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Dispersive liquid–liquid microextraction and gas chromatography accurate mass spectrometry for extraction and non-targeted profiling of volatile and semi-volatile compounds in grape marc distillates
Journal of Chromatography A ( IF 3.8 ) Pub Date : 2018-03-03 , DOI: 10.1016/j.chroma.2018.03.003
Ariel Fontana , Isaac Rodríguez , Rafael Cela

The suitability of dispersive liquid–liquid microextraction (DLLME) and gas chromatography accurate mass spectrometry (GC–MS), based on a time-of-flight (TOF) MS analyzer and using electron ionization (EI), for the characterization of volatile and semi-volatile profiles of grape marc distillates (grappa) are evaluated. DLLME conditions are optimized with a selection of compounds, from different chemical families, present in the distillate spirit. Under final working conditions, 2.5 mL of sample and 0.5 mL of organic solvents are consumed in the sample preparation process. The absolute extraction efficiencies ranged from 30 to 100%, depending on the compound. For the same sample volume, DLLME provided higher responses than solid-phase microextraction (SPME) for most of the model compounds. The GC-EI-TOF-MS records of grappa samples were processed using a data mining non-targeted search algorithm. In this way, chromatographic peaks and accurate EI-MS spectra of sample components were linked. The identities of more than 140 of these components are proposed from comparison of their accurate spectra with those in a low resolution EI-MS database, accurate masses of most intense fragment ions of known structure, and available chromatographic retention index. The use of chromatographic and spectral data, associated to the set of components mined from different grappa samples, for multivariate analysis purposes is also illustrated in the study.



中文翻译:

分散液-液微萃取和气相色谱精确质谱法对葡萄渣中的挥发性和半挥发性化合物进行萃取和非目标分析

基于飞行时间(TOF)MS分析仪和电子电离(EI)的分散液-液微萃取(DLLME)和气相色谱精确质谱(GC-MS)的适用性,用于表征挥发性有机化合物和评估了葡萄渣(格拉巴酒)的半挥发性特征。DLLME条件是通过选择蒸馏酒中存在的不同化学族的化合物来优化的。在最终工作条件下,样品制备过程消耗了2.5 mL样品和0.5 mL有机溶剂。取决于化合物,绝对提取效率为30%至100%。对于相同的样品量,对于大多数模型化合物,DLLME提供的响应均比固相微萃取(SPME)高。格拉巴样品的GC-EI-TOF-MS记录使用数据挖掘非目标搜索算法进行处理。这样,色谱峰与样品组分的准确EI-MS谱图关联了起来。通过将它们的准确光谱与低分辨率EI-MS数据库中的准确光谱进行比较,已知结构的最强碎片离子的精确质量以及可用的色谱保留指数,提出了140多个这些组分的身份。该研究还说明了将色谱和光谱数据与从不同格拉巴酒中提取的成分组相关联的用途进行多变量分析。通过将它们的准确光谱与低分辨率EI-MS数据库中的准确光谱进行比较,已知结构的最强碎片离子的精确质量以及可用的色谱保留指数,提出了140多个这些组分的身份。该研究还说明了将色谱和光谱数据与从不同格拉巴酒中提取的成分组相关联的用途进行多变量分析。通过将它们的准确光谱与低分辨率EI-MS数据库中的准确光谱进行比较,已知结构的最强碎片离子的精确质量以及可用的色谱保留指数,提出了140多个这些组分的身份。该研究还说明了将色谱和光谱数据与从不同格拉巴酒中提取的成分组相关联的用途进行多变量分析。

更新日期:2018-03-03
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