Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 65888, United States.
Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States; Texas Institute for Discovery Education in Science and Freshman Research Initiative, The University of Texas at Austin, Austin, TX 78712, United States.
Food Chem. 2021 Aug 30;354:129531. doi: 10.1016/j.foodchem.2021.129531. Epub 2021 Mar 10.
Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. 'Pinot noir' wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.
葡萄园、产区和年份，使用来自同一接穗克隆（黑皮诺 667）的 15 种酿酒葡萄品种‘黑皮诺’葡萄酒进行评估。这些葡萄酒由两个年份（2015 年和 2016 年）和美国太平洋沿岸的八个不同产区生产。我们成功地通过将非靶向 1D H NMR 分析与基于肽的差异感应阵列相结合，改善了所选黑皮诺葡萄酒的分类。NMR 光谱用于评估葡萄酒的化学指纹图谱，而基于肽的感应阵列通过表征酚类图谱来模拟味觉、嗅觉和口感质地的感觉。组合的 NMR 和差异感应阵列数据集的多元和单变量统计分析根据与生长地区、葡萄园和年份相关的独特代谢特征对遗传上相同的黑皮诺葡萄酒进行分类。
