Department of Biology, Chemistry and Forensic Science, University of Wolverhampton.
Department of Biology, Chemistry and Forensic Science, University of Wolverhampton; Department of Anthropology & Behavior, Ecology and Evolution Research Centre, Durham University;
J Vis Exp. 2021 Feb 13(168). doi: 10.3791/60902.
We have developed an effective methodology for sampling and analysis of odor signals, by using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry, to understand how they may be used in animal communication. This technique allows the semi-quantitative analysis of the volatile components of odor secretions by enabling the separation and tentative identification of the components in the sample, followed by the analysis of peak area ratios to look for trends that could signify compounds that may be involved in signaling. The key strengths of this current approach are the range of sample types that can be analyzed; the lack of need for any complex sample preparation or extractions; the ability to separate and analyze the components of a mixture; the identification of the components detected; and the capability to provide semi-quantitative and potentially quantitative information on the components detected. The main limitation to the methodology relates to the samples themselves. Since the components of specific interest are volatile, and these could easily be lost, or their concentrations altered, it is important that the samples are stored and transported appropriately after their collection. This also means that sample storage and transport conditions are relatively costly. This method can be applied to a variety of samples (including urine, feces, hair and scent-gland odor secretions). These odors consist of complex mixtures, occurring in a range of matrices, and thus necessitate the use of techniques to separate the individual components and extract the compounds of biological interest.
我们已经开发出一种有效的方法来采样和分析气味信号，使用顶空固相微萃取结合气相色谱-质谱联用技术，以了解它们如何在动物通讯中使用。该技术通过分离和初步鉴定样品中的成分，然后分析峰面积比，寻找可能参与信号传递的化合物的趋势，从而实现气味分泌物挥发性成分的半定量分析。这种方法的主要优点是可以分析的样本类型范围广泛；不需要任何复杂的样品制备或提取；能够分离和分析混合物的成分；可以识别检测到的成分；以及能够提供检测到的成分的半定量和潜在定量信息。该方法的主要局限性与样品本身有关。由于特定感兴趣的成分是挥发性的，并且这些成分很容易丢失或浓度发生变化，因此在收集后适当地储存和运输样品非常重要。这也意味着样品储存和运输条件相对昂贵。这种方法可以应用于各种样本（包括尿液、粪便、头发和气味腺分泌物）。这些气味由复杂的混合物组成，存在于多种基质中，因此需要使用分离单个成分和提取生物感兴趣化合物的技术。
