Login / Signup

Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork.

Ge ZhaoTengteng YangHuimin ChengLin WangYunzhe LiuYubin GaoJianmei ZhaoNa LiuXiumei HuangJunhui LiuXiyue ZhangYing XuJun WangJunwei Wang
Published in: Molecules (Basel, Switzerland) (2022)
To better guide microbial risk management and control, growth kinetic models of Salmonella with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Salmonella Derby ( S. Derby), Pseudomonas aeruginosa ( P. aeruginosa ), and Escherichia coli ( E. coli ), and incubated at various temperatures (4-37 °C). The predictive growth models were developed based on the observed growth data. By comparing R 2 of primary models, Baranyi models were preferred to fit the growth curves of S. Derby and P. aeruginosa , while the Huang model was preferred for E. coli (all R 2 ≥ 0.997). The secondary Ratkowsky square root model can well describe the relationship between temperature and μ max (all R 2 ≥ 0.97) or Lag (all R 2 ≥ 0.98). Growth models were validated by the actual test values, with B f and A f close to 1, and MSE around 0.001. The time for S. Derby to reach a pathogenic dose (10 5 CFU/g) at each temperature in pork was predicted accordingly and found to be earlier than the time when the pork began to be judged nearly fresh according to the sensory indicators. Therefore, the predictive microbiology model can be applied to more accurately predict the shelf life of pork to secure its quality and safety.
Keyphrases
  • escherichia coli
  • pseudomonas aeruginosa
  • cystic fibrosis
  • machine learning
  • microbial community
  • biofilm formation
  • listeria monocytogenes
  • artificial intelligence
  • deep learning
  • klebsiella pneumoniae
  • candida albicans