%0 Conference Proceedings
%T Research on Pattern Recognition Method for Honey Nectar Detection by Electronic Nose
%+ China Agricultural University (CAU)
%+ China National Institute of Standardization [Beijing] (CNIS)
%A Liu, Ningjing
%A Shi, Bolin
%A Zhao, Lei
%A Qing, Zhaoshen
%A Ji, Baopin
%A Zhou, Feng
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 8th International Conference on Computer and Computing Technologies in Agriculture (CCTA)
%C Beijing, China
%Y Daoliang Li
%Y Yingyi Chen
%3 Computer and Computing Technologies in Agriculture VIII
%V AICT-452
%P 393-403
%8 2014-09-16
%D 2014
%R 10.1007/978-3-319-19620-6_44
%K electronic nose
%K nectar detection
%K pattern recognition
%K support vector machine
%Z Computer Science [cs]Conference papers
%X Electronic nose (e-nose) utilizes the gas sensors array to absorb the volatile organic compounds (VOCs) of samples to classify them into different clusters, and it is noted by the sensitive of the sensors. However, limited by the methodologies of the pattern recognition, this kind of advantage had not been exploited fully. The research studied on different types of pattern recognition method, and selected the optimum method for the detection of samples with little nuance, exemplified by honey nectar detection for including rape honey, linden honey and acacia honey. It was found that support vector machine (SVM, non-linear prediction model) showed better performance than linear discriminate analysis (LDA, linear prediction model) for the classification of tiny different samples, especially when multi-group detection was involved. After the optimized method was selected, the key points of the SVM model were analyzed, and two key parameters were displayed, which were kernel parameter and penalty parameter. Three algorithms, including grid searching (GS), particle swarm optimization (PSO) and genetic algorithm (GA) were applied to find the appropriate parameter values. The results showed parameters optimized by genetic algorithm (kernel parameter and penalty parameter is 0.11 and 14.38 respectively) led to the optimal model, whose training accuracy was 98.78% and prediction accuracy was 97.5%. The results suggested that in the method of SVM with parameters selected by GA, e-nose could handle well the discrimination of similar samples like honey nectar detection.
%G English
%Z TC 5
%Z WG 5.1
%2 https://inria.hal.science/hal-01420255/document
%2 https://inria.hal.science/hal-01420255/file/978-3-319-19620-6_44_Chapter.pdf
%L hal-01420255
%U https://inria.hal.science/hal-01420255
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-CCTA
%~ IFIP-WG5-1
%~ IFIP-AICT-452