Title :
Malicious odor item identification using an electronic nose based on support vector machine classification
Author :
Hasan, Najam Ul ; Ejaz, Naveed ; Ejaz, Waleed ; Kim, Hyung Seok
Author_Institution :
Dept. of Inf. & Commun. Eng., Sejong Univ., Seoul, South Korea
Abstract :
The aim of this study is to develop an electronic nose for identifying the spoiled meat stocked inside a refrigerator. Electronic nose analyses the samples of beef and fish and applies a classifier named support vector machine (SVM) to identify the meat creating malodor. To evaluate, the experiment is performed for a week. The results indicate that SVM classifier exhibits good generalization performance and enable accuracy rate of almost 94.5 % for both beef and fish. This means that SVM is an effective pattern classification technique for spoiled meat identification using electronic nose.
Keywords :
computerised instrumentation; electronic noses; food products; pattern classification; production engineering computing; support vector machines; SVM; beef sample; electronic nose analyses; fish sample; malicious odor item identification; pattern classification technique; refrigerator; spoiled meat identification; support vector machine classification; Accuracy; Arrays; Electronic noses; Marine animals; Refrigerators; Sensors; Support vector machines; Electronic nose; odor analysis; support vector machine;
Conference_Titel :
Consumer Electronics (GCCE), 2012 IEEE 1st Global Conference on
Conference_Location :
Tokyo
Print_ISBN :
978-1-4673-1500-5
DOI :
10.1109/GCCE.2012.6379638