Bacterial Infection Potato Tuber Soft Rot Disease Detection Based on Electronic Nose

被引:19
|
作者
Chang, Zhiyong [2 ,3 ]
Lv, Jianhua [1 ,3 ]
Qi, Hongyan [1 ,3 ]
Ma, Yunhai [1 ,3 ]
Chen, Donghui [1 ,3 ]
Xie, Jun [1 ,4 ]
Sun, Youhong [2 ]
机构
[1] Minist Educ, Key Lab Bion Engn, Changchun 130022, Jilin, Peoples R China
[2] Jilin Univ, Minist Land & Resources, Key Lab Drilling & Exploitat Technol Complex Cond, Changchun 130026, Jilin, Peoples R China
[3] Jilin Univ, Coll Biol & Agr Engn, Changchun 130022, Jilin, Peoples R China
[4] Air Force Aerial Univ, Aerosp Intelligence Dept, Changchun 130021, Jilin, Peoples R China
来源
OPEN LIFE SCIENCES | 2017年 / 12卷 / 01期
基金
中国博士后科学基金;
关键词
electronic nose; potato; soft rot disease; bacterial infectio; ERWINIA-CAROTOVORA; PATHOGENS; PECTOBACTERIUM; IDENTIFICATION; VOLATILES;
D O I
10.1515/biol-2017-0044
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Soft rot is a severe bacterial disease of potatoes, and soft rot infection can cause significant economic losses during the storage period of potatoes. In this study, potato soft rot was selected as the research object, and a type of potato tuber soft rot disease early detection method based on the electronic nose technology was proposed. An optimized bionic electronic nose gas chamber and a scientific and reasonable sampling device were designed to detect a change in volatile substances of the infected soft rot disease of potato tuber. The infection of soft rot disease in potato tuber samples was detected and identified by using the RBF NN algorithm and SVM algorithm. The results revealed that the proposed bionic electronic nose system can be utilized for early detection of potato tuber soft rot disease. Through comparison and analysis, the recognition rate using the SVM algorithm reached up to 89.7%, and the results were superior to the RBF NN algorithm.
引用
收藏
页码:379 / 385
页数:7
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