4.4 Article

Age Discrimination of Chinese Baijiu Based on Midinfrared Spectroscopy and Chemometrics

期刊

JOURNAL OF FOOD QUALITY
卷 2021, 期 -, 页码 -

出版社

WILEY-HINDAWI
DOI: 10.1155/2021/5527826

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资金

  1. Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX20-0572, KYCX20-0207]
  2. Interdisciplinary Innovation Foundation for Graduates, NUAA [KXKCX JJ202008]

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Baijiu, a traditional Chinese liquor, is influenced by storage time. Through spectroscopy and chemometrics, different storage times of Baijiu samples can be accurately distinguished. The study found that the substances in Baijiu change differently during aging, and with the introduction of pretreatment methods and analysis techniques, the age of Baijiu can be effectively identified.
Baijiu is a traditional and popular Chinese liquor which is affected by the storage time. The longer the storage time of Baijiu is, the better its quality is. In this paper, the raw and mellow Baijiu samples from different storage time are discriminated accurately throughout midinfrared (MIR) spectroscopy and chemometrics. Firstly, changing regularities of the substances in Chinese Baijiu are discussed by gas chromatography-mass spectrometry (GC-MS) during the aging process. Then, infrared spectrums of Baijiu samples are processed by smoothing, multivariate baseline correction, and the first and second derivative processing, but no significant variation can be observed. Next, the spectral date pretreatment methods are constructively introduced, and principal component analysis (PCA) and discriminant analysis (DA) are developed for data analyses. The results show that the accuracy rates of samples by the DA method in calibration and validation sets are 91.7% and 100%, respectively. Consequently, an identification model based on support vector machine (SVM) and PCA is established combined with the grid search strategy and cross-validation methods to discriminate the age of Chinese Baijiu validly, where 100% classification accuracy rate is obtained in both training and test sets.

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