Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement
Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement
复制标题
DOI：
10.1002/rcm.6215
复制
发表时间：
2012-05-15
影响因子：
2
通讯作者：
Espinoza, Edgard
中科院分区：
文献类型：
--
作者：
Lancaster, Cady;Espinoza, Edgard
理由：几种黄檀木材的国际贸易受到《濒危野生动植物种国际贸易公约》（CITES）的管制。为了补充这些物种的形态鉴定，开发了一种快速化学分析方法。方法：采用实时电离直接分析(DART) -飞行时间质谱（TOF）联用技术对黄檀和常见贸易品种进行分析。利用主成分分析和线性判别分析（LDA）对13种木材进行分类。这些统计数据簇为未知物种识别提供了可靠的锚点。结果：本研究对13个物种的20个或更多样品进行了分析，结果表明DART-TOFMS结果具有可重复性。最丰富的离子的统计分析给出了很好的分类，有助于识别未知的木材样品。结论：对所选的13种木材样品进行DART-TOFMS和LDA分析，并进行统计分类，可以正确分配未知木材样品。这种方法快速，在解剖鉴定困难但需要支持CITES执法时非常有用。2012年出版。这篇文章是美国政府的作品，在美国属于公有领域。
RATIONALE: International trade of several Dalbergia wood species is regulated by The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). In order to supplement morphological identification of these species, a rapid chemical method of analysis was developed.METHODS: Using Direct Analysis in Real Time (DART) ionization coupled with Time-of-Flight (TOF) Mass Spectrometry (MS), selected Dalbergia and common trade species were analyzed. Each of the 13 wood species was classified using principal component analysis and linear discriminant analysis (LDA). These statistical data clusters served as reliable anchors for species identification of unknowns.RESULTS: Analysis of 20 or more samples from the 13 species studied in this research indicates that the DART-TOFMS results are reproducible. Statistical analysis of the most abundant ions gave good classifications that were useful for identifying unknown wood samples.CONCLUSIONS: DART-TOFMS and LDA analysis of 13 species of selected timber samples and the statistical classification allowed for the correct assignment of unknown wood samples. This method is rapid and can be useful when anatomical identification is difficult but needed in order to support CITES enforcement. Published 2012. This article is a US Government work and is in the public domain in the USA.