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Modeling of yeast thermal resistance and optimization of the pasteurization treatment applied to soft drinks

Montanari, Chiara, Tabanelli, Giulia, Zamagna, Ilaria, Barbieri, Federica, Gardini, Aldo, Ponzetto, Mauro, Redaelli, Erika, Gardini, Fausto
International journal of food microbiology 2019 v.301 pp. 1-8
Kluyveromyces marxianus, Saccharomyces cerevisiae, Saccharomycodes, Schizosaccharomyces pombe, Zygosaccharomyces rouxii, carbon nitrogen ratio, death, energy costs, food industry, fruits, heat tolerance, inoculum, microbial physiology, orange juice, pH, pasteurization, predictive microbiology, probability, regression analysis, sensory properties, soft drinks, spoilage, stress response, temperature, yeasts
Yeast are usually responsible for spoilage of soft drinks and fruit beverages, because of the particular characteristics of these products (low pH, high C/N ratio). The microbial stability is guaranteed by thermal treatments. However, excessive heat treatments can affect food sensorial quality. In this work the thermal resistance of different yeasts strains (seven belonging to the species Saccharomyces cerevisiae and six belonging to the species Kluyveromyces marxianus, Zygosaccharomyces bisporus, Z. mellis, Z. rouxii, Schizosaccharomyces pombe and Saccharomycodes ludwigii) was assessed in a model system. The results showed non-linear death curves and a high variability also within the same species. The most resistant strain, belonging to the species S. cerevisiae, was chosen for further experiments in orange juice based industrial beverages: first, death curves were performed; then, the probability of beverage spoilage in relation to process parameters (initial inoculum, temperature, treatment time) was evaluated using a logistic regression model. Finally, a cross-validation was performed to investigate the predictive capability of the fitted model. Pasteurization in the soft drink industry is commonly applied according to parameters defined several decades ago, which does not consider the successive findings concerning microbial physiology and stress response, the process improvement and the more recent tools provided by predictive microbiology. In this perspective, this study can fill a gap in the literature on this subject, going to be a basis for optimizing thermal processes. In fact, the data obtained indicated an interesting possibility for food industry to better modulated (and even reduce) thermal treatments, with the aim to guarantee microbial stability while reducing thermal damage and energy costs.