Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

Author:

Xu Zeyu1234ORCID,Han Yu1234,Zhao Dianbo1234,Li Ke1234,Li Junguang1234,Dong Junyi123,Shi Wenbo123,Zhao Huijuan5,Bai Yanhong1234ORCID

Affiliation:

1. College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China

2. Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China

3. Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China

4. Food Laboratory of Zhongyuan, Luohe 462000, China

5. Henan Lianduoduo Supply Chain Management Co., Ltd., Hebi 458000, China

Abstract

Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat.

Funder

major science and technology project in Henan province

doctoral program of Zhengzhou University of Light Industry

project of Food Laboratory of Zhongyuan

Publisher

MDPI AG

Reference60 articles.

1. Xia, K.X. (2022). Study on Food Safety Behavior of Meat Traders in Farmers’ Market and Its Influencing Factors—Based on a Survey in Shanghai and Wuhan. [Master’s Thesis, Shanghai University of Finance and Economics].

2. Application of Information Fusion Related to Electronic Nose and Electronic Tongue;Zhu;Mod. Food,2020

3. Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy;Xu;J. Food Eng.,2019

4. Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms;Pinto;Livest. Sci.,2023

5. White striping degree assessment using computer vision system and consumer acceptance test;Kato;Asian-Australas. J. Anim. Sci.,2019

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