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Analysis of volatile organic compounds in black garlic made of garlic bulbs after harvesting blanched garlic leaves at different growth periods based on PCA and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS)

Journal 2022 · IF 2.609 · 46(5): e16550 · 发表于 2022 年 3 月 19 日
作者: Zhen Liu,Da-Cheng Kang,Xin-Rui Li,Ling-Xiao Liu,Yun-Guo Liu
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摘要

In this study, garlic bulbs after harvesting blanched garlic leaves at four different growth periods were used as the main experimental objects, and black garlic was prepared by solid-state fermentation at variable temperatures. The volatile organic compounds and flavor fingerprints of black garlic were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and the differences of volatile organic compounds in black garlic were compared by principal component analysis (PCA). The physical and chemical indexes of black garlic changed slightly. As the Maillard reaction went on more thoroughly, the content of 5- HMF in black garlic increased. GC-IMS provided information on the characteristics and intensity of 55 volatile organic compounds (monomers and dimers). The results showed that the relative content of organosulfur compounds in black garlic decreased gradually with the extension of the garlic bulb growth period, which was due to the inherent instability of the organosulfur compounds and more complex compounds being formed as precursors in further reactions. The results by PCA showed that BG0 samples can be well distinguished from BG20 samples. Novelty impact statement In this study, garlic bulbs after harvesting blanched garlic leaves at four different growth periods were used as the main experimental objects, and black garlic was prepared by solid-state fermentation at variable temperatures. The volatile organic compounds and flavor fingerprints of black garlic were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and the differences of volatile organic compounds in black garlic were compared by principal component analysis (PCA).

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