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The quantitative measurements of the growth of Salmonella can be traced back to the 1950s, when the Copenhagen School studied its cell cycle. Although predictive food microbiology has only been recognised as a discipline in its own right since the 1980s, the first predictive models on Salmonella, specifically on its thermal inactivation, were published in the 1960s. Today this is the foodborne pathogen for which the most D-values can be found in the literature. Being of concern in meat, growth models were developed mainly in poultry, other meats, egg products and culture media. With the advent of the internet, predictive modelling has become more advanced in terms of organising, analysing and visualising large amounts of data, and it has also become easier to disseminate the resultant predictive software packages. We anticipate that computational developments will generate further improvements, including complex scenario analysis, probabilistic and dynamic models. |
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