Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Via R. Balzarini 1, 64100 Teramo, Italy.
Food Consultant as BeerStudioLab, Via Nazionale per Teramo 75, 64021 Giulianova, Italy.
Food Chem. 2024 May 30;441:138387. doi: 10.1016/j.foodchem.2024.138387. Epub 2024 Jan 6.
This study was aimed to unravel the effect of raw materials (barley and wheat), wheat concentration (0, 25, 40, and 100 %), wheat species (common and durum), beer style (Blanche and Weiss), and yeast (US-05 and WB-06) on the chemical composition, volatiles, and sensory profile of wheat craft beers by using a multivariate statistical approach. Beer samples were analysed for their composition, volatiles and sensory profile and data were processed using unsupervised multivariate analyses, PLS regression and a multi-omics approach using multi-block PLS-DA. Multi-block variable sparsification was used as an embedded dimension reduction step. The adopted multi-omics approach permitted to correctly classify beers with different styles and wheat concentration, and to accurate classify (95 % accuracy) beers according to yeast type. Wheat species was of lower importance since it permitted a classification with 49 % accuracy which increased to 74 % in Blanche beers, thus suggesting that malting flattened differences determined by wheat species.
本研究旨在通过多变量统计方法，揭示原料（大麦和小麦）、小麦浓度（0、25、40 和 100%）、小麦品种（普通小麦和硬粒小麦）、啤酒风格（白啤酒和小麦啤酒）和酵母（US-05 和 WB-06）对小麦精酿啤酒的化学成分、挥发性成分和感官特征的影响。对啤酒样品进行成分、挥发性成分和感官特征分析，使用无监督多元分析、PLS 回归和多组学方法（多块 PLS-DA）处理数据。采用多块变量稀疏化作为嵌入降维步骤。所采用的多组学方法能够正确地对不同风格和小麦浓度的啤酒进行分类，并根据酵母类型准确地（95%的准确率）对啤酒进行分类。小麦品种的重要性较低，因为它的分类准确率为 49%，而在白啤酒中增加到 74%，这表明制麦过程中使小麦品种造成的差异趋于一致。
