Neural network based electronic nose for apple ripeness determination

被引：37
|
作者：
Hines, EL [1 ]
Llobet, E
Gardner, JW
机构：
[1] Univ Warwick, Sch Engn, Div Elect & Elect Engn, Coventry CV4 7AL, W Midlands, England
[2] Univ Rovira & Virgili, Dept Elect Engn, Tarragona 43006, Spain
来源：
ELECTRONICS LETTERS | 1999年 / 35卷 / 10期
关键词：
D O I：
10.1049/el:19990547
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
It is possible to non-destructively determine apple ripeness using a simple electronic nose. The instrument employs tin oxide resistive gas sensors and neural networks (fuzzy ARTMAP, LVQ and MLP) to classify the samples into three states df ripeness with 100% accuracy. Fuzzy ARTMAP was found to be the best classifier in the presence of simulated Gaussian noise.
引用
收藏
页码：821 / 823
页数：3
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