Center for Viticulture & Enology, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Viticulture and Enology, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Center for Viticulture & Enology, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Viticulture and Enology, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Food Res Int. 2023 Feb;164:112440. doi: 10.1016/j.foodres.2022.112440. Epub 2022 Dec 31.
Correlating aroma expression with volatile compounds has long been an ambition in researches of flavor chemistry. To propose a reliable methodology to depict wine aroma, 76 oak barrel-aged dry red wines were investigated through the combination of machine learning algorithm and multivariate analysis. Aromatic characteristic was evaluated by quantitative descriptive analysis (QDA), while non- or oak derived volatiles were detected by HS-SPME-GC-MS and targeted SPE-GC-QqQ-MS/MS, respectively. Results showed that variable importance for projection values (VIPs) from partial least-squares regression (PLSR) and mean decrease accuracy (MDA) from random forest were efficient parameters for feature selection. The correlating accuracy of the optimal PLSR model to predict intensities of different aroma characteristics through selected volatile compounds could achieve 0.754 to 0.943, representing potential application to manage wine aroma by chemical assay in winemaking. From the perspective of mathematical modeling in the real wine matrix, the network analysis between aroma characteristics and key volatile compounds indicated that the expression of oak aroma was not only directly contributed by volatiles derived from oak wood, but also influenced by ethyl esters, including ethyl acetate, ethyl butanoate, ethyl hexanoate, ethyl decanoate, and ethyl nonanoate.
长期以来，将香气表达与挥发性化合物相关联一直是风味化学研究的目标。为了提出一种可靠的方法来描述葡萄酒香气，通过机器学习算法和多变量分析相结合，对 76 种橡木桶陈酿干红葡萄酒进行了研究。通过定量描述性分析（QDA）评估香气特征，通过 HS-SPME-GC-MS 和靶向 SPE-GC-QqQ-MS/MS 分别检测非橡木或橡木衍生的挥发性化合物。结果表明，偏最小二乘回归（PLSR）的变量重要性投影值（VIPs）和随机森林的平均减少精度（MDA）是特征选择的有效参数。通过所选挥发性化合物预测不同香气特征强度的最优 PLSR 模型的相关准确性可达到 0.754 至 0.943，这代表了通过化学分析在酿酒过程中管理葡萄酒香气的潜在应用。从真实葡萄酒基质中的数学建模角度来看，香气特征和关键挥发性化合物之间的网络分析表明，橡木香气的表达不仅直接由橡木木材衍生的挥发性化合物贡献，还受包括乙酸乙酯、丁酸乙酯、己酸乙酯、辛酸乙酯和壬酸乙酯在内的乙酯的影响。
