摘要
目的探索木质肉(woody/wooden breast,WB)生肉品质特性差异及客观检测方法。方法对不同木质鸡胸肉等级样本的重量、挤压力(CF Probe、CF TA-25)进行检测,同时在剪切实验中以剪切力(MORSF)、剪切做功(MORSE)及剪切峰值个数(PC-MORS)作为生肉质构分析指标。结果重量、CF Probe、CF TA-25、MORSF、MORSE、PC-MORS随着WB等级的递增而显著增大,2种硬度检测探头均可用于鸡胸肉的硬度检测。PC-MORS在MORSF和MORSE的基础上可有效区分WB等级,同时可显示鸡胸肉肌肉内部的层状结构信息。各测量指标之间存在不同程度的相关性,其中重量、CF Probe、MORSE、PC-MORS与WB等级之间存在极显著相关。建立的BP神经网络模型区分正常肉和木质肉的总体识别率达90.6%。结论本研究确定了木质鸡胸肉生肉硬度检测方法、质构分析的特征参数,并研究其木质化等级判定模型,研究结果可为后续木质鸡胸肉在线检测分级装置的研发提供一定的理论支撑。
Objective To explore the differences in the quality characteristics of raw wooden meat(WB)and objective detection methods.Methods Fillet weight was determined and compression force(CF)of cranial region were conducted using Probe and TA-25 respectively,then shear values(MORSF,MORSE and PC-MORS)were measured.Results Fillet weight,CF Probe,CF TA-25,MORSF,MORSE,PC-MORS were increased with the increase of WB category,2 kinds of hardness probes could be used to detect the hardness of chicken breast.Raw breast texture analysis was useful in distinguishing WB,and PC-MORS could be a good characteristic feature to describe inner muscle texture in raw fillets associated with woody condition.There were different degrees of correlation among the meat quality traits,and fillet weight,CF Probe,MORSE,PC-MORS were extremely significant correlated to WB score.The overall recognition accuracy rate of the developed BP neural network model was 90.6%.Conclusion The characteristic parameters of hardness test method and texture analysis are determined,and the evaluation model of lignification grade is studied.The research results can provide a theoretical support for the development of on-line detection and grading device of ligneous chicken breast.
作者
孙啸
谢葛亮
束婧婷
刘一帆
Casey M.Owens
陈坤杰
SUN Xiao;XIE Ge-Liang;SHU Jing-Ting;LIU Yi-Fan;Casey M.Owens;CHEN Kun-Jie(School of Biological Science and Food Engineering,Chuzhou University,Chuzhou 239000,China;Key Laboratory for Poultry Genetics and Breeding of Jiangsu Province,Jiangsu Institute of Poultry Science,Yangzhou 225125,China;Poultry Science Department,University of Arkansas,Fayetteville 72701,USA;College of Engineering,Nanjing Agricultural University,Nanjing 210031,China)
出处
《食品安全质量检测学报》
CAS
2020年第3期745-751,共7页
Journal of Food Safety and Quality
基金
江苏省家禽遗传育种重点实验室开放课题(JQLAB-KF-201901)
滁州学院科研启动基金项目(2017qd01)
滁州学院省级大学生创新训练项目(S20190377059,S20190377113).
关键词
木质肉
生肉肉质
挤压力
剪切指标
BP神经网络
分级
woody breast
raw meat quality
compression force
shearing
back-propagation neural network
classification
