Top Institute Food and Nutrition, P.O. Box 557, 6700 AN Wageningen, The Netherlands.
Appl Environ Microbiol. 2011 Sep;77(17):6233-9. doi: 10.1128/AEM.00352-11. Epub 2011 Jul 8.
With the advent of the -omics era, classical technology platforms, such as hyphenated mass spectrometry, are currently undergoing a transformation toward high-throughput application. These novel platforms yield highly detailed metabolite profiles in large numbers of samples. Such profiles can be used as fingerprints for the accurate identification and classification of samples as well as for the study of effects of experimental conditions on the concentrations of specific metabolites. Challenges for the application of these methods lie in the acquisition of high-quality data, data normalization, and data mining. Here, a high-throughput fingerprinting approach based on analysis of headspace volatiles using ultrafast gas chromatography coupled to time of flight mass spectrometry (ultrafast GC/TOF-MS) was developed and evaluated for classification and screening purposes in food fermentation. GC-MS mass spectra of headspace samples of milk fermented by different mixed cultures of lactic acid bacteria (LAB) were collected and preprocessed in MetAlign, a dedicated software package for the preprocessing and comparison of liquid chromatography (LC)-MS and GC-MS data. The Random Forest algorithm was used to detect mass peaks that discriminated combinations of species or strains used in fermentations. Many of these mass peaks originated from key flavor compounds, indicating that the presence or absence of individual strains or combinations of strains significantly influenced the concentrations of these components. We demonstrate that the approach can be used for purposes like the selection of strains from collections based on flavor characteristics and the screening of (mixed) cultures for the presence or absence of strains. In addition, we show that strain-specific flavor characteristics can be traced back to genetic markers when comparative genome hybridization (CGH) data are available.
随着组学时代的到来，传统的技术平台，如串联质谱技术，目前正朝着高通量应用的方向发展。这些新型平台可以在大量样本中产生高度详细的代谢物图谱。这些图谱可作为指纹，用于准确识别和分类样本，以及研究实验条件对特定代谢物浓度的影响。这些方法的应用面临的挑战在于获取高质量的数据、数据归一化和数据挖掘。在这里，我们开发了一种基于顶空挥发物分析的高通量指纹图谱方法，该方法使用超快气相色谱-飞行时间质谱联用（ultrafast GC/TOF-MS），并在食品发酵中进行了分类和筛选的评估。收集了不同混合培养的乳酸菌（LAB）发酵牛奶的顶空样品的 GC-MS 质谱，并在 MetAlign 中进行预处理，这是一个专门用于预处理和比较液相色谱（LC）-MS 和 GC-MS 数据的软件包。随机森林算法用于检测区分发酵中使用的物种或菌株组合的质谱峰。其中许多质谱峰来自关键风味化合物，表明个别菌株或菌株组合的存在与否显著影响这些成分的浓度。我们证明了该方法可用于根据风味特征从菌株库中选择菌株，以及筛选（混合）培养物中是否存在菌株。此外，当比较基因组杂交（CGH）数据可用时，我们还表明可以追踪到菌株特异性风味特征的遗传标记。
