Characterization of aroma profiles of chinese four most famous traditional red-cooked chickens using GC–MS, GC-IMS, and E-nose

化学 芳香 电子鼻 食品科学 色谱法 气相色谱-质谱法 质谱法 生物 神经科学
作者
X. Sun,Yumei Yu,Ahmed S.M. Saleh,Xinyu Yang,Jiale Ma,Ziwu Gao,Dequan Zhang,Wenhao Li,Zhenyu Wang
出处
期刊:Food Research International [Elsevier BV]
卷期号:173 (Pt 1): 113335-113335 被引量:100
标识
DOI:10.1016/j.foodres.2023.113335
摘要

The aroma profile of the four most popular types of red-cooked chickens in China was analyzed using a combination of gas chromatography-mass spectrometry (GC-MS), gas chromatography-ion mobility spectrometry (GC-IMS), and electronic nose (E-nose). Principal component analysis (PCA) demonstrated that the E-nose could successfully distinguish between the four types of red-cooked chickens. Additionally, a fingerprint was created using GC-IMS to examine the variations in volatile organic compounds (VOCs) distribution in the four chicken types. A total number of 84 and 62 VOCs were identified in the four types of red-cooked chickens using GC-MS and GC-IMS, respectively. Odor activity value (OAV) showed that 1-octen-3-ol, heptanal, hexanal, nonanal, octanal, eugenol, dimethyl trisulfide, anethole, anisaldehyde, estragole, and eucalyptol were the key volatile components in all samples. Furthermore, partial least squares-discriminant analysis (PLS-DA) demonstrated that (E, E)-2,4-decadienal, dimethyl trisulfide, octanal, eugenol, hexanal, (E)-2-nonenal, 1-octen-3-ol, butanal, ethyl acetate, ethyl acetate (D), nonanal, and heptanal could be used as markers to distinguish aroma of the four types of red-cooked chickens. Also, it is worth noting that levels of VOCs varied between chicken breast muscle and skin. The obtained results offer theoretical and technological support for flavor identification and control in red-cooked chickens to enhance their quality and encourage consumer consumption, which will be advantageous for the red-cooked chicken production chain.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
薛岩晟发布了新的文献求助10
刚刚
刚刚
故意的以旋完成签到,获得积分10
刚刚
5秒前
海洋完成签到,获得积分10
7秒前
领导范儿的应助被欢喜采纳,获得10
7秒前
科研通AI6.4的应助被七月流火采纳,获得50
8秒前
随风发布了新的文献求助10
9秒前
10秒前
molihuakai的应助被我www采纳,获得10
10秒前
松奈子完成签到 ,获得积分10
13秒前
14秒前
14秒前
威武苑睐完成签到,获得积分20
16秒前
17秒前
木瑾完成签到 ,获得积分10
17秒前
17秒前
万能图书馆的应助被坦率帅哥采纳,获得10
18秒前
waka完成签到,获得积分10
19秒前
我www发布了新的文献求助10
19秒前
汉堡包的应助被威武苑睐采纳,获得10
20秒前
11发布了新的文献求助10
20秒前
东风压倒西风完成签到,获得积分10
20秒前
hajimi123发布了新的文献求助10
21秒前
Mockingbird发布了新的文献求助10
21秒前
橘子和柚子完成签到 ,获得积分20
21秒前
21秒前
21秒前
chwjx完成签到,获得积分10
22秒前
GYY发布了新的文献求助30
22秒前
23秒前
25秒前
25秒前
77完成签到,获得积分10
25秒前
利奈唑胺完成签到,获得积分10
25秒前
25秒前
sswaggyc完成签到,获得积分10
27秒前
windsky完成签到,获得积分10
28秒前
28秒前
Literature发布了新的文献求助200
28秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
自動車の空力技術 800
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Issues in Task-Based Language Teaching 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7783524
求助须知:如何正确求助?哪些是违规求助? 9322854
关于积分的说明 20391805
捐赠科研通 7372172
什么是DOI,文献DOI怎么找? 3320690
关于科研通互助平台的介绍 2468717
邀请新用户注册赠送积分活动 2336931