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使用二维气相色谱 - 飞行时间质谱法对啤酒挥发性香气成分进行分析的先进方法优化

Advanced method optimization for volatile aroma profiling of beer using two-dimensional gas chromatography time-of-flight mass spectrometry.

作者信息

Stefanuto Pierre-Hugues, Perrault Katelynn A, Dubois Lena M, L'Homme Benjamin, Allen Catherine, Loughnane Caitriona, Ochiai Nobuo, Focant Jean-François

机构信息

Organic and Biological Analytical Chemistry Group - CART, Chemistry Department, University of Liège, Allée du Six Aout 11, B6c, Quartier Agora, Sart-Tilman, B-4000 Liège, Belgium.

Organic and Biological Analytical Chemistry Group - CART, Chemistry Department, University of Liège, Allée du Six Aout 11, B6c, Quartier Agora, Sart-Tilman, B-4000 Liège, Belgium; Chaminade University of Honolulu, Forensic Sciences Unit, 3140 Waialae Avenue, Honolulu, HI 96815, USA.

出版信息

J Chromatogr A. 2017 Jul 21;1507:45-52. doi: 10.1016/j.chroma.2017.05.064. Epub 2017 Jun 1.


DOI:10.1016/j.chroma.2017.05.064
PMID:28587778
Abstract

The complex mixture of volatile organic compounds (VOCs) present in the headspace of Trappist and craft beers was studied to illustrate the efficiency of thermal desorption (TD) comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) for highlighting subtle differences between highly complex mixtures of VOCs. Headspace solid-phase microextraction (HS-SPME), multiple (and classical) stir bar sorptive extraction (mSBSE), static headspace (SHS), and dynamic headspace (DHS) were compared for the extraction of a set of 21 representative flavor compounds of beer aroma. A Box-Behnken surface response methodology experimental design optimization (DOE) was used for convex hull calculation (Delaunay's triangulation algorithms) of peak dispersion in the chromatographic space. The predicted value of 0.5 for the ratio between the convex hull and the available space was 10% higher than the experimental value, demonstrating the usefulness of the approach to improve optimization of the GC×GC separation. Chemical variations amongst aligned chromatograms were studied by means of Fisher Ratio (FR) determination and F-distribution threshold filtration at different significance levels (α=0.05 and 0.01) and based on z-score normalized area for data reduction. Statistically significant compounds were highlighted following principal component analysis (PCA) and hierarchical cluster analysis (HCA). The dendrogram structure not only provided clear visual information about similarities between products but also permitted direct identification of the chemicals and their relative weight in clustering. The effective coupling of DHS-TD-GC×GC-TOFMS with PCA and HCA was able to highlight the differences and common typical VOC patterns among 24 samples of different Trappist and selected Canadian craft beers.

摘要

对特拉普啤酒和精酿啤酒顶空中存在的挥发性有机化合物(VOCs)复杂混合物进行了研究,以说明热脱附(TD)全二维气相色谱飞行时间质谱(GC×GC - TOFMS)在突出高度复杂的VOCs混合物之间细微差异方面的效率。比较了顶空固相微萃取(HS - SPME)、多次(和经典)搅拌棒吸附萃取(mSBSE)、静态顶空(SHS)和动态顶空(DHS)对一组21种啤酒香气代表性风味化合物的萃取效果。采用Box - Behnken表面响应方法实验设计优化(DOE)对色谱空间中峰分散的凸包进行计算(德劳内三角剖分算法)。凸包与可用空间之比的预测值为0.5,比实验值高10%,证明了该方法在改进GC×GC分离优化方面的有效性。通过在不同显著性水平(α = 0.05和0.01)下基于z分数归一化面积进行Fisher比率(FR)测定和F分布阈值过滤,研究了对齐色谱图之间的化学变化以进行数据缩减。通过主成分分析(PCA)和层次聚类分析(HCA)突出了具有统计学意义的化合物。树形图结构不仅提供了关于产品之间相似性的清晰视觉信息,还允许直接识别化学物质及其在聚类中的相对权重。DHS - TD - GC×GC - TOFMS与PCA和HCA的有效结合能够突出24种不同特拉普啤酒和选定的加拿大精酿啤酒样品之间的差异和常见典型VOC模式。

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