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Liu S.,National Engineering Research Center for Agricultural Product in Modern Logistics | Liu S.,Beijing Research Center for Information Technology in Agriculture | Zhao C.,National Engineering Research Center for Agricultural Product in Modern Logistics | Zhao C.,Beijing Research Center for Information Technology in Agriculture | And 4 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2014

In order to scientifically monitor sensory quality changes of chilled pork during cold chain logistics, the sensory characteristics of pork samples from distribution center, short-distance supermarket and long-distance supermarket during the progress of cold chain logistics were investigated, including odor, color, springiness, cooking odor and overall acceptability of pork samples. The distribution and the probability of different sensory indicators scores were studied by normality test. The key evaluation indicators was obtained by using sensitivity analysis and regression analysis, then according to its data distribution characteristics and its diagnostic value to overall acceptability, the control charts were designed to visualize the fluctuation of sensory characteristics distinctly. The results showed that: overall, there were significant difference in the sensory quality of pork samples from distribution centers, short-distance supermarket and long-distance supermarket; color is the sensitive indicator to evaluate overall acceptability, and its mean-standard deviation control chart were designed; there were more sensory quality fluctuations in distribution center, and its process capability index was lower, thus it was need to improve quality management. The product quality fluctuation and its qualified ratio were distinctly visualized by mean-standard deviation control chart, and it would provide scientific management method for monitoring pork appearance changes in cold chain logistics. Source


Liu S.,Chinese National Engineering Research Center for Information Technology in Agriculture | Liu S.,National Engineering Research Center for Agricultural Product in Modern Logistics | Zhao C.,Chinese National Engineering Research Center for Information Technology in Agriculture | Zhao C.,National Engineering Research Center for Agricultural Product in Modern Logistics | And 3 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

The quantity changes of major spoilage microflora (total viable counts, Pseudomonas sp. and Brochothrix thermosphacta) and pathogen microflora (Coliform bacteria and Listeria sp.) during the cold chain logistics were investigated by selective medium, to evaluate the quality and safety of chilled pork by monitoring the microbial growth. The mean-range control chart of microbial contamination of chilled pork during the logistics chain was designed using the statistical process control technology, to analyze their quantity fluctuation and the ratio of eligible quality. The results showed that the microbial quantity (except Coliform bacteria) of pork samples increased significantly (P<0.05) with the extension of logistics chain, and the quantity of pork samples in supermarket were higher (P<0.05) than those in slaughterhouse; The microbial contamination of pork samples in the supermarket from external city was higher (P<0.05) than those from local city. The microbial quantity distribution of chilled pork during the logistics chain was studied by normality test, and it was found that the quantity of Coliform bacteria and Brochothrix thermosphacta showed normal distribution, but Pseudomonas sp. and Listeria sp. did not fit normal distribution. The mean-range control chart of total viable counts and Coliform bacteria of chilled pork during the logistics chain was designed using statistical process control technology, according to the microbial safety limits of chilled pork in the national standard of the People's Republic of China. As showed in the control charts, there were significantly differents in the process capability index (CPk) and the ratio of quality eligibility calculated by different control limits from zero to alerting limit, from zero to maximum limit, and from lower control limit (LUL) to upper control limit (UCL). However, the total viable counts in all pork samples were eligible according to the national microbial quantity limits (1×106cfu/g). Mean-range control chart can be used to show intuitively the abnormal fluctuations and substandard quality of microorganism, and it is not yet or seldom applied to the microbial contamination of pork in the cold chain logistics. The research is meaningful to provide a scientific method in pork quality monitoring and microbial control in the cold chain logistics. Source

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