Establishing Equivalence for Microbial-Growth-Inhibitory Effects (“Iso-Hurdle Rules”) by Analyzing Disparate Listeria monocytogenes Data with a Gamma-Type Predictive Model

Abstract

Preservative factors act as hurdles against microorganisms by inhibiting their growth; these are essential control measures for particular food-borne pathogens. Different combinations of hurdles can be quantified and compared to each other in terms of their inhibitory effect ("iso-hurdle"). We present here a methodology for establishing microbial iso-hurdle rules in three steps: (i) developing a predictive model based on existing but disparate data sets, (ii) building an experimental design focused on the iso-hurdles using the model output, and (iii) validating the model and the iso-hurdle rules with new data. The methodology is illustrated with Listeria monocytogenes. Existing data from industry, a public database, and the literature were collected and analyzed, after which a total of 650 growth rates were retained. A gamma-type model was developed for the factors temperature, pH, a w , and acetic, lactic, and sorbic acids. Three iso-hurdle rules were assessed (40 logcount curves generated): salt replacement by addition of organic acids, sorbic acid replacement by addition of acetic and lactic acid, and sorbic acid replacement by addition of lactic/acetic acid and salt. For the three rules, the growth rates were equivalent in the whole experimental domain (␥ from 0.1 to 0.5). The lag times were also equivalent in the case of mild inhibitory conditions (␥ > 0.2), while they were longer in the presence of salt than acids under stress conditions (␥ < 0.2). This methodology allows an assessment of the equivalence of inhibitory effects without intensive data generation; it could be applied to develop milder formulations which guarantee microbial safety and stability.

A Study on the Synbiotic Composition of Bifidobacterium bifidum and Fructans from Arctium lappa Roots and Helianthus tuberosus Tubers against Staphylococcus aureus

“…The inhibitory effect of SCFAs on pathogen growth is described by the minimum inhibitor concentration (MIC) equation [ 29 , 30 ]: where [ L ] and [ A ] are the concentrations of nondissociated lactic and acetic acids, respectively, in mg·mL −1 ; MIC is the minimum inhibitory concentration in mg/mL; and α and β are constants.…”

Combined effect of anaerobiosis, low pH and cold temperatures on the growth capacities of psychrotrophic Bacillus cereus

“…Growth was defined by an increase in CFU of more than 1 log 10 (Pujol et al, 2012), considering that it should be twice the commonly accepted microbiological experimental error of 0.5 log 10 CFU. In one of the conditions tested in our study, successive slight increases in counts, approx.…”

Multi-criteria framework as an innovative tradeoff approach to determine the shelf-life of high pressure-treated poultry

“…Data were analyzed by applying simultaneously primary and secondary models. The model error was found to be 0.5 log cfu/mL, which is compatible with commonly accepted for microbiological experimental error (Pujol et al, 2012). The parameters of the model were shown to be significant (P b 0.05) (Table S1 in supplementary materials).…”