Science and Technology of Food Industry

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Volume 42 Issue 12
Jun.  2021
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CHEN Yu, LIANG Ying, ZHOU Pingping, et al. Establishment of Growth Prediction Model of Escherichia coli in Imported Fresh Beef at Different Temperatures [J]. Science and Technology of Food Industry, 2021, 42(12): 81−88. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020110003.
Citation: CHEN Yu, LIANG Ying, ZHOU Pingping, et al. Establishment of Growth Prediction Model of Escherichia coli in Imported Fresh Beef at Different Temperatures [J]. Science and Technology of Food Industry, 2021, 42(12): 81−88. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020110003.
shu

Establishment of Growth Prediction Model of Escherichia coli in Imported Fresh Beef at Different Temperatures

  • 1.

    International Laboratory of Animal Health and Food Safety, Nanjing Agricultural University, Nanjing 210095, China

  • 2.

    National Food Safety Risk Assessment Center, Beijing 100022, China

  • 3.

    Shanghai Customs Animal, Plant And Food Testing Center, Shanghai 200135, China

  • 4.

    China Pharmaceutical University, Nanjing 211199, China

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  • Received Date: November 02, 2020
  • Available Online: April 20, 2021
    Graphical Abstract
    • Abstract
  • In order to establish the growth prediction model of Escherichia coli O157:H7 in imported fresh beef at different temperatures. Fresh imported beef purchased from supermarkets and Escherichia coli O157:H7 were selected as the research objects. The growth data of Escherichia coli in imported fresh beef stored at 4, 16, 25, 30 and 37 ℃ were monitored and the growth curve was drawn. Four models of modified Gompertz, Logistic, Richards and MMF were used to fit the growth curve, and the first-order model of Escherichia coli in imported fresh beef was established. The data fitted by the first-order model was substituted into Ratkowsky equation to establish the second-order model. The accuracy of the model was tested by the accuracy factor, bias factor and root mean square error. Results showed that, the correlation coefficients of the four models were all above 0.98. The data of modified Gompertz model showed that the fitting degree of the modified Gompertz model was the best, and it was most suitable to predict the growth dynamics of E.coli in imported fresh beef. The accuracy factors of the models were 0.99, the bias factors were 1.14 and 1.03, and the determination coefficients R2 were 0.97 and 0.99, indicating that the established model was highly reliable. The growth prediction model established in this study could effectively predict the growth of Escherichia coliO157:H7 in imported fresh beef at 4~37 ℃.
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    • References (31)
  • [1]
    Bibbal D, Loukiadis E, Kérourédan M, et al. Prevalence of carriage of shiga toxin-producing Escherichia coli serotypes O157: H7, O26: H11, O103: H2, O111: H8, and O145: H28 among slaughtered adult cattle in france[J]. Applied and Environmental Microbiology,2015,81(4):1397−1405. doi: 10.1128/AEM.03315-14
    [2]
    李军, 禤雄标, 谢宇舟, 等. 广西畜禽大肠杆菌O157∶H7流行病学调查[J]. 中国人兽共患病学报,2011,27(12):1151−1155. doi: 10.3969/j.issn.1002-2694.2011.12.021
    [3]
    Jang J, Hur H G, Sadowsky M J, et al. Environmental Escherichia coli: Ecology and public health implications-a review[J]. J Appl Microbiol,2017,123(3):570−581. doi: 10.1111/jam.13468
    [4]
    Farrokh C, Jordan K, Auvray F, et al. Review of Shiga-toxin-producing Escherichia coli (STEC) and their significance in dairy production[J]. International Journal of Food Microbiology,2013,162(2):190−212. doi: 10.1016/j.ijfoodmicro.2012.08.008
    [5]
    Lim J Y, Yoon J, Hovde C J. A brief overview of Escherichia coli O157: H7 and its plasmid O157[J]. Journal of Microbiology and Biotechnology,2010,20(1):5. doi: 10.4014/jmb.0908.08007
    [6]
    Saxena T, Kaushik P, Krishna M M. Prevalence of E. coli O157: H7 in water sources: an overview on associated diseases, outbreaks and detection methods[J]. Diagn Microbiol Infect Dis,2015,82(3):249−264. doi: 10.1016/j.diagmicrobio.2015.03.015
    [7]
    邓秀武, 高亚娟, 司海丰, 等. 食源性疾病现状及监控技术的研究[J]. 食品安全质量检测学报,2016,7(6):2235−2239.
    [8]
    白凤翎, 李鸿雁. 肉品微生物污染现状分析与安全对策[J]. 肉类工业,2007(12):36−38. doi: 10.3969/j.issn.1008-5467.2007.12.016
    [9]
    龚玲芬, 殷俊, 刘萍, 等. 2016-2018年无锡市食源性疾病监测结果[J]. 职业与健康,2019,35(21):2944−2947.
    [10]
    李鹏. 2018年酒泉市部分地区市售牛羊肉食源性致病菌污染情况调查与分析[J]. 中国草食动物科学,2020,40(5):53−55. doi: 10.3969/j.issn.2095-3887.2020.05.011
    [11]
    郑向梅, 王建平, 高景枝, 等. 十堰市2000~2006年食源性致病菌监测分析[J]. 中国卫生检验杂志,2007(7):1255−1257. doi: 10.3969/j.issn.1004-8685.2007.07.046
    [12]
    李蕙, 孙晓岩, 郭素英, 等. 2015-2018年营口市食品安全风险微生物检测结果分析[J]. 社区医学杂志,2019,17(19):1181−1184.
    [13]
    毛书奇, 胡群雄, 徐明敏, 等. 2017年宁波市北仑区熟肉制品风险监测结果分析[J]. 河南预防医学杂志,2019,30(10):801−802.
    [14]
    梅国勇, 徐彦, 姚孝明, 等. 2016-2017年南京市栖霞区食源性疾病流行病学现状及监测结果分析[J]. 环境卫生学杂志,2019,9(2):113−117.
    [15]
    罗伟倩, 潘伟芬, 黄群娣. 增城区2016~2017年食品安全风险监测微生物检测结果分析[J]. 食品安全导刊,2018(25):67−69. doi: 10.3969/j.issn.1674-0270.2018.25.028
    [16]
    Mcmeekin T, Bowman J, Mcquestin O, et al. The future of predictive microbiology: Strategic research, innovative applications and great expectations[J]. International Journal of Food Microbiology,2008,128(1):2−9. doi: 10.1016/j.ijfoodmicro.2008.06.026
    [17]
    唐佳妮, 张爱萍, 刘东红. 预测微生物学的研究进展及其在食品中的应用[J]. 中国食品学报,2010,10(6):162−166. doi: 10.3969/j.issn.1009-7848.2010.06.027
    [18]
    Havelaar A H, Brul S, de Jong A, et al. Future challenges to microbial food safety[J]. International Journal of Food Microbiology,2010,139:S79−S94. doi: 10.1016/j.ijfoodmicro.2009.10.015
    [19]
    Ratkowsky D A, Lowry R K, Mcmeekin T A, et al. Model for bacterial culture growth rate throughout the entire biokinetic temperature range[J]. J Bacteriol,1983,154(3):1222−1226. doi: 10.1128/JB.154.3.1222-1226.1983
    [20]
    Tenaillon O, Barrick J E, Ribeck N, et al. Tempo and mode of genome evolution in a 50, 000-generation experiment[J]. Nature,2016,536(7615):165−170. doi: 10.1038/nature18959
    [21]
    Whitman R L, Shively D A, Pawlik H, et al. Occurrence of Escherichia coli and enterococci in Cladophora (Chlorophyta) in nearshore water and beach sand of Lake Michigan[J]. Appl Environ Microbiol,2003,69(8):4714−4719. doi: 10.1128/AEM.69.8.4714-4719.2003
    [22]
    Duffy G, Cummins E, Nally P, et al. A review of quantitative microbial risk assessment in the management of Escherichia coli O157: H7 on beef[J]. Meat Science,2006,74(1):76−88. doi: 10.1016/j.meatsci.2006.04.011
    [23]
    Yu H H, Song Y J, Kim Y J, et al. Predictive model of growth kinetics for Staphylococcus aureus in raw beef under various packaging systems[J]. Meat Science,2020,165:108108. doi: 10.1016/j.meatsci.2020.108108
    [24]
    Li M Y, Sun X M, Zhao G M, et al. Comparison of mathematical models of lactic acid bacteria growth in vacuum-packaged raw beef stored at different temperatures[J]. J Food Sci,2013,78(4):M600−M604. doi: 10.1111/j.1750-3841.2012.02995.x
    [25]
    Juneja V K, Melendres M V, Huang L, et al. Mathematical modeling of growth of Salmonella in raw ground beef under isothermal conditions from 10 to 45 °C[J]. International Journal of Food Microbiology,2009,131(2-3):106−111. doi: 10.1016/j.ijfoodmicro.2009.01.034
    [26]
    金鑫, 禹迎迎, 吴菊清, 等. 大肠杆菌和假单胞菌在猪背最长肌上混合预测模型的建立[J]. 食品科学,2013,34(17):87−91. doi: 10.7506/spkx1002-6630-201317020
    [27]
    赵格, 宋雪, 赵建梅, 等. 鸡蛋中常见食源性致病菌的生长预测模型研究[J]. 中国动物检疫,2016,33(6):68−71. doi: 10.3969/j.issn.1005-944X.2016.06.022
    [28]
    Posada-Izquierdo G, Del R S, Valero A, et al. Assessing the growth of Escherichia coli O157: H7 and Salmonella in spinach, lettuce, parsley and chard extracts at different storage temperatures[J]. J Appl Microbiol,2016,120(6):1701−1710. doi: 10.1111/jam.13122
    [29]
    Mishra A, Guo M, Buchanan R L, et al. Prediction of Escherichia coli O157: H7, Salmonella, and Listeria monocytogenes growth in leafy greens without temperature control[J]. J Food Prot,2017,80(1):68−73. doi: 10.4315/0362-028X.JFP-16-153
    [30]
    Wang D, Huber A, Dunfield K, et al. Comparative persistence of Salmonella and Escherichia coli O157: H7 in loam or sandy loam soil amended with bovine or swine manure[J]. Canadian Journal of Microbiology,2018,64(12):979−991. doi: 10.1139/cjm-2018-0234
    [31]
    Gullian-Klanian M, Sánchez-Solis M J. Growth kinetics of Escherichia coli O157: H7 on the epicarp of fresh vegetables and fruits[J]. Brazilian Journal of Microbiology,2018,49(1):104−111. doi: 10.1016/j.bjm.2017.08.001
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