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Food Chem

Title:		Physicochemical parameters combined flash GC e-nose and artificial neural network for quality and volatile characterization of vinegar with different brewing techniques
Author(s):		Li Y; Fei C; Mao C; Ji D; Gong J; Qin Y; Qu L; Zhang W; Bian Z; Su L; Lu T;
Address:		"College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China. College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China; College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230038, China. College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China; Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, China. College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China. Electronic address: sll2020@njucm.edu.cn. College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China. Electronic address: ltl2021@njucm.edu.cn"
Journal Title:		Food Chem
Year:		2022
Volume:		20211128
Issue:
Page Number:		131658 -
DOI:		10.1016/j.foodchem.2021.131658
ISSN/ISBN:		1873-7072 (Electronic) 0308-8146 (Linking)
Abstract:		"Vinegar is a kind of traditional fermented food, there are significant variances in quality and flavor due to differences in raw ingredients and processes. The quality assessment and flavor characteristics of 69 vinegar samples with 5 brewing processes were analyzed by physicochemical parameters combined with flash gas chromatography (GC) e-nose. The evaluation system of quality and the detection method of flavor profile were established. 17 volatile flavor compounds and potential flavor differential compounds of each brewing process were identified. The artificial neural network (ANN) analysis model was established based on the physicochemical parameters and the analysis of flash GC e-nose. Although the physicochemical parameters were more intuitive in quality evaluating, the flash GC e-nose could better reflect the flavor characteristics of vinegar samples and had better fitting, prediction and discrimination ability, the correct rates of training and prediction of flash GC e-nose trained ANN model were 98.6% and 96.7%, respectively"
Keywords:		"Acetic Acid Chromatography, Gas *Electronic Nose Gas Chromatography-Mass Spectrometry Neural Networks, Computer Odorants/analysis *Volatile Organic Compounds/analysis Artificial neural network (ANN) Classification Flash GC e-nose Vinegar Volatiles;"
Notes:		"MedlineLi, Yu Fei, Chenghao Mao, Chunqin Ji, De Gong, Jingwen Qin, Yuwen Qu, Lingyun Zhang, Wei Bian, Zhenhua Su, Lianlin Lu, Tulin eng England 2021/12/14 Food Chem. 2022 Apr 16; 374:131658. doi: 10.1016/j.foodchem.2021.131658. Epub 2021 Nov 28"