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Ling M, Bai X, Cui D, Shi Y, Duan C, Lan Y. An efficient methodology for modeling to predict wine aroma expression based on quantitative data of volatile compounds: A case study of oak barrel-aged red wines. Food Res Int 2023; 164:112440. [PMID: 36738004 DOI: 10.1016/j.foodres.2022.112440] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/26/2022] [Accepted: 12/29/2022] [Indexed: 01/01/2023]
Abstract
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.
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Affiliation(s)
- Mengqi Ling
- 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
| | - Xiaoxuan Bai
- 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
| | - Dongsheng Cui
- 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
| | - Ying Shi
- 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
| | - Changqing Duan
- 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
| | - Yibin Lan
- 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.
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