In silico logistic model for table olive related microorganisms as a function of sodium metabisulphite, cinnamaldehyde, pH, and type of acidifying agent

Abstract

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A probabilistic/logistic model, based on binary data (growth/no growth), was used to assess the effects of sodium metabisulphite (SM) and cinnamaldehyde (CIN) (0-1,000 mg/L) against the main microbial groups found in table olive environment (lactic acid bacteria (LAB), yeasts, and Enterobacteriaceae), according to pH (range 3.5-5.0) and type of acidifying agent (HCl or pyruvic acid). The inhibitory effect of SM depended on the pH while that of CIN was scarcely influenced by it (except for LAB). LAB were more sensitive to SM, while yeasts were to CIN. The use of pyruvic acid for correction of pH always produced a reduction (compared to HCl) of the inhibitory power of both preservatives. The in silico models for HCl showed that, at pH 4.0 and growth probability 0.01, the LAB population might be inhibited by the presence in the medium of 150 mg/L SM or 1,000 mg/L CIN, while in the case of yeasts, 450 mg/L SM or 150 mg/L CIN are required. No growth of Enterobacteriaceae was observed at this (or lower) pH level. The results obtained may contribute to the stabilization of non-thermally treated table olive packaging.

Keywords

• Table Olives
• Predictive Modeling
• preservatives
• sulphides
• Cinnamaldehyde