酿造的
化学计量学
主成分分析
鉴定(生物学)
风味
化学
质谱法
偏最小二乘回归
色谱法
食品科学
计算机科学
人工智能
机器学习
生物化学
植物
生物
作者
Miao Li,Zhengyu Zhao,Zhang Yu-song,Xinguang Guo,Yu Zhang,J. Wang,Yangqingxue Liu,Lihua Yang,Weiwei Mou,Xin Zhang,Hongbo Gao
出处
期刊:Food Chemistry
[Elsevier]
日期:2024-02-09
卷期号:444: 138690-138690
被引量:1
标识
DOI:10.1016/j.foodchem.2024.138690
摘要
The identification of baijiu vintage is crucial for quality assessment and economic value determination. However, its complex composition and multifaceted influences pose significant technical challenges, necessitating research into its aging mechanisms and the development of related identification methods. This study utilized Chemometrics in conjunction with GC × GC-TOFMS for Baijiu Vintage identification. Data compression achieved a reduction of over 1000-fold without compromising key information, enabling analysis on many samples and get their changing regular in a big matrix by MCR. Subsequently, MCR-ALS facilitated the extraction of physical and chemical meaningful information related to baijiu vintage. Key MCR principal components suitable for qualitative and quantitative assessments were selected using CARS-PLS. The regression model demonstrated errors of less than one year. Furthermore, a PLS-DA model provided 30 MCR principal components as potential markers. The research results provide technical support for baijiu vintage identification and lay the groundwork for studying the changing patterns of flavor compounds in baijiu.
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