FOOD SCIENCE ›› 2019, Vol. 40 ›› Issue (8): 218-224.doi: 10.7506/spkx1002-6630-20180429-379

• Composition Analysis • Previous Articles     Next Articles

Effects of Storage Environment on the Main Chemical Components of Raw Pu-erh Tea

NING Jingming, XU Shanshan, HOU Zhiwei, HU Xin, ZHANG Zhengzhu   

  1. State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
  • Online:2019-04-25 Published:2019-05-05

Abstract: Storage time and conditions are the important factors affecting the aging of raw Pu-erh tea. Tea samples produced in the years 2008, 2010, 2012, 2014 and 2016 and stored in Xinjiang and Guangdong were detected for their characteristic components by high performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS) and an automatic amino acid analyzer. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed for differential clustering of Pu-erh tea from these two different storage areas. Variable importance in projection (VIP) and S-plot were applied to find out the important components for classification. The results showed that the aging rate of raw Pu-erh tea was faster when stored in Guangdong than in Xinjiang, but its quality was inferior to that in Xinjiang. The contents of all main chemical components except for gallic acid in Guangdong samples were lower than those in Xinjiang samples, and the degradation rate was also faster than that in Xinjiang samples. Beta-damascenone, 2,6,6-trimethyl-1-cyclohexene- 1-carboxyaldehyde, alpha-pineol, carbamate, palmitoleic acid, beta-cyclocitral, linolenic acid, neroli, benzaldehyde and linoleic acid were the important differential volatile components. The volatile components changed sharply in Guangdong, while they changed slowly in Xinjiang. This study provides a theoretical basis for the scientific storage of tea, and also helps to guide the market for scientific consumption.

Key words: raw Pu-erh tea, temperature and humidity, aging time, orthogonal partial least squares-discriminant analysis

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