The electronic nose system is widely used to detect tea aroma, and the sensor array plays a fundamental role in detecting results. In this paper, we propose a sensor array optimization method based on correlation coefficient and cluster analysis. First, redundant sensors are assessed according to the correlation coefficient calculated between two sensors. The discriminating performance value (DPV) of the sensor is calculated, which reflects both the inter- and intra-class dispersion of tea varieties discriminated by the sensors. Thus, the redundant sensor with a small DPV is removed. Second, based on the cluster analysis (CA), the independence between sensors is analyzed. Beginning with the sensor with the highest DPV, the optimized sensor array can be acquired by iteratively selecting a candidate sensor that has the largest clustering coefficient with previous selected sensors. According to the above methods, sensor arrays for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed. Validated experiments are carried out by detecting 12 kinds of green tea by LG, 6 kinds of fried green tea by LF and 6 kinds of roasted green tea by LB. The classification accuracy using methods of Linear Discriminant Analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach to 100%. When used to discriminate between various grades of West Lake Longjingtea, LF shows better performance than that of the German PEN2 electronic nose.

电子鼻系统广泛应用于茶叶香气检测，传感器阵列对检测结果起着基础性作用。在本文中，我们提出了一种基于相关系数和聚类分析的传感器阵列优化方法。首先，根据两个传感器之间计算的相关系数来评估冗余传感器。计算传感器的判别性能值（DPV），它反映了传感器判别的茶叶品种的类间和类内分散程度。因此，去除了具有小 DPV 的冗余传感器。其次，基于聚类分析（CA），分析传感器之间的独立性。从具有最高 DPV 的传感器开始，可以通过迭代选择与先前选择的传感器具有最大聚类系数的候选传感器来获得优化的传感器阵列。根据上述方法，构建了绿茶（LG）、炒绿茶（LF）和烘焙绿茶（LB）的传感器阵列。通过LG检测12种绿茶、LF检测6种炒绿茶、LB检测6种炒绿茶进行验证实验。采用基于平均值的线性判别分析（LDA-ave）结合最近邻分类器（NNC）的方法，分类准确率几乎可以达到100%。当用于区分西湖龙井茶的各个等级时，LF表现出比德国PEN2电子鼻更好的性能。