Growth and survival ofStaphylococcus aureuson beef jerky as a function of temperature

Attachment characteristics and kinetics of biofilm formation by Staphylococcus aureus on ready-to-eat cooked beef contact surfaces

Staphylococcus aureus is a food-borne pathogen that quickly forms biofilm on meat contact surfaces and thus poses a serious threat to the safety of the meat industry. This study evaluated the attachment, survival, and growth of S. aureus biofilm with exposure to environmental factors in the meat industry by simulated ready-to-eat (RTE) cooked beef product contamination scenarios. The results indicated that the meat-borne S. aureus biofilm formation dynamic could be divided into four different phases: initial adhesion (4-12 h), exponential (12-24 h), slow growth (1-3 days), and stationary (3-7 days). Meat-borne S. aureus has strong adhesion and biofilm formation ability, and its biofilm exhibits persistence, high-intensity metabolic activity, aerotaxis, and strain heterogeneity. This study has also demonstrated that in the long-term existence of meat-borne S. aureus biofilm on stainless steel and plexiglass surfaces (>7 days, 7.2-8.8 log CFU/cm² ), expose to RTE cooked beef products, may cause it to become high-risk contaminated food. Meat-borne S. aureus that forms a dense and rough concave-convex in the shape of biofilm architecture was observed by scanning electron microscopy, consisting of complex components and adhesion of living and dead cells. This was further confirmed by the meat-borne S. aureus biofilm on the stainless steel surface by attenuated total reflectance Fourier transformed infrared spectroscopy, and the dominant peaks in biofilm spectra were mainly associated with proteins, polysaccharides, amino acid residues, and phospholipids (>50%). These findings may help in the identification of the main sources of contamination within the meat industry and the subsequent establishment of strategies for biofilm prevention and removal. PRACTICAL APPLICATION: This study revealed the meat-borne S. aureus biofilm formation mechanism and found that it exhibited strong colonization and biofilm-forming ability, which can persist on the contact surfaces of ready-to-eat beef products. These initial findings could provide information on the behavior of meat-borne S. aureus biofilm attached to meat contact surfaces under conditions commonly encountered in meat environments, which help to support the determination of the main sources of contamination within the meat industry and the subsequent establishment of strategies for biofilm prevention and removal. It was also helpful in controlling biofilm contamination and improving meat safety to minimize it.

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

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 (α₁ = 1.04), but had an inhibitory effect on the number of S. aureus (α₂ = 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.

Characterization of the Inclusion Complexes of Isothiocyanates with γ-Cyclodextrin for Improvement of Antibacterial Activities against Staphylococcus aureus

The aim of this study was to develop inclusions formed by γ-cyclodextrin (γ-CD) and three isothiocyanates (ITCs), including benzyl isothiocyanate (BITC), phenethyl isothiocyanate (PEITC), and 3-methylthiopropyl isothiocyanate (MTPITC) to improve their controlled release for the inhibition of Staphylococcus aureus (S. aureus). These inclusion complexes were characterized using X-ray diffraction, Fourier-transform infrared, thermogravimetry, and scanning electron microscopy (SEM), providing appropriate evidence to confirm the formation of inclusion complexes. Preliminary evaluation of the antimicrobial activity of the different inclusion complexes, carried out in vitro by agar diffusion, showed that such activity lasted 5–7 days longer in γ-CD-BITC, in comparison with γ-CD-PEITC and γ-CD-MTPITC. The biofilm formation was less in S. aureus treated with γ-CD-BITC than that of BITC by using crystal violet quantification assay and SEM. The expression of virulence genes, including sarA, agr, cp5D, cp8F, clf, nuc, and spa, showed sustained downregulation in S. aureus treated with γ-CD-BITC for 24 h by quantitative real-time polymerase chain reaction (qRT-PCR). Moreover, the growth of S. aureus in cooked chicken breast treated with γ-CD-BITC and BITC was predicted by the Gompertz model. The lag time of γ-CD-BITC was 1.3–2.4 times longer than that of BITC, and correlation coefficient (R²) of the secondary models was 0.94–0.99, respectively. These results suggest that BITC has a more durable antibacterial effect against S. aureus after encapsulation by γ-CD.

Analysis and mathematical modeling of the survival kinetics of Staphylococcus aureus in raw pork under dynamic and static temperature conditions

The incidence of frequent foodborne disease outbreaks due to Staphylococcus aureus contamination necessitates the urgent searching for effective methods to monitor S. aureus. This research aims to construct model with a dynamic survival curve and some static growth curves to predict the behavior of S. aureus in raw pork. Lack of research about S. aureus kinetics in pork under fluctuating temperature conditions across freezing and thawing necessitates this study. One-step analysis was used to determine the model parameters, which was more efficient than conventional model analysis with two steps. The results of kinetic analysis showed that Tmin (minimum growth temperature) was 6.85°C, which is close to the estimated values in previous reports. Subsequently, validation results indicated the integrated model can accurately predict the behavior of S. aureus regardless of isothermal or nonisothermal conditions with the root-mean-square errors (RMSE < 0.44 log CFU/g, 73.9% of the errors of prediction falls within ±0.5 log CFU/g), accuracy factors Af and bias factors Bf were both close to 1. This work may offer an effective method for the assessment of microbial security related to S. aureus in pork.

Predictive model of growth kinetics for Staphylococcus aureus in raw beef under various packaging systems

This study describes a model of the growth kinetics for S. aureus in raw beef under wrapped packaging (WP), modified atmosphere packaging (MAP), vacuum packaging (VP), and vacuum skin packaging (VSP). Beef samples were inoculated with S. aureus and stored at 10, 15, 20, and 25 °C. VP and VSP showed lower maximum bacteria counts and higher lag time than WP and MAP at all temperatures. At 10 °C, S. aureus in VP and VSP decreased to about 2.5 Log CFU/g. Two primary models (modified Gompertz model and reparameterized Gompertz survival model) were used in the study. The secondary models were described using a polynomial equation and the Davey model. The bias factor (Bf), accuracy factor (Af), and root mean square error (RMSE) of the secondary models were 0.91-1.09, 1.00-1.13, and 0.00-0.68, respectively. The predictive models for kinetics of S. aureus in various packaged raw beef could help to predict the fate of S. aureus more accurately.