Review

A review on meat quality evaluation methods based on non-destructive computer vision and artificial intelligence technologies

Yinyan Shi1,2, Xiaochan Wang2, Borhan Mohammad1, Jennifer Young3, David Newman4, Eric Berg3, Xin Sun1,*
Author Information & Copyright
1Department of Agricultural and Biosystems Engineering, North Dakota State University, FARGO 58102, United States.
2College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
3Department of Animal Sciences, North Dakota State University, FARGO 58102, United States.
4Department of Animal Science, Arkansas State University, Jonesboro 72467, United States.
*Corresponding Author: Xin Sun, Department of Agricultural and Biosystems Engineering, North Dakota State University, FARGO 58102, United States. Phone: 701-231-5756. E-mail: xin.sun@ndsu.edu.

© Copyright 2021 Korean Society for Food Science of Animal Resources. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Mar 22, 2021 ; Revised: May 4, 2021 ; Accepted: May 05, 2021

Published Online: May 17, 2021

Abstract

Increasing meat demand in terms of both quality and quantity in conjunction with feeding a growing population has resulted in regulatory agencies imposing stringent guidelines on meat quality and safety. Objective and accurate rapid non-destructive detection methods and evaluation techniques based on artificial intelligence have become the research hotspot in recent years and have been widely applied in the meat industry. Therefore, this review surveyed the key technologies of non-destructive detection for meat quality, mainly including ultrasonic technology, machine (computer) vision technology, near-infrared spectroscopy technology, hyperspectral technology, Raman spectra technology, and electronic nose/tongue. The technical characteristics and evaluation methods were compared and analyzed the practical applications of non-destructive detection technologies in meat quality assessment were explored, and the current challenges and future research directions were discussed. This literature presented in this review found clearly demonstrates that previous research on non-destructive technologies is of great significance to ensure consumers' urgent demand for high-quality meat by promoting automatic, real-time inspection and quality control in meat production. In the near future, with ever-growing application requirements and research developments, it is a trend to integrate such systems to provide effective solutions for various grain quality evaluation applications.

Keywords: Meat quality; Non-destructive detection; Key technology; Grading assessment; Industrial application


Change of publication charge


As day of June 1, 2021 (based on date of article submission), article processing charges (APC) will be applied to papers accepted after peer review as follows:
 

Author APC Remark
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Non-member in Korea 1,200,000 KR won
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Special Issue: 67th ICoMST 2021


The 67th International Congress of Meat Science and Technology (ICoMST) is currently accepting abstracts.

The deadline for submitting abstracts has been extended to April 15th, 2021, and FSAR will apply a 50% publishing fee discount to this special issue.

For more information, please check the website below.

https://www.icomst2021.com/

 


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