A new predictive model for the description of the growth of Salmonella spp. in Italian fresh ricotta cheese. (May 2021) - Tirloni, Erica; Stella, Simone; Bernardi, Cristian; Rosshaug, Per Sand
Abstract: In this study, cardinal parameter models were developed for the growth of Salmonella spp. in different brands of Italian fresh ricotta cheese. Two models were proposed, including the effect of temperature or the combined effect of temperature, pH, and concentration of lactic, citric and, acetic acid. Validation of the models included an assessment of the ability to predict maximum specific growth rate μ m a x using two indices: bias-factor (Bf ) and accuracy factor (Af ), and the acceptable simulation zone (ASZ). The new models for Salmonella spp. showed good performances with Bf of 1.11–1.10 (model with 1 or 5 variables), and an average of 91% and 89% of observations within the ASZ (model with 1 or 5 variables, respectively). Comparing the performances of other existing models when applied to ricotta cheese, a general underprediction of the growth rate was evidenced. The proposed models can be applied by a high number of users with the aim to assess levels of this pathogen in ricotta cheese under both static and dynamic environmental conditions, being useful for the dairy business as the tested conditions cover a wide range of the available brands on the market. Highlights: Cardinal parameter models were developed and validated for the growth of Salmonella in ricotta. The models included the effect of temperature, pH, lactic, citric, and acetic acids. The model for Salmonella spp. showed good performances with Bias factor of 1.11–1.10 (1–5 variables). Existing models under predicted the growth when applied to ricotta. The new models could be useful for ricotta cheese producers.