Modelling the influence of single acid and mixture on bacterial growth

Coroller, Louis; Guerrot, Virginie; Huchet, Véronique; Marc, Yvan Le; Mafart, Pierre; Sohier, Danièle; Thuault, Dominique

doi:10.1016/j.ijfoodmicro.2004.10.014

Cited by 63 publications

(48 citation statements)

References 18 publications

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“…On the contrary, the combination of a temperature of 13 C, a pH of 5.3 after day one and undissociated L and D lactic acid (27 mM at 3 days) and acetic acid levels (12 mM at 3 days) can help to the stop of the Listeria growth. This is supported by the growth-non growth models built by Le Marc et al (2002), and extended by Coroller et al (2005), to predict whether L. monocytogenes will grow or not. These models incorporated pH, temperature, and a mixture of undissociated Fig.…”

Section: Physico-chemical and Organic Acid Changesmentioning

confidence: 83%

Behaviour of Listeria monocytogenes in raw milk Cantal type cheeses during cheese making, ripening and storage in different packaging conditions

Chatelard-Chauvin¹,

Pelissier²,

Hulin³

et al. 2015

Food Control

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Section: Physico-chemical and Organic Acid Changesmentioning

confidence: 83%

Behaviour of Listeria monocytogenes in raw milk Cantal type cheeses during cheese making, ripening and storage in different packaging conditions

Chatelard-Chauvin¹,

Pelissier²,

Hulin³

et al. 2015

Food Control

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“…Conversely, our focus was not on the choice of the secondary model but on the methodology to fit a given model (i.e., to estimate the parameters of the model) using disparate data sets. The model chosen was derived from Coroller et al (9) and was adapted to include the six following environmental factors: temperature, pH, a w , and acetic, lactic, and sorbic acids. In terms of data selection, a systematic procedure was applied.…”

Section: Discussionmentioning

confidence: 99%

“…The n value was set to 2 for temperature, and 1 for pH and water activity (9). Since no data were available in the range above the optimum for a w and only limited data were available in the range above the optimum for temperature and pH, the parameters T opt , T max , pH opt , pH max , a w opt , and a w max were not estimated but fixed in the fitting procedure.…”

Section: Data (I)mentioning

confidence: 99%

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

Pujol¹,

Kan-King-Yu

Marc

et al. 2012

Appl Environ Microbiol

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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.

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“…Square root type models (Ratkowsky et al, 1982) were preferred by Erkmen (2009b), Gumudavelli, Subbuh, Thippareddi, Velugoti, and Froning (2007), Iturriaga, Tamplin, and Escartin (2007), Juneja et al (2007), Thippareddi (2009), Smith (1985), and Velugoti et al (in press). Cardinal parameter model (Rosso et al, 1993) was used by Coroller et al (2005) and Pinon et al (2004); and Arrhenius type models by Davey and Daughtry (1995) and Erkmen (2009b). Artificial neural networks were also proposed to account for the complexities of bacterial growth (Oscar, 2009a;Xiong, Xie, Edmondson, & Meullenet, 2002).…”

Section: Modelling Growthmentioning

confidence: 99%