de Oliveira Lidia S, Rodrigues Frederico de M, de Oliveira Fabio S, Mesquita Paulo R R, Leal Danielle C, Alcântara Adriano C, Souza Barbara M, Franke Carlos R, Pereira Pedro A de P, de Andrade Jailson B
Instituto de Química, Universidade Federal da Bahia, Campus Universitário de Ondina, 40170-290 Salvador, BA, Brazil.
J Chromatogr B Analyt Technol Biomed Life Sci. 2008 Nov 15;875(2):392-8. doi: 10.1016/j.jchromb.2008.09.028. Epub 2008 Oct 2.
A new analytical methodology using HS-SPME/GC-MS was optimized in order to attain maximum sensitivity, using multivariate strategies. The proposed method was employed to evaluate the VOC profile exhaled from canine hair samples collected from 8 healthy dogs and from 16 dogs infected by Leishmania infantum. 274 VOCs were detected, which could be identified as aldehydes, ketones and hydrocarbons. After application of the Soft Independent Modeling of Class Analogy (SIMCA) and Principal Component Analysis (PCA) healthy and infected dogs, with similar VOCs profiles, could be separately grouped, based on compounds such as 2-hexanone, benzaldehyde, and 2,4-nonadienal. The proposed method is non-invasive, painless, readily accepted by dog owners and could be useful to identify several biomarkers with applications in the diagnosis of diseases.
为了实现最大灵敏度，采用多变量策略优化了一种使用顶空固相微萃取/气相色谱-质谱联用（HS-SPME/GC-MS）的新分析方法。所提出的方法用于评估从8只健康犬和16只感染婴儿利什曼原虫的犬采集的毛发样本呼出的挥发性有机化合物（VOC）谱。检测到274种VOC，可鉴定为醛、酮和烃类。应用类软独立建模（SIMCA）和主成分分析（PCA）后，基于2-己酮、苯甲醛和2,4-壬二烯醛等化合物，具有相似VOC谱的健康犬和感染犬可以被分别分组。所提出的方法是非侵入性的、无痛的，易于被犬主接受，并且可用于识别多种生物标志物，在疾病诊断中具有应用价值。
