Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University, Beijing 100048, China; Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing 100048, China; Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing 100048, China.
Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University, Beijing 100048, China; Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing 100048, China; Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing 100048, China.
Food Chem. 2022 Apr 16;374:131641. doi: 10.1016/j.foodchem.2021.131641. Epub 2021 Nov 19.
Non-volatile organic acids (NVOAs) in 12 main flavor types of Baijiu were analyzed by a derivatization method combined with GC-MS and 38 NVOAs were quantified. Meanwhile, a flavoromics strategy based on the contents of NVOAs in the 12 flavor types of Baijiu was successfully used to the differentiation of Baijiu. PLS-DA models (explained variation, predictive capability) were used to consider different categories: fermentation process (0.931, 0.870), starter (0.921, 0.834), fermentation container (0.899, 0.810) and raw material (0.951, 0.909). Based on the selected categories, suitable separations were achieved, and the classification ability of these models were nearly 100%. As a result, the model demonstrated its ability to perfectly distinguish different types of Baijiu. Seventeen potential markers were identified by variable importance in projection method and were further processed using heatmap and hierarchical cluster analysis, indicating that the NVOAs had great discrimination power to differentiate Baijiu.
采用衍生化 GC-MS 法对 12 种主要香型白酒中的非挥发性有机酸(NVOAs)进行分析,共定量了 38 种 NVOAs。同时,成功地利用基于这 12 种香型白酒中 NVOAs 含量的代谢组学策略对白酒进行了区分。偏最小二乘判别分析(解释变异、预测能力)模型用于考虑不同类别:发酵过程(0.931、0.870)、酒曲(0.921、0.834)、发酵容器(0.899、0.810)和原料(0.951、0.909)。基于选定的类别,实现了合适的分离,这些模型的分类能力接近 100%。结果表明,该模型能够完美地区分不同类型的白酒。通过变量重要性投影法(VIP)鉴定了 17 个潜在标志物,进一步通过热图和层次聚类分析进行了处理,表明 NVOAs 对区分白酒具有很强的判别能力。