Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, Poland.
Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, Poland.
J Chromatogr A. 2021 Jan 11;1636:461774. doi: 10.1016/j.chroma.2020.461774. Epub 2020 Dec 3.
Baijiu is a traditional Chinese spirit with an extraordinarily rich pattern of volatile compounds resulting from the microflora involved in fermentation, as well as the complexity of technological process. Comprehensive two-dimensional gas chromatography in the conventional and reversed column setups (non-polar - polar and polar - non-polar columns) was tested for its ability to differentiate Baijiu samples in terms of their aroma and origin (region). A total of 65 Baijiu samples were used for the study and volatile compounds were extracted by SPME with a subsequent analysis by GC×GC-TOFMS. Orthogonality of both setups was compared, so was the number of compounds identified using each setup. Repeatability of compound groups for the conventional and reversed column setups was compared; moreover, multiblock orthogonal component analysis (MOCA) was used to visualize data sets. OPLS-DA was used for Baijiu classification. Both column setups provided excellent discrimination of the Light, Soy sauce, Feng and Herbal aromas. A better classification result for the Strong and Jian aromas was recorded for the conventional column setup. Within the Strong aroma using OPLS-DA both column setups provided perfect abilities to discriminate Baijiu from the Sichuan, Heilongjiang and Jiangsu regions. Two validation methods were applied in the classification models - all the predictive abilities evaluated by the internal validation were further confirmed by the external validation.
白酒是中国传统的烈酒,其发酵过程中涉及的微生物以及复杂的工艺导致其挥发性化合物的模式非常丰富。在常规和反向柱设置(非极性-极性和极性-非极性柱)中测试了综合二维气相色谱法,以评估其在香气和来源(地区)方面区分白酒样品的能力。共有 65 种白酒样品用于研究,通过 SPME 提取挥发性化合物,然后通过 GC×GC-TOFMS 进行分析。比较了两种设置的正交性,以及每种设置识别的化合物数量。比较了常规和反向柱设置的化合物组的重复性;此外,还使用多块正交成分分析(MOCA)来可视化数据集。使用 OPLS-DA 对白酒进行分类。两种柱设置都能很好地区分轻、酱油、凤香和草药香。常规柱设置对强和剑香的分类结果更好。在强香中,使用 OPLS-DA,两种柱设置都能够完美地区分来自四川、黑龙江和江苏地区的白酒。在分类模型中应用了两种验证方法——内部验证评估的所有预测能力都通过外部验证进一步确认。