• Title/Summary/Keyword: trail pheromone

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Analysis of the composition of trail pheromone secreted from live Camponotus japonicus by HS-SPME GC/MS (HeadSpace-Solid Phase MicroExtraction Gas Chromatography/Mass Spectrometry) (HS-SPME GC/MS법을 이용한 일본왕개미의 trail pheromone 성분 분석)

  • Park, Kyung-Eun;Lee, Dong-Kyu;Kwon, Sung Won;Lee, Mi-Young
    • Analytical Science and Technology
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    • v.25 no.5
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    • pp.292-299
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    • 2012
  • GC/MS has been utilized for many applications due to great resolution and reproducibility, which made it possible to build up the database of mass spectrum, while HS-SPME has the advantage of solventfree extraction of volatile compounds. The combination of these two methods, HS-SPME GC/MS, enabled many scientific applications with various possibilities. In this study, the analysis of trail pheromone excreted from live Camponotus japonicus with the feature of solvent-free extraction was carried out and the optimization for this analysis was performed. The major compounds detected were n-decane, n-undecane, and n-tridecane. Optimization for the best detection of these hydrocarbons was processed in the point of SPME parameter (selection of fiber, extraction temperature, extraction time, etc.). The advantage of the analysis of live sample is to analyze phenomenon right after it is excreted by ants. But the experimental process has restriction of extraction temperature and time because of the analysis of live ants. Establishing the process of HS-SPME GC/MS applied to live samples shown in this study can be a breakthrough for the ecofriendly and ethical research of live things.

Ant Colony Optimization for Feature Selection in Pattern Recognition (패턴 인식에서 특징 선택을 위한 개미 군락 최적화)

  • Oh, Il-Seok;Lee, Jin-Seon
    • The Journal of the Korea Contents Association
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    • v.10 no.5
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    • pp.1-9
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    • 2010
  • This paper propose a novel scheme called selective evaluation to improve convergence of ACO (ant colony optimization) for feature selection. The scheme cutdown the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. With the aim of checking applicability of algorithms according to problem size, we analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.