Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia; ARC Industrial Transformation Research Hub for Processing Advanced Lignocellulosics (PALS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia.
Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia.
J Chromatogr A. 2021 Mar 15;1640:461896. doi: 10.1016/j.chroma.2021.461896. Epub 2021 Jan 22.
Gas chromatography electron impact ionization mass spectrometry (GC-EI-MS) has been, and remains, the most widely applied analytical technique for metabolomic studies of essential oils. GC-EI-MS analysis of complex samples, such as essential oils, creates a large volume of data. Creating predictive models for such samples and observing patterns within complex data sets presents a significant challenge and requires application of robust data handling and data analysis methods. Accordingly, a wide variety of software and algorithms has been investigated and developed for this purpose over the years. This review provides an overview and summary of that research effort, and attempts to classify and compare different data handling and data analysis procedures that have been reported to-date in the metabolomic study of essential oils using GC-EI-MS.
气相色谱电子轰击电离质谱（GC-EI-MS）一直是，并且仍然是用于对精油进行代谢组学研究的最广泛应用的分析技术。对复杂样品（如精油）进行 GC-EI-MS 分析会产生大量数据。为这种样品创建预测模型并观察复杂数据集内的模式提出了重大挑战，需要应用强大的数据处理和数据分析方法。因此，多年来已经针对该目的研究和开发了各种各样的软件和算法。本文综述了这方面的研究工作，并尝试对迄今为止使用 GC-EI-MS 对精油进行代谢组学研究中报告的不同数据处理和数据分析程序进行分类和比较。
