1398 微生物学通报 2009, Vol.36, No.9 ) [2], 25% [3],,,,, ,,,,, 1 预报微生物学 1.1 预报微生物学概况 (Predictive Microbiology) [4] 80,,, 1.2 预报微生物学模型的建立与研究现状,,,

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1 微生物学通报 SEP 20, 2009, 36(9): 1397~1403 Microbiology 2009 by Institute of Microbiology, CAS 专论与综述预报微生物学在食品安全风险评估中的作用 * 胡洁云欧杰李柏林 ( ) 摘要 : 随着中国食品工业的发展, 食品安全问题日益凸显, 建立一种准确及时的食品安全风险评估是产品市场对食品安全体系提出的挑战预报微生物学是食品安全风险评估的核心预警技术, 依据建立的预报微生物学模型, 可快速地对食品中的致病菌和腐败菌生长情况进行判断, 对食品中病原微生物和腐败微生物的控制有重要的意义本文概述了预报微生物学模型的建立和研究现状, 探讨预报微生物学在食品安全风险评估中的应用现状, 概述了预报微生物学模型在食品安全风险评估应用中的发展前景关键词 : 预报微生物学, 预报模型, 食品安全风险评估 The Role of Predictive Microbiology in Food Safety Risk Assessment HU Jie-Yun OU Jie * LI Bai-Lin (College of Food Science and Technology, Shanghai Ocean University, Shanghai , China) Abstract: With the development of the food industry in China, it has been found that food safety is becoming the biggest issue in the food manufacture and logistics. Accurate and timely to establish a risk assessment method in produce market is the challenge for food safety system. Predictive microbiology is a core early warning technology in the food safety risk assessment. According to the microorganism predicting model, the pathogen and spoilage microorganism s growth in food can be fast judgment in advance. And it plays an important part in controlling the growth of pathogen and the spoilage microorganism in food. This paper summarized the predictive microbiology model s establishment and the present research situation, and discussed the present situation and application of predictive microbiology in food safety risk assessment. The future trend of predictive microbiology in food safety risk assessment was prospected as well. Keywords: Predictive microbiology, Predicting model, Food safety risk assessment,,, [1],, ( O157: H7 基金项目 : (No. 2007BAD52B05) * 通讯作者 :Tel: ; : jou@shou.edu.cn 收稿日期 : ; 接受日期 :

2 1398 微生物学通报 2009, Vol.36, No.9 ) [2], 25% [3],,,,, ,,,,, 1 预报微生物学 1.1 预报微生物学概况 (Predictive Microbiology) [4] 80,,, 1.2 预报微生物学模型的建立与研究现状,,, 19, (TDT) Z F D,,, Buchanan Whiting [5] 初级模型 (Primary model): 1825, Gompertz, S, Gibson [6] Gompertz,, S,, : LogN=A+C/{1+exp [ B(t M )]} N ; A ; C (); M ; B, (Linear model) (Logistic function) Gompertz Baranyi & Roberts ; Logistic Gompertz, Gompertz,, Gibson Zwietering [7] :, Gompertz S, Gomeprtz 3, Buchanan RL [8] (Three phase linear model) Branyi Gompertz Escherichia coli O157:H7, Gompertz 二级模型 (Secondary model):

3 : 1399,, ( ph ) 3 : (Response surface equation) (Square root model) Arrhenius [9] Escherichia coli O157:H7, ph NaCl Raktowksy [10],,,, ;, ;,,,,,,, [11] :, Smith [12], ph Lindroth [13], 三级模型 (Tertiary model):,,,,, : ; ;, [14], 1988, Food MicroModel, Food Micro- Model, PMP(Pathogen modelling program) ( , 2, ComBase(Combined database) [15] ,, ComBase 2006, 2 ComBase, ComBase Predictor 25000, ( MKES (Microbial kinetics expert system) FMM (Food MicroModel) (Seafood spoilage and safety predictor, SSSP), 1999 (Seafood spoilage predictor, SSP, dfu.min.dk) (Relative rate of spoilage, RRS) (Microbial spoilage, MS), 2 预报微生物学与食品安全风险评估的关系,,

4 1400 微生物学通报 2009, Vol.36, No.9,,,, (Risk assessment), [16] 20 80,,,,,, 1998, O157: H7, Haas CN Rose JB [17] O157: H7 -,,, [18],,,,,,,, 4, 1) () ; 2) ; 3) ; 4) [19],,,, 3 微生物风险评估在预测水产品安全中的应用,,,, 20 60,, Spencer [20] Olley [21],, Daud [22] Olley 90, Dalgaard [23],, Dalgaard [23,24]

5 : 1401 ;,, (Pseudomonas spp.) (Shewanella putrefaciens) (Photobacterium phosphoreum), FSP(Food spoilage predictor) SSP(Seafood spoilage predictor) [25,26],,, Tasmania (FSP, Foods spoilage predictor), [27], (Pseudomonas putida)1442, Aw ph 800, 500,,, (Accuracy factor, Af),,, (Bias) 20% FSP, [28], FSLP(Fish shelf life predictor); Lalitha KV Surendran PK [29], [30], 4 研究前景与展望,,,, : 1),,,, 2), 3),,, 4), : (Quorum sensing),,

6 1402 微生物学通报 2009, Vol.36, No.9 5),, 6) PCR-DGGE, 拮抗,,,, [26],, 参考文献 [1].., 2008, 29(10): [2].., 2008, 7(7): [3] Baird-Parker TC. The producyion of microbiologically safe and stable foods. The Microbiological Safety and Quslity of Food, Gaithersburg Maryland USA: Aspen Publishers Inc, 2000, 3(9): [4] McMeekin TA, Olley J, Ratkowsky DA, et al. Predictive Microbiology: towards the interface and beyond. International Journal of Food Microbiology, 2002, 73(2): [5] Whiting RC, Buchanan RL. A classification of models for predictive microbiology. Food Microbiology, 1993, 10(12): [6] Gibson AM, Bratchell N, Roberts TA. The effect of sodium chloride and storage temperature on the rate and extent of growth of Clostridium botulinum type A in pasteurized pork slurry. J Appl Bacteriol, 1987, 62(6): [7] Zwietering MH, Jongenburger I, Rombouts FM. Modeling of the bacterial growth curve. Applied and Environmental Microbiology, 1990, 56(6): [8] Buchanan, Whiting, Damert. When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves. Food Microbiology, 1997, 14(44): [9],,,. Escherichia coli O157:H7., 2006, 10(10): [10] Ratkowsky DA, Olley J, McMeekin TA, et al. Relationship between temperature and growth rate of bacterial cultures. Journal of Bacteriology, 1982, 149(1): 1 5. [11],,,.., 2005, 25(11): [12] Smith MG. Use of response surface methodology in shelf life extension studies of a bakery product. Food Microbiol, 1988, 5(3): [13] Lindroth SE, Genigeorgis CA. Probability of growth and toxin production by nonproteolytic Clostridium botulism in rockfish stored under modified atmosphere. International Journal of Food Microbiology, 1987, 4(2): [14],,.., 2004, 2(1): 5 8. [15] New microbiology database and predictive models. ( ). [16] FAO Fisheries Technical Paper. International Regulatory Framework for Fish Safety and Quality. No.442 Rome, FAO [17] Haas, Rose, Gerba. Development of a dose-response relationship for Escherichia coli O157:H7. International Journal of Food Microbiology, 2000, 56(4): [18] McMeekin. Predictive microbiology: Quantitative science delivering quantifiable benefits to the meat industry and other food industries. Meat Science, 2007, 77(11): [19],,,.., 2006, 1(6): [20] Spencer R, Bains CR. The effect of temperature on the spoilage of wet fish. I. Storage at constant temperature between 21 C and 25 C. Food Microbial, 1964, 18(4): [21] Olley J, Ratkowsky DA. The role of temperature function integration in monitoring fish spoilage. Food Techno, 1973, 8(10): [22] Daud HB, McMeekin TA, Olley J. Temperature function

7 : 1403 integration and the development and metabolism of poultry spoilage bacteria. Appl Environ Microbiol, 1978, 36(14): [23] Dalgaard P. Qualitative and quantitative characterization of spoilage bacteria from packed fish. International Journal of Food Microbiology, 1995, 26(6): [24] Dalgaard P. Modelling of microbial activity and prediction of shelf of packed fish. International Journal of Food Microbiology, 1995, 19(2): [25] Dalgaard, Buch, Silberg. Seafood spoilage predictor-development and distribution of a product specific application software. International Journal of Food Microbiology, 2002, 73(11): [26],,,.., 2008, 35(4): [27] Thomas, Meekin. Shelf life prediction: status and future possibilities. International Journal of Food Microbiology, 1996, 33(7): [28],,,.., 2005, 29(4): [29] Lalitha KV, Surendran PK. Microbiological changes in farm reared fresh water prawn in ice. Food Control, 2006, 17(1): [30],,,.., 2007, 28(9): 腐植酸杂志 2010 年征订启事征订启事 1979 ( ) ( ) ( ) ISSN CN /TQ () chaia@126.com

590 微生物学通报 2008, Vol.35, No.4,, 1 模型介绍, ; ;,, [4] Whiting Buchanan(1993) [5] Isabelle Andre(2006) [6], 1 Primary models 表 1 模型的分类 Table 1 Classificati

590 微生物学通报 2008, Vol.35, No.4,, 1 模型介绍, ; ;,, [4] Whiting Buchanan(1993) [5] Isabelle Andre(2006) [6], 1 Primary models 表 1 模型的分类 Table 1 Classificati 微生物学通报 APR 20, 2008, 35(4): 589~594 Microbiology tongbao@im.ac.cn 2008 by Institute of Microbiology, CAS 专论与综述食品微生物生长预测模型研究新进展周康 1 刘寿春 1 李平兰 1* 马长伟 1 彭朝辉 2 (1. 100083) (2. 100000) 摘要 : 为了更好的了解食品微生物学预测模型的基本内容,