Vintage analysis of Chinese Baijiu by GC and 1H NMR combined with multivariable analysis. (30th October 2021)

• Record Type: Journal Article
• Authors: Li, Yicong; Fan, Shuangxi; Li, Anjun; Liu, Guoying; Lu, Wei; Yang, Bo; Wang, Fengxian; Zhang, Xin; Gao, Xiaojuan; Lǚ, Zhiyuan; Su, Ning; Wang, Guanghao; Liu, Yinuo; Ji, Xin; Xin, Peng; Li, Guohui; Wang, Daobing; Lu, Fuping; Zhong, Qiding
• Is Part Of: Food chemistry. Volume 360(2021)
• Publication Date: 2021-10-30
• Subjects: 1H NMR Hydrogen proton nuclear magnetic resonance -- 3D-EEM Three dimension-excitation emission matrix fluorescence spectroscopy -- ANN Artificial neural network -- CA Clustering analysis -- Ctr Centering -- CV Cross validation -- E-tongue Electronic tongue -- FA Factor analysis -- FT-IR Fourier transform-infrared spectroscopy -- FT-Raman Fourier transform-Raman spectroscopy -- GC Gas chromatography -- GC- E-nose Gas chromatography-electronic noise -- GC-MS Gas chromatography-mass spectrometry -- GC-O Gas chromatography-olfactometry -- HPLC-Q-Orbitrap-MS High performance liquid chromatography-quadrupole-orbitrap mass spectrometry -- ICP-MS Inductively coupled plasma mass spectrometry -- IR-MS Isotope ratio-mass spectrometry -- LDA Linear discriminant analysis -- LOOCV Leave-one-out cross validation -- LR Linear regression -- LS-SVM Least squares support vector machine -- Par Pareto scaling -- PCA Principal component analysis -- PLS-DA Partial least squares discriminant analysis -- PLSR Partial least squares regression -- RMSECV Root mean square error of calibration validation -- RMSEP Root mean square error of prediction -- Roman Roman spectroscopy -- SD Standard deviation -- SLDA Stepwise linear discriminant analysis -- SVR Support vector regression -- UV Unit variance scaling -- Vis-NIR Visible and near infrared spectroscopy; Chinese Baijiu -- Vintage -- 1H NMR -- GC -- Multivariate statistical analysis; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.129937
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Baijiu vintage analysis was achieved by targeted GC and non-targeted 1 H NMR. Three variable selection methods were used to decrease input variables. PLSR vintage model was optimized to have robust prediction performance. Ten components were found as potential vintage markers for Chinese Baijiu. Abstract: Economical-driven counterfeit and inferior aged Chinese Baijiu has caused serious concern of publicity in China. In this study, a total of 167 authentic Chinese Baijiu samples with different vintages including 3 flavor types were carefully collected. Gas chromatography (GC) was used to determine main volatile components and proton nuclear magnetic resonance ( 1 H NMR) spectroscopy was employed to obtain non-targeted fingerprints of Chinese Baijiu samples. Partial least squares regression (PLSR) models, which were confirmed by internal and external validation, were established for effectively identifying actual storage vintage of Chinese Baijiu with various brands, flavor types. Centering (Ctr), pareto scaling (Par), unit variance scaling (UV) data pretreatment methods, principal components (PCs), and three modified variable selection methods were proposed to successfully optimize the vintage model and effectively extract important vintage characteristic factors. This study demonstrated that NMR and GC combined with multivariate statistical analysis are effective tools for validating vintage authenticity of Chinese Baijiu.