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author:

Yao, Yishun (Yao, Yishun.) [1] | Wang, Shaoyun (Wang, Shaoyun.) [2] | Wang, Xiaomin (Wang, Xiaomin.) [3] | Cui, Heping (Cui, Heping.) [4] | Yu, Jingyang (Yu, Jingyang.) [5] | Hayat, Khizar (Hayat, Khizar.) [6] | Huang, Qingrong (Huang, Qingrong.) [7] | Zhang, Xiaoming (Zhang, Xiaoming.) [8] | Ho, Chi-Tang (Ho, Chi-Tang.) [9]

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EI

Abstract:

Background: The industrial cooking of traditional meat dishes is a current trend, driven by the acceleration of modern life. However, the distortion of the characteristic aroma in industrially cooked meat dishes has posed a challenge to the industry's progress. Therefore, an increasing number of studies have recently focused on the composition, formation mechanisms, and control methods of the characteristic aroma in traditional meat dishes, aiming to provide new insights for enhancing the aroma of industrial meat dishes. Scope and approach: The analytical techniques of the characteristic aroma in traditional meat dishes are compared. The mechanism of aroma formation in traditional meat dishes, especially the interaction between Maillard reaction intermediates and lipid oxidation products is systematically reviewed. Building upon these insights, some strategies to improve the aroma quality and promote the standardization of industrialized meat dishes are proposed. Key findings and conclusions: Molecular sensory science is the main method for identifying key aroma compounds in traditional meat dishes, but aroma extraction and chromatographic separation techniques still require improvement. The integration of multi-omics techniques with simulated model experiments exhibits significant potential in uncovering the key precursors and formation pathways of the characteristic aroma. Accurate control and standardization of meat aroma during industrial cooking can be achieved through control of the precursors in raw meat, optimization and innovation of the cooking technologies, integration of machine learning-driven aroma evaluation, and prediction and intelligent control of industrial cooking parameters. © 2025 Elsevier Ltd

Keyword:

Agriculture Industrial heating Intelligent control Learning systems Livestock Machine learning Odor control Poultry Reaction intermediates Standardization Thermal processing (foods)

Community:

  • [ 1 ] [Yao, Yishun]State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Avenue, Jiangsu, Wuxi; 214122, China
  • [ 2 ] [Wang, Shaoyun]College of Biological Science and Engineering, Fuzhou University, Fuzhou, China
  • [ 3 ] [Wang, Xiaomin]State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Avenue, Jiangsu, Wuxi; 214122, China
  • [ 4 ] [Cui, Heping]State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Avenue, Jiangsu, Wuxi; 214122, China
  • [ 5 ] [Yu, Jingyang]State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Avenue, Jiangsu, Wuxi; 214122, China
  • [ 6 ] [Hayat, Khizar]Department of Food and Animal Sciences, Alabama A&M University, Normal; AL; 35762, United States
  • [ 7 ] [Huang, Qingrong]Department of Food Science, Rutgers University, 65 Dudley Road, New Brunswick; NJ; 08901, United States
  • [ 8 ] [Zhang, Xiaoming]State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Avenue, Jiangsu, Wuxi; 214122, China
  • [ 9 ] [Ho, Chi-Tang]Department of Food Science, Rutgers University, 65 Dudley Road, New Brunswick; NJ; 08901, United States

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Source :

Trends in Food Science and Technology

ISSN: 0924-2244

Year: 2025

Volume: 163

1 5 . 1 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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