Application of interaction models in predicting the simultaneous growth of Staphylococcus aureus and different concentrations of background microbiota in Chinese-style braised beef. (June 2023)

• Record Type: Journal Article
• Authors: Cheng, Chuansong; Liu, Binxiong; Tian, Meiling; Fang, Ting; Li, Changcheng
• Is Part Of: Meat science. Volume 200(2023)
• Publication Date: 2023-06
• Subjects: Staphylococcus aureus -- Background microbiota -- Predictive model -- Microbial interaction -- One-step kinetic analysis -- Chinese-style braised beef; Meat -- Periodicals; Meat industry and trade -- Periodicals; Viande -- Périodiques; Viande -- Industrie -- Périodiques; Meat; Meat industry and trade; Periodicals; 641.36
• DOI: 10.1016/j.meatsci.2023.109162
• Languages: English
• ISSNs: 0309-1740

Abstract

Abstract: This study aimed to investigate the growth kinetics of S. aureus and different concentrations of background microbiota in Chinese-style braised beef (CBB). A one-step analysis method was applied to develop predictive model to describe the simultaneous growth and interaction of S. aureus with different concentrations of background microbiota in CBB. The results show that a one-step method successfully models the growth of S. aureus and background microbiota in CBB and the competing interactions between the two. In sterile CBB, the estimated minimum growth temperatures ( T min, S ) and the maximum growth concentrations ( Y max, S ) were 8.76 °C and 9.58 log CFU/g for S. aureus . Under competition, the growth of background microbiota was not affected by S. aureus, the estimated T min, B and Y max, B was 4.46 °C and 9.94 log CFU/g. The background microbiota in CBB did not affect the growth rate of S. aureus ( α 1 = 1.04), but had an inhibitory effect on the number of S. aureus ( α 2 = 0.69) at the later growth stage. The Root Mean Square Error ( RMSE ) of the modeling data was 0.34 log CFU/g, with 85.5% of the residual errors within ±0.5 log CFU/g of experimental observations. The one-step analysis and dynamic temperatures (8 °C–32 °C) verification indicated that the RMSE of prediction was <0.5 log CFU/g for both S. aureus and background microbiota. This study demonstrates that microbial interaction models are a useful and promising tool for predicting and evaluating the spatiotemporal population dynamics of S. aureus and background microbiota in CBB products. Highlights: A growth prediction model for S. aureus in sterile CBB was developed. Interaction models of S. aureus and background microbiota in CBB were developed. A one-step analysis method was used to estimate kinetic parameters. The applicability and accuracy of the model were verified under dynamic temperature.