摘要
【目的】为了研究货架期内真空包装牛肉贮藏时间与肉品质之间的关系.【方法】试验分别测定了真空包装牛肉4℃贮藏0、1、3、9、15、21d时的pH值、肉色、挥发性盐基氮(TVB-N)值、过氧化值(POV)、菌落总数和质构特性,并对所测指标进行主成分分析.【结果】随贮藏时间延长,牛肉品质逐渐降低,由主成分分析提取出的两个主成分因子PC1(67.316%)、PC2(28.598%)计算的综合主成分值呈下降趋势,且一般在-3.435~1.551;根据PC1因子载荷,贮藏0~9d和9~21d真空包装牛肉的差异与牛肉的b*值、TVB-N值、POV、菌落总数、弹性、内聚性和咀嚼性密切相关,PC2表明贮藏3~15d与其他贮藏时间段牛肉品质差异主要表现在pH值、L*值、a*值及硬度;15份样品验证数据证明采用主成分值评估真空包装牛肉已贮藏时间准确率达到86.67%.【结论】将主成分分析法应用于真空包装牛肉品质评定具有一定的应用价值.
[Objective] In order to investigate the relationship between storage time and meat quality of vacuum packaged beef during shelf life. [Method] The vacuum packaged beef was assessed by measuring its pH value, color, total volatile basic nitrogen (TVB-N), peroxide value (POV), total bacterial counts, and the textures indexes at 0,1,3,9,15,21 days during storage at 4 ℃, respectively. After that, the measured indexes were analyzed by using principal component analysis. [Result] Two principal component factors were extracted with PC1 of 67. 316% and PC2 of 28. 598%,while a gradually decrease in quality traits and a decreased trend in the comprehensive principal component value of the vacuum packed beef samples was found during the storage. The comprehensive principal component value generally ranged from -3. 435 to 1. 551. guishe ones stored at 9-21 days distin- value, TVB-N value, POV, total bacterial counts,springiness, cohesiveness and chewiness. The factor PC2 indicated that the quality of the beef stored at 3-15 days was mainly expressed in pH value,L* value,a* value and hardness. The valida- tion data obtained from 15 samples proved the detection accuracy rate of storage time of vacuum packed beef by using principal component value assessment reached 86. 67%. [Conclusion] It is suggested that principal component analysis could be used for evaluating the quality of vacuum packaged beef.
出处
《甘肃农业大学学报》
CAS
CSCD
北大核心
2017年第1期126-131,138,共7页
Journal of Gansu Agricultural University
基金
兰州市科技局资助项目(2009-1-142)
国家自然科学基金项目(31460402)
国家现代农业产业技术体系项目(CARS-38)
关键词
真空包装
牛肉
品质
质地剖面分析
主成分分析
vacuum package
beef
quality
texture profile analysis
principal component analysis