influence of different feature combination representation types of E-nose signals on the discrimination result of moldy maize was studied in depth. In our investigation, the maize with 5 kinds of different moldy degrees was identification objects, and there were a total of 40 samples for each kind of moldy maize. Firstly, 30 samples were randomly selected from each kind of moldy maize for forming a training set（totaling 150 samples）, and the rest 10 samples were used to form a corresponding test set（totaling 50 samples）. To verify the robustness of this research finding, 5 groups of training sets and their corresponding test sets were randomly generated and respectively tested by the E-nose, and the test signals of the 5 groups of training sets and test sets were obtained; meanwhile their discrimination results were also compared with each other. Secondly, integral value（INV）, average differential value（ADV） and relative steady-state average value（RSAV） of E-nose signals were extracted as 3 kinds of feature values; the five groups of training sets and corresponding test sets were respectively represented by each feature value, and also by their combination feature values. Then, the 5 groups of training sets were respectively analyzed by Fisher discriminant analysis（FDA） and 5 FDA analysis models were established, and then their corresponding test sets were used to verify the 5 FDA models. FDA results showed that： when the E-nose signals were represented by single feature or 2 features＇ combination, different moldy degrees of maize could not be discriminated effectively, but the correct rate of discrimination results based on 2 features＇ combination was better than that of the single feature, and the highest correct rate of single feature was 86%, while the highest correct rate of 2 features＇ combination was 96%; the identification a