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从加那利海枣棕榈挥发物中鉴定红棕象甲诱捕系统的信息素增效剂。

Identification of pheromone synergists for Rhynchophorus ferrugineus trapping systems from Phoenix canariensis palm volatiles.

作者信息

Vacas Sandra, Abad-Payá María, Primo Jaime, Navarro-Llopis Vicente

机构信息

Centro de Ecología Quı́mica Agrícola, Instituto Agroforestal del Mediterráneo, Universitat Politècnica de València, edificio 6C, 5a planta, Cmno. de Vera s/n, 46022 Valencia, Spain.

出版信息

J Agric Food Chem. 2014 Jul 2;62(26):6053-64. doi: 10.1021/jf502663y. Epub 2014 Jun 23.

Abstract

Trapping systems for the red palm weevil, Rhynchophorus ferrugineus Olivier, rely on the use of natural plant odor sources to boost the attractiveness of the aggregation pheromone. The identification of the key odorants involved in attraction is essential in the development of a synthetic pheromone synergist to replace the nonstandardized use of plant material in traps. Canary Islands date palms (Phoenix canariensis) have become preferred hosts for R. ferrugineus in Europe; thus, the volatile profile of different P. canariensis plant materials, including healthy and infested tissues, is investigated in the present work by means of solid phase microextraction (SPME-GC-MS), aimed to identify pheromone synergists. The electroantennography (EAG) response of the compounds identified was recorded, as well as the preliminary field response of several EAG-active compounds. The so-called "palm esters" (ethyl acetate, ethyl propionate, ethyl butyrate, and propyl butyrate) elicit the strongest EAG responses but performed poorly in the field. Mixtures of esters and alcohols give evidence of better performance, but release rates need further optimization.

摘要

红棕象甲(Rhynchophorus ferrugineus Olivier)诱捕系统依赖于使用天然植物气味源来增强聚集信息素的吸引力。确定参与吸引作用的关键气味物质对于开发合成信息素增效剂以取代诱捕器中未标准化使用的植物材料至关重要。加那利海枣(Phoenix canariensis)已成为欧洲红棕象甲的首选寄主;因此,本研究通过固相微萃取(SPME-GC-MS)对不同加那利海枣植物材料(包括健康组织和受侵染组织)的挥发性成分进行了研究,旨在鉴定信息素增效剂。记录了所鉴定化合物的触角电位(EAG)反应,以及几种具有EAG活性的化合物的初步田间反应。所谓的“棕榈酯”(乙酸乙酯、丙酸乙酯、丁酸乙酯和丁酸丙酯)引发最强的EAG反应,但在田间表现不佳。酯类和醇类的混合物显示出更好的效果,但释放速率需要进一步优化。

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