Predictive modelling of the microbial lag phase: a review.
This paper summarises recent trends in predictive modelling of microbial lag phenomena. The lag phase is approached from both a qualitative and a quantitative point of view. First, a definition of lag and an analysis of the prevailing measuring techniques for the determination of lag time is presented. Furthermore, based on experimental results presented in literature, factors influencing the lag phase are discussed. Major modelling approaches concerning lag phase estimation are critically assessed. In predictive microbiology, a two-step modelling approach is used. Primary models describe the evolution of microbial numbers with time and can be subdivided into deterministic and stochastic models. Primary deterministic models, e.g., Baranyi and Roberts [Int. J. Food Microbiol. 23 (1994) 277], Hills and Wright [J. Theor. Biol. 168 (1994) 31] and McKellar [Int. J. Food Microbiol. 36 (1997) 179], describe the evolution of microorganisms, using one single (deterministic) set of model parameters. In stochastic models, e.g., Buchanan et al. [Food Microbiol. 14 (1997) 313], Baranyi [J. Theor. Biol. 192 (1998) 403] and McKellar [J. Appl. Microbiol. 90 (2001) 407], the model parameters are distributed or random variables. Secondary models describe the relation between primary model parameters and influencing factors (e.g., environmental conditions). This survey mainly focuses on the influence of temperature and culture history on the lag phase during growth of bacteria.
Swinnen
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BioTeC--Bioprocess Technology and Control, Katholieke Universiteit Leuven, W. de Croylaan 46, B-3001 Heverlee, Belgium.
Bernaerts
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Dens
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EJ
Geeraerd
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AH
Van Impe
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JF
D001419:Bacteria / Q000254:growth & development*; D015169:Colony Count, Microbial; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D011237:Predictive Value of Tests; D013269:Stochastic Processes; D013696:Temperature