Sensor Array Optimization of Electronic Nose for Detection of Bacteria in Wound Infection
Sensor Array Optimization of Electronic Nose for Detection of Bacteria in Wound Infection
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用于检测伤口感染细菌的电子鼻传感器阵列优化
DOI：
10.1109/tie.2017.2694353
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发表时间：
2017-09-01
影响因子：
7.7
通讯作者：
Liu, Xiangmin
中科院分区：
文献类型：
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作者：
Sun, Hao;Tian, Fengchun;Liu, Xiangmin
为了识别伤口感染中的细菌，设计了一个由34个传感器组成的传感器阵列的电子鼻系统。共检测到8种样品，培养基、大肠杆菌、金黄色葡萄球菌、铜绿假单胞菌及其不同浓度的混合物。采用支持向量机作为分类器，无需传感器阵列优化，识别率达到86.54%。为了简化传感器阵列，提高对细菌样本的识别率，采用Wilks λ统计量（Wilks. statistics）、马氏距离、主成分分析（PCA）、线性判别分析（LDA）和遗传算法对传感器阵列进行优化。结果表明，除主成分分析法外，其它方法都能有效地实现传感器阵列的优化。采用Wilks A统计量和LDA对传感器阵列进行优化后，识别率最高，分别达到96.15%，优化后的传感器阵列中传感器数目分别为22个和20个。在10个传感器的限制下，用Wilks的A统计量和LDA优化后的识别率仍可达到95.19%。
In order to identify the bacteria in wound infection, an electronic nose system with a sensor array of 34 sensors was designed. Eight kinds of samples were detected, i.e., culture medium, Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa and their mixture with different concentration. Using support vector machine as the classifier and without sensor array optimization, the recognition rate is up to 86.54%. To simplify the sensor array and improve the recognition rate for bacteria samples, Wilks' lambda statistic (Wilks'.statistic), Mahalanobis distance, principal component analysis (PCA), linear discriminant analysis (LDA), and genetic algorithm are used to optimize the sensor array. It is shown that the sensor array optimization may be realized efficiently by these methods except PCA. After sensor array optimization by Wilks' A-statistic and LDA, both of their recognition rates are the highest and up to 96.15%, while the numbers of sensors in optimized sensor arrays are 22 and 20, respectively. Under the limitation of ten sensors, the recognition rate optimized by Wilks' A-statistic and LDA may still reach 95.19%.