Table 1.
Overview of microorganisms and predictive models used to evaluate the log increase of pathogens and spoilage bacteria during different meat ageing processes in ToR3 and ToR4
| Group species | Meat ageing processes | Name | Description secondary model | Source | Calibration factor (b) |
|---|---|---|---|---|---|
| Pathogens | |||||
| Listeria monocytogenes | Wet‐ageing (beef, pork, lamb) | LM | CPM/Gamma (a) | (Mejlholm et al., 2010) | 1 |
| Dry‐ageing (beef) | 0.76 (0.74–0.78) | ||||
| Non‐proteolytic Clostridium botulinum | Wet‐ageing (beef, pork, lamb) | CB | CPM/Gamma (a) | (Koukou et al., 2021) | 1 |
| Yersinia enterocolitica | Wet‐ageing (pork) | YE | Square root model | based on data from (Gill and Reichel, 1989) | 1.10 |
| Non‐pathogens/spoilage bacteria | |||||
| Psychrotolerant LAB |
Wet‐ageing (beef, pork, lamb) Dry‐ageing (beef) |
LAB | CPM/Gamma (a) | (Mejlholm and Dalgaard, 2007) (JFP) or (Mejlholm and Dalgaard, 2013) | 1.89 (2.17–1.30) (c) |
| Pseudomonads | Dry‐ageing (beef) | PS | Square root model (equation for sub‐optimal temperature range) | (Neumeyer et al., 1997) | 1.80 |
LAB: lactic acid bacteria.
Cardinal Parameter Model employing the gamma concept.
Calibration factor equal to 1 means that the original model without correction was implemented. In parentheses the range of calibration factor values considered in the uncertainty analysis.
The same factor applies for aerobic conditions (i.e. when the model is used to simulate microbial interaction between LAB and L. monocytogenes during dry‐ageing). The few data available about LAB growth kinetics on meat under aerobic conditions indicates that the bias factor is within the same order of magnitude as for anaerobic (vacuum packaged) growth.