The dissertation combined in-silico (simulation-based) analyses and in-vitro (laboratory) experiments, leveraging these two complementary perspectives to utilize predictive microbiology methods in practical solutions. The first part of the thesis introduced the Pbase database, constructed on a well-defined ontology that had been developed to standardize microbiological and food-composition data for plant-based alternatives. Although still at a preliminary stage, this contribution highlights the value of structuring and digitizing experimental data as a prerequisite for advancing computational and predictive approaches, such as Big Data and AI applications, in food sciences. The second part evaluated the statistical reliability of parameter estimates when regressing predictive models. For primary models, histogram and correlation analyses revealed that the Baranyi-Roberts regression (BR-Reg) produced estimates more consistent with those generated by linear regression than the Gompertz regression (G-Reg) did, owing this to the construction of BRM which was in fact a refinement of a linear model. For secondary models, reparameterization proved to be a critical method: once transformed, both the Cardinal Temperature Model (CTM) and the Ratkowsky Model (RKM) produced reliable confidence intervals and symmetric error distributions. RKM displayed greater robustness, while CTM performed similarly only when supported by suitable initial estimates. These results emphasize that reliability in predictive modeling is not dictated by model choice only, but also the arrangement in which the model is presented, line its parameterization. The third part focused on B. licheniformis growth kinetics in plant-based beverages. By testing a wide range of temperatures in real food matrices, this study provided high-resolution data that questioned one of the simplifying assumptions often made in predictive microbiology. In particular, the findings demonstrated that cardinal temperatures (minimum, optimum, maximum) are not always independent of the food matrix. This exception underscores the importance of collecting high accuracy data and reporting microbial growth data transparently, even when they depart from convenient generalizations. Moreover, the study showed that food components beyond broad product categories (e.g., “almond milk”) can significantly influence microbial behavior, highlighting the potential of treating matrix composition as a continuous variable in future tertiary models. For practical use, models developed here should be applied within the experimental range of 15–55°C, as extrapolated Tmin and Tmax values may not reflect biological reality. By combining database ontology development, statistical evaluation of models, and applied microbiological studies, it contributes both to the methodological rigor of predictive microbiology and to its practical application in emerging food sectors such as plant-based alternatives.