Prediction of the intramuscular fat content of pork cuts by improved U2-Net model and clustering algorithm  

作　　者：Hu Liu Wei Zhan Zhiqiang Du Mengyuan Xiong Tao Han Peiwen Wang Weihao Li Yong Sun 

机构地区：[1]School of Computer Science,Yangtze University,Jingzhou,434023,China [2]School of Animal Science,Yangtze University,Jingzhou,434025,China

出　　处：《Food Bioscience》2023年第3期3136-3147,共12页食品生物科学（英文）

基　　金：supported by the fund of National Natural Science Foundation of China(62276032);China University Industry-University-Research Innovation Fund“New Generation Information Technology Innovation Project”(2020ITA03012);Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing(KLIGIP-2021A07);the National Natural Science Foundation of China(31772206,31972274);Joint research on improved livestock and poultry breeds in Anhui Province(2021-2025);the 2020 Jingzhou Science and Technology Development Plan Project.

摘　　要：The intramuscular fat content is an essential indicator of pork quality,directly affecting sensory quality and consumers’willingness to buy.Traditional testing methods are subjective and destructive,their assessment is scored by trained assessors according to a marbling scale,but the human sensory evaluation has great subjectivity and randomness.Nowadays,there are more methods to predict the intramuscular fat content of livestock meat using computer vision techniques.However,the complex background of the image makes it difficult to segment the target and background.This study proposes a method based on semantic segmentation networks and machine learning algorithms to detect the intramuscular fat content of multi-part pork cuts.The images of five different pork cuts(belly,loin,collar,ham,and hock)are used as experimental data in the study,the results show that the method proposed in this paper can objectively detect the intramuscular fat content of pork and the average accuracy of prediction can reach 93.28%.This new information method can be used to assess the quality of pork in markets and food processing plants,enabling processors and consumers to distinguish pork cuts with different fat content.It improves the quality of pork to meet the needs of the food processing industry.

关 键 词：Multi-part pork cuts Predicting pork intramuscular fat Semantic segmentation Machine learning 

分 类 号：TS251[轻工技术与工程—农产品加工及贮藏工程]