HS-GC-IMS with PCA to analyze volatile flavor compounds across different production stages of fermented soybean whey tofu

•Establishment of flavor fingerprint of soybean fermented whey tofu (FSWT).•GC-IMS combined with PCA was applied to detect the flavor of fermented soybean products.•The flavor changes during the processing of FSWT. The variations in flavor substances across the different stages of fermented soybean...

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Bibliographic Details
Published in:	Food chemistry 2021-06, Vol.346, p.128880-128880, Article 128880
Main Authors:	Yang, Yang, Wang, Bing, Fu, Yu, Shi, Yan-guo, Chen, Feng-lian, Guan, Hua-nan, Liu, Lin-lin, Zhang, Chun-yan, Zhu, Peng-yu, Liu, Ying, Zhang, Na
Format:	Article
Language:	eng
Subjects:	Chromatography, Gas Fermentation Fermented soybean whey tofu Flavor fingerprint Glycine max - chemistry Glycine max - metabolism Headspace-gas chromatography-ion mobility spectrometry Ion Mobility Spectrometry Principal Component Analysis Soy Foods - analysis Volatile Organic Compounds - analysis Volatile Organic Compounds - chemistry Volatile profile
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Summary:	•Establishment of flavor fingerprint of soybean fermented whey tofu (FSWT).•GC-IMS combined with PCA was applied to detect the flavor of fermented soybean products.•The flavor changes during the processing of FSWT. The variations in flavor substances across the different stages of fermented soybean whey tofu (FSWT) production were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with principal component analysis (PCA). The results revealed 24 representative flavor compounds in the samples across all production stages. After heating, the signal intensity of hexanal, 1-octen-3-ol, heptanal, and (E)-2-hexenol, which are unpleasant flavor substances found in raw soymilk, weakened, whereas those of some aroma substances increased. Furthermore, fermented flavor compounds, namely, 2-heptanone, 2-pentylfuran, pentanal, and 2,3-butanedione, were produced after the addition of fermented soybean whey as a coagulant. A PCA based on the signal intensity of the detected volatile compounds revealed effective differentiation of samples from different stages into comparatively independent spaces. These results showed that the flavor fingerprints of the samples from different stages of FSWT production can be successfully built using HS-GC-IMS and PCA based on the detected volatile compounds.
ISSN:	0308-8146 1873-7072