The GC/MS-based metabolomics method was done to show differential metabolites between ale and lager beer. The metabolite profiles of ale beers were significantly different from those of lager beers. Ale beers contained significantly higher levels of propylene glycol, 2,3-butanediol, propionic acid, glycerol, succinic acid, malic acid, fructose, sorbitol, palmitic acid, stearic acid, and sucrose, but lower levels of alanine, glycine, leucine, isoleucine, phenylalanine, and tryptophan compared to lager beers. Relatively high correlations were found between stearic acid and palmitic acid (r = 0.98), leucine and isoleucine (r = 0.95), and glycine and alanine (r = 0.91). Alcohol content, hops, and wheat related metabolites were identified, which could affect the metabolic differences between ale and lager beer. A Random Forest approach extracted some key metabolites for beer type prediction with low error rates of 13.5% in the training set and 8.2% in the test set, respectively. This study highlights that GC/MS-based metabolomics is a powerful method for identifying metabolic differences between ale and lager beers.

进行了基于GC / MS的代谢组学方法，以显示淡啤酒和大啤酒之间的差异代谢产物。大麦啤酒的代谢物特征与大啤酒的代谢物特征显着不同。啤酒中丙二醇，2,3-丁二醇，丙酸，甘油，琥珀酸，苹果酸，果糖，山梨醇，棕榈酸，硬脂酸和蔗糖的含量明显较高，但丙氨酸，甘氨酸，亮氨酸，异亮氨酸，苯丙氨酸和色氨酸与啤酒相比。在硬脂酸和棕榈酸（r = 0.98），亮氨酸和异亮氨酸（r = 0.95）以及甘氨酸和丙氨酸（r = 0.91）之间发现相对较高的相关性。确定了酒精含量，啤酒花和小麦相关的代谢物，这可能影响淡啤酒和啤酒之间的代谢差异。随机森林方法提取了一些关键代谢物用于啤酒类型预测，在训练集中错误率分别为13.5％和测试集中8.2％的低错误率。这项研究强调，基于GC / MS的代谢组学是鉴定强麦啤酒和啤酒之间代谢差异的有效方法。