FOOD SCIENCE ›› 2019, Vol. 40 ›› Issue (7): 1-8.doi: 10.7506/spkx1002-6630-20180316-217

• Basic Research •     Next Articles

Prediction of Pseudomonas aeruginosa Growth in Milk

ZHU Lei, ZHANG Aijing, WANG Pengjie, XIA Fan, DONG Yue, GAO Yulong   

  1. Collaborative Innovation Center for Modern Grain Circulation and Safety, Key Laboratory of Grains and Oils Quality Control and Processing, College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
  • Online:2019-04-15 Published:2019-05-05

Abstract: The relationship between the parameters of growth models for foodborne pathogenic Pseudomonas aeruginosa ATCC27853 in milk and temperature was investigated. P. aeruginosa ATCC27853 was inoculated into fresh sterilized milk samples and incubated at 6, 10, 16, 22, 28, 36, 42, 45, 48 and 50 ℃, respectively. The Gompertz equation was used as primary model to fit the growth data of P. aeruginosa by Matlab software. Maximum specific growth rate (μmax) and lag phase duration (λ) obtained from the Gompertz model were fitted to secondary models, modified Ratkowsky and Hyperbola models, respectively. The primary model and secondary models were evaluated and validated with respect to determination coefficient, root mean square error, accuracy factor and bias factor. The results showed that the Gompertz model was fitted well to the experimental data and could well predict the growth of P. aeruginosa ATCC27853 in milk at different temperatures. Both secondary models had excellent goodness of fit to the experimental data. The initial physiological state parameter (h0) of P. aeruginosa ATCC27853 at 6, 10 and 48 ℃ were significantly higher than at 16–45 ℃. The models, indicating the effects of growth temperatures on μmax, λ and h0, can predict the growth of P. aeruginosa ATCC27853 in milk during its processing, transportation, storage and distribution, and therefore provide a theoretical basis for microbial safety control of milk.

Key words: Pseudomonas aeruginosa, Gompertz model, modified Ratkowsky model, Hyperbola model, prediction

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