|
|
|
|
|
|
Scientific
Publications - Work Done by Microbiology Reader
International Journal of Food Microbiology, 2001, Oct 22, Vol. 70, No. 1-2, pp.163-73 The effect of inoculum size on the lag phase of Listeria monocytogenesTobin P. Robinsona, Olosimbo O. Aboabab, Anu Kalotib, Maria J. Ociob, Jozsef Baranyic and Bernard M. Mackeyb a Pôle de Competénce en Sécurité des Aliments, Centre International de Recherche Daniel Carasso, Le Plessis-Robinson 92350, France b School of Food Biosciences, University of Reading, PO Box 226, Whiteknights, Reading, Berkshire, RG6 6AP UK c Institute of Food Research, Norwich Research Park, Norwich, NR4 7UA UK Received 23 April 2001; accepted 16 May 2001. Available online 16 October 2001. ABSTRACT The effect of inoculum size on population lag times of Listeria monocytogenes was investigated using the Bioscreen automated microtitre plate incubator and reader. Under optimum conditions, lag times were little affected by inoculum size and there was little variation between replicate inocula even at very low cell numbers. However, in media containing inhibitory concentrations of NaCl, both the mean lag time and variation between replicate inocula increased as the inoculum size became smaller. The variation in lag time of cells within a population was investigated in more detail by measuring the distribution of detection times from 64 replicate inocula containing only one or two cells capable of initiating growth. The variance of the lag time distribution increased with increasing salt concentration and was greater in exponential than in stationary phase inocula. The number of cells required to initiate growth increased from one cell under optimum conditions to 105 cells in medium with 1.8 M NaCl. The addition of spent medium from a stationary phase culture reduced the variance and decreased lag times. The ability to initiate growth under severe salt stress appears to depend on the presence of a resistant sub-fraction of the population, although high cell densities assist adaptation of those resistant cells to the unfavourable growth conditions by some unspecified medium conditioning effect. These results are relevant to the prediction of lag times and probability of growth from low numbers of stressed cells in food. Author Keywords: Lag phase; Listeria monocytogenes
1. INTRODUCTION The lag phase of the microbial growth cycle represents the time period needed for bacteria to adapt to a new environment before cell multiplication commences. Recent work modelling the behaviour of bacteria in foods has shown that the lag phase is more difficult to predict than is the specific growth rate (McMeekin; Duh; Duffy; Kn; Robinson; Delignette and Augustin). This implies that certain relevant variables affecting lag time are not taken into account, particularly the physiological state of the inoculum and the inoculum size. The physiological state of cells will be affected by their previous growth environment and by exposure to stress conditions. Cellular injury caused by heating freezing or drying can extend the lag time considerably and also increase its variability (Mackey; Mackey; Stephens; Br and Br). Starved cells may also have very extended lag phases (Albertson et al., 1990). The temperature history of the inoculum culture and its growth conditions have a profound effect on lag (Buchanan; Hudson; Gay; Dufrenne and Augustin). Some mathematical models used in predictive food microbiology contain a parameter related to the physiological state of the inoculum (Baranyi; Hills and Van) but it has not been possible so far to measure physiological state directly in terms that are useful for predicting behaviour. This may become possible eventually with developments in methods for measuring single cell metabolic activity (Porter and Ueckert) Two classes of inoculum size effect on population lag may be envisaged (a) cooperative or inhibitory effects of high cell concentrations or (b) statistical effects at low cell concentrations arising from the variability in individual lag times. There is little specific information about the possible effects of cell–cell interactions on lag time although cell signalling has been shown to affect the emergence of cells from dormancy and the lag time of populations in biofilms (Mukamolova and Batchelor). The effect of substances produced in culture supernatants on microbial growth was reviewed by Kaprelyants et al. (1999) Mathematical treatments of the statistical effects of inoculum size on population lag were provided by Baranyi (1998) and Baranyi and Pin (1999). These authors showed that as the cell number in the inoculum decreases, the population lag increases by an amount that depends on the distribution of individual lag times and the maximum specific growth rate. The mathematical analyses predict that under optimum growth conditions, this statistical effect would only be expected at inoculum levels of below about 102–103 cells. McKellar and Knight (2000) have also presented a model of the lag phase of Listeria monocytogenes that takes into account the variability of individual cells. There have been relatively few experimental tests of the effect of inoculum size on lag, but available data suggests that the effects are small in broth cultures under optimum growth conditions (Jason, 1983) and also in food (Mackey; Lanciotti and Duffy). However more recently, it has been shown that the lag time of L. monocytogenes was extended in stressed cells when the inoculum size was small (Gay and Augustin). Stephens et al. (1997) also showed that the length of the lag phase is not only very variable in heat injured cultures of low cell density, but also on average, longer than those of high cell density. The purpose of this study was to investigate the effect of inoculum size on the population lag time of L. monocytogenes under conditions of salt stress and to assess the heterogeneity in lag times of inocula containing very low cell numbers. During the course of this work, an effect of population size on the probability of initiating growth became evident.
2. MATERIALS AND METHODS 2.1. Organism and culture conditionsL. monocytogenes NCTC 11994 was used throughout these experiments. It
was kept on glass beads at −70 °C (Jones et al., 1984) and maintained on
tryptone soya agar (TSA; Oxoid, Basingstoke, UK) slopes at 5 °C. Stationary
phase inocula were prepared by inoculating 20 ml tryptone soya broth (TSB;
Oxoid) and incubating overnight at 37 °C. To produce an exponential phase
inoculum, 0.1 ml of a stationary phase culture was inoculated into 20 ml TSB
(pre-warmed to 37 °C) and incubated until the optical density at 680 nm (OD680)
reached 0.15 measured in a spectrophotometer (Cecil Instruments, Cambridge, UK,
model CE 1021) with a light path of 1 cm. Viable counts were measured by using
spread plates. Tenfold dilutions of culture were made in Maximum Recovery
Diluent (Oxoid) and duplicate 50
2.2. BioscreenThe effect of inoculum size on the variation in the population lag was
investigated by preparing tenfold serial dilutions of exponential and stationary
phase cultures in either TSB, TSB with 1.2 M NaCl or TSB with 1.6 M NaCl. Ten
replicate 300
Detection times obtained from the outermost wells of the microtitre plates were generally longer than those from the other wells, particularly when the medium contained a high salt concentration. On further investigation, we found that for long incubations (greater than 7 days), there was a decrease in the volume of media in these outer wells of up to 10%. We therefore discarded the results obtained from all the outer wells in all experiments. So, for the initial experiments, there were only eight replicates rather than 10 per dilution. To investigate variability in the length of the lag phase at very low inoculum levels, cultures were diluted to a level that gave growth in approximately 50% of 64 wells. According to the Poisson distribution, 70% of positive wells would then contain a single cell and 24% two cells. Three salt concentrations, 0, 1.2 and 1.6 M, were investigated. The possible effect of medium conditioning (alteration of some
physicochemical aspect of the growth medium by the previous growth of a culture)
or cell signalling was checked by preparing dilutions of log phase cells using
50:50 spent broth/fresh TSB, with added NaCl as before. The spent broth was
prepared by filtering a 24-h culture through a 0.2- 2.3. Probability of growthThe effect of inoculum size on the probability of initiating growth in a culture was investigated by incubating 40 replicates of a few critical inoculum levels, taken around the growth/no-growth inoculum size boundary at three salt concentrations, 0, 1.7 and 1.8 M. 2.4. Mathematical treatment of dataData from the Bioscreen were analysed using the approach of Baranyi (1998).
Detection time is converted to a corresponding ‘physiological state’ parameter (
where
3. RESULTS 3.1. The effect of inoculum size on lag timeTo test whether there was an inoculum size effect, tenfold serial dilutions
of cultures were made in TSB containing different levels of NaCl, and the
detection times recorded. Assuming that specific growth rate is unaffected by
inoculum size, the difference in detection time between consecutive dilutions
should be constant and proportional to the maximum specific growth rate (
As conditions became more stressful and growth rate decreased, i.e. in TSB containing 1.2 M NaCl, there was greater variation in lag between replicates particularly for the exponential phase inoculum (Fig. 1b). The variability increased with decreasing inoculum size especially below 104 cells per well. The trend of the scatter was towards longer detection times rather than being distributed around the expected detection time. The net effect was that as inoculum size decreased, the mean detection time deviated more from that predicted assuming that lag time and growth rate remain constant (Fig. 1b). Assuming that the maximum growth rate is independent of inoculum size, the observed deviation represents an increase in lag at low cell numbers. An additional effect was that, with exponential phase cultures, no growth was seen from inocula containing less than 10 cells. Cultures inoculated with stationary-phase cells showed a linear relationship between inoculum size and detection time, except at very low cell numbers, where detection times were longer than predicted and fewer wells showed growth. The results from experiments with TSB containing 1.6 M NaCl (Fig. 1c) show a continuation of this trend, such that the relationship between inoculum size and mean detection time is no longer linear. Less than 50% of the cultures grew when inoculated with 104 cells, and none grew with inocula of less than 103 cells, irrespective of the growth phase. Exponential phase inocula gave consistently longer detection times than stationary phase inocula (Fig. 1c). The detection time data were transformed according to the method of Baranyi
and Pin (1999) to yield
The distribution of detection times was studied in more detail for very small inocula. Sixty-four replicates were prepared at inoculum levels that gave growth in approximately half the replicates. The average number of cells per inoculum required to give growth in half the replicates was determined independently by plate counting. For TSB containing 0, 1.2 or 1.6 M NaCl, the mean inoculum sizes were 1.2, 5.0 and 2000 cells, respectively. However, since only half the wells actually showed growth, the effective mean inoculum size according to the Poisson distribution would have been 0.7 cells per well in each case. Variations in detection times are shown in Fig. 3. The data were normalised by setting histogram bin size equal to the doubling time for each salt concentration, hence, the increase in variability in detection times shown in the figures was not simply an effect of growth rate. In TSB alone, there was very little variation in detection times (and hence lag times) between inocula of approximately one cell (Fig. 3a). In medium containing 1.2 M NaCl, there was a pronounced difference between the responses of exponential and stationary phase cells (Fig. 3b). Stationary phase cells showed relatively little variation in detection times a slight tailing towards longer detection times. The range between shortest and longest detection time was equivalent to about five doubling times. Under the same conditions, exponential phase cells showed a much larger variation, with the longest detection times being approximately twice that of the shortest (a range of about 22 doubling times). A similar effect was observed at the higher salt concentration of 1.6 M (Fig. 3c).
3.2. Effect of inoculum size on the ability to initiate growthWhen cells were inoculated into TSB with no added salt, the percentage of wells showing growth was close to that predicted from the Poisson distribution, showing that growth was possible from single cells. However, with increasing salt concentration, the number of cells required to initiate growth in 50% of the wells increased to more than 103 cells in media containing 1.7 M NaCl and around 106 cells the presence of 1.8 M NaCl (Fig. 4).
3.3. Effect of spent mediumTo further investigate the cause of the inoculum size effects, log phase cells were inoculated into fresh TSB mixed with an equal volume of spent broth. The hypothesis was that inoculum size effects (a shortened lag phase or increased probability of growth at high cell density) might be caused either by carry-over of substances with the inoculum, or the presence of a signal chemical produced during the lag phase that may also be present in medium from stationary phase cultures. Spent medium had no effect at NaCl concentrations of 1.2 M or less (results not shown). However, with 1.6 M NaCl detection times were shortened with inocula of 3000 cells per inoculum or less, and the variability between samples reduced (Fig. 5). A simple ANOVA was run on the detection times measured at the two lowest inoculum levels, where the variabilty of the lag times of individual cells has the biggest effect on the population lag. An F-test showed that the probability that the shorter avarage lag times were observed only by chance was less than 10−3. The standard deviation of the detection times decreased by more than 50% for both inoculum levels, confirming the significance of the spent medium effect.
4. DISCUSSION 4.1. Use of the bioscreenThe Bioscreen proved to be a useful means of producing large quantities of growth curve data. It has been used for a number of different applications such as determining bacterial growth rates (Dalgaard et al., 1994), studying the effect of different conditions on growth (McClure and Korkeala), growth inhibition studies (Adams and Hall, 1988), enumeration of bacteria from food samples (Mattila and Jakob), and comparison of pre-enrichment media for resuscitation of injured salmonellae (Stephens et al., 1997). However, the data produced need to be analysed with care. The spurious readings obtained from the outermost wells were most obvious when the cells were grown with high NaCl concentrations under conditions that required long incubation times before growth became detectable. This probably resulted in evaporation from the outermost wells, a feature that limits the incubation times that can be employed in these studies. 4.2. The effect of inoculum size on lag timeOur basic assumption was that the maximum specific growth rate ( The lag time of a population is less than the average lag time of individual cells because cells with shortest lags begin to multiply soonest and their descendants dominate the population numerically. This effect was analysed by Baranyi (1998) who derived a mathematical relationship between population lag and the distribution of individual cell lag times and growth rate. This analysis revealed that as cell numbers in the inoculum increased from 1 to 103, the scatter of lag times from replicate inocula decreased. The greatest variation in lag should thus occur in inocula containing low numbers of cells from populations that had a wide scatter of individual cell lag times. Any variation in lag time between replicate inocula will be reflected in a corresponding variation in detection times. Because plots of inoculum size versus detection time were essentially linear for growth in TSB, we conclude that, under optimum conditions, there was no effect of inoculum size on population lag, except that due to statistical variation which was noticeable only at low cell numbers (100 cells per well or less). Our results obtained under optimum conditions agree with the work of Jason (1983) who found that lag and growth rate of Escherichia coli were independent of inoculum size. Duffy et al. (1994b) also concluded from viable count data that the lag times of L. monocytogenes in Listeria Selective Broth, Palcam broth or a mixture of beef mince and broth, were unaffected by inoculum size. Although an increase in lag time was observed with decreasing inoculum size this was statistically insignificant due to the wide scatter of the data. Under the more exacting growth conditions provided by TSB containing 1.2 or 1.6 M NaCl, there was a wider scatter of detection times as inoculum size decreased, and the mean value diverged increasingly from that predicted assuming a constant lag. This was particularly noticeable with exponential phase inocula. Exponential phase cells are generally more fragile and susceptible to injury than those in the stationary phase (Mackey, 2000) and this may account for their longer and more variable lag times following osmotic shock. Transformation of the data according to Baranyi and Pin (1999) showed that inoculum size had an effect on lag that was separate from the statistical effect caused by the distribution of lag times of single cells, i.e. there was a cooperative population effect. An effect of inoculum size on lag has been shown previously with L. monocytogenes inoculated into broth at pH 5.9 and an incubation temperature of 14 °C, designed to simulate ripening of Camembert cheese (Gay et al., 1996). The biggest inoculum size effect was seen when cells were grown at 30 °C then held for 4 weeks at 4 °C before inoculation into broth at 14 °C, when an inoculum of 103 cells gave a lag of less than 21 h compared with 7.7 days for an inoculum of 10 cells. An extension of lag time and an increase in variability between replicate inocula has also been reported for heat-injured cells of Salmonella typhimurium (Mackey and Stephens). This is similar to the effect described here with salt-stressed cells of L monocytogenes. 4.3. Effect of inoculum size on the probability of initiating growthUnder optimum conditions a single cell was able to initiate growth in TSB but, in the presence of high salt concentrations, much larger inocula were needed. A similar observation was made by Razavilar and Genigeorgis (1988) who found that the number of cells of L. monocytogenes required to initiate growth at 30 °C was unaffected by NaCl concentrations up to 1.37 M. However, at concentrations of 1.71 to 2.05 M NaCl, at least 105 cells were required for growth to occur, comparable with an inoculum size of 103 with 1.7 M and 106 with 1.8 M NaCl that we observed at 37 °C. 4.4. Statistical explanations of population size effectsStudies with inocula containing predominantly single cells suggested that the increase in mean population lag time at low cell number under stressful growth conditions could have been explained simply by the corresponding increase in scatter in the lag times of single cells. In unstressed populations the variation of lag times was only about ±2 doubling times. Part of that variation can be accounted for by variation in the number of cells per well as predicted from the Poisson distribution. Since the number of wells showing growth was around 50%, about 70% of positive wells would contain one cell and 24% would contain two cells. The detection times of wells containing two cells would be shorter by one generation time than those containing only a single cell. Under stressful conditions, the scatter increased to about ±10 doubling times implying that the variability in detection time was largely due to greatly extended lags rather than different numbers of cells in the wells. The question nevertheless arises whether inoculum size effects on lag time and the probability of initiating growth can be explained solely by the distribution of some resistance attribute within the population. In broth containing 1.2 M NaCl, a wide scatter of lag times was seen even with inocula containing 3000 cells, a number which, from the theoretical results of Baranyi (1998), should have ensured that the lag times of replicates would have converged close to the population mean. However, a comparison of the dilution at which wells showed no growth with the known number of cells inoculated, showed that the number of cells able to initiate growth was much less than the number present in the wells, and this may therefore bring the effective inoculum size within the range where statistical effects become apparent. The same argument applies with cells in 1.6 M NaCl except the fraction of cells able to initiate growth was even smaller. 4.5. Cooperative effects on lag and the ability to initiate growthAn alternative explanation for the inoculum size effects is that some kind of conditioning of the culture medium or chemical signalling is required before growth can occur under stressful conditions. There appears to be a critical threshold cell density below which growth initiation is not possible and this threshold is related to the severity of the culture conditions. Cells may produce a chemical or physicochemical change in situ, or there may be carry-over of substances from the inoculum. The addition of spent medium to TSB containing 1.6 M NaCl shortened detection times, and decreased lag time variability, but no change was recorded in the probability of initiating growth. A requirement for specific signal molecules in recovery from stress has been reported in some bacteria. Cells of Nitrosomonas europaea recovered from starvation and commenced growth sooner when cells were present in a biofilm (Batchelor et al., 1997). Acylated homoserine lactones were shown to reduce the length of the lag phase in a concentration dependent manner, suggesting that these signal molecules responsible for this cooperative effect. In the Gram positive Micrococcus luteus a peptide signal molecule that is necessary for the revival of dormant cells has been isolated from culture medium (Mukamolova et al., 1998). The growth stimulatory effects described here may also involve signal molecules, but a non-specific conditioning effect could also explain the phenomenon. For example, the protective effect of high cell densities on stressed bacteria (Winslow and Spangler) may be due to non-specific effects such as leakage of magnesium or other unspecified material from dead and injured cells that protects the remaining cells (Strange and Strange). Oxygen tension and redox potential are other examples of non-specific factors that can markedly effect the recovery of stressed cells (Knabel; George and Stephens). The results reported here support the view that the ability to initiate growth under severe salt stress depends on the presence of a resistant sub-fraction of the population, but that high cell densities appear to assist the adaptation of those cells to the unfavourable growth conditions by some unspecified medium conditioning effect. 4.6. Relevance to growth in foodThe results presented here predict that, under inhibitory conditions, the mean lag time of cells that were sparsely distributed in food would be appreciably longer than that indicated from broth experiments with large inocula. Mackey and Kerridge (1988) found that lag times of salmonellae growing in minced beef at temperatures between 10 and 35 °C were much more variable than the corresponding growth rates. Considering all data, there were no statistically significant differences between the lag times of large and small inocula (40 or 104 cells g−1, respectively) but at the lowest most stressful temperature measured (10 °C), the lag time was 50% longer (60 h compared with 40.6 h) for the lower inoculum level. The results presented here also suggest that, under inhibitory conditions, the probability that a pathogen could initiate growth in any single food item containing very low numbers of cells would be less than that suggested by growth studies in broth inoculated with thousands of cells. The exact probability value per pack would depend on the distribution of resistance within the population and the actual number of cells present per pack. The overall probability that growth would occur at least in some packs would be the same as that calculated from broth studies with large inocula. Any reduction of lag time by cooperative or conditioning effects would depend on cell concentration and proximity; for example whether cells were present as microcolonies or single cells. Predicting the behaviour of bacteria in food is often based on experiments with initial inocula in excess of 103 cells ml−1 (Papegeorgiou; Buchanan; Farber and Miller). From our work, it would appear that beneath this level of inoculation, the lag phase becomes longer and the probability of growth is less. This may have significance in estimating risk and calculating safe storage times for foods.
ACKNOWLEDGEMENTS We thank the Ministry of Agriculture, Fisheries and Food/Food Standards Agency for their financial support of this work. O.O. Aboaba was funded by the Royal Society and by the United Nations Educational Scientific and Cultural Organisation. M.J. Ocio was supported by the Spanish Ministry for Science and Education.
REFERENCES Adams and Hall, 1988M.R. Adams and C. Hall , Growth inhibition of food-borne pathogens by lactic and acetic acid and their mixtures. Int. J. Food Sci. Technol. 23 (1988), pp. 287–292. Albertson et al., 1990N.H. Albertson, T. Nystrom and S. Kjelleberg , Macromolecular synthesis during recovery of the marine Vibrio sp. S4 from starvation. J. Gen. Microbiol. 136 (1990), pp. 2201–2207. Augustin and Carlier, 2000J.C. Augustin and V. Carlier , Mathematical modelling of the growth rate and lag time for Listeria monocytogenes. Int. J. Food Microbiol. 56 (2000), pp. 29–51. Augustin et al., 2000aJ.-C. Augustin, L. Rosso and V. Carlier , A model describing the effect of temperature history on lag time for Listeria monocytogenes. Int. J. Food Microbiol. 57 (2000), pp. 169–181. Augustin et al., 2000bJ.-C. Augustin, A. Brouillaud-Delattre, L. Rosso and V. Carlier , Significance of inoculum size in the lag time of Listeria monocytogenes. Appl. Env. Microbiol. 66 (2000), pp. 1706–1710. Baranyi, 1998J. Baranyi , Comparison of stochastic and deterministic concepts of bacterial lag. J. Theor. Biol. 192 (1998), pp. 403–408. Baranyi and Pin, 1999J. Baranyi and C. Pin , Estimating bacterial growth parameters by means of detection times. Appl. Environ. Microbiol. 65 (1999), pp. 732–736. Baranyi and Roberts, 1994J. Baranyi and T.A. Roberts , A dynamic approach to predicting bacterial growth in food. Int. J. Food Microbiol. 23 (1994), pp. 277–294. Batchelor et al., 1997S.E. Batchelor, M. Cooper, S.R. Chhabra, L.A. Glover, G.S.A.B. Stewart, P. Williams and J.I. Prosser , Cell density-regulated recovery of starved biolfilm populations of ammonia-oxidising bacteria. Appl. Environ. Microbiol. 63 (1997), pp. 2281–2286. Bréand et al., 1997S. Bréand, G. Fardel, J.P. Flandrois, L. Rosso and R. Tomassone , A model describing the relationship between lag time and mild temperature increase duration. Int. J. Food Microbiol. 38 (1997), pp. 157–167. Bréand et al., 1999S. Bréand, G. Fardel, J.P. Flandrois, L. Rosso and R. Tomassone , A model describing the relationship between regrowth lag time and mild temperature increase for Listeria monocytogenes. Int. J. Food Microbiol. 38 (1999), pp. 157–167. Buchanan and Klawitter, 1990R.L. Buchanan and L.A. Klawitter , Effects of temperature and oxygen on the growth of Listeria monocytogenes at pH 4.5. J. Food Sci. 55 (1990), pp. 1754–1756. Dalgaard et al., 1994P. Dalgaard, T. Ross, L. Kamperman, K. Neumeyer and T.A. McMeekin , Estimation of bacterial growth rates from turbidometric and viable count data. Int. J. Food Microbiol. 23 (1994), pp. 391–404. Delignette-Muller, 1998M.L. Delignette-Muller , Relation between the generation time and the lag time of bacterial growth kinetics. Int. J. Food Microbiol. 43 (1998), pp. 97–104. Duffy et al., 1994aL.L. Duffy, P.B. Vanderline and F.H. Grau , Growth of Listeria monocytogenes on vacuum-packed meats: effect of pH, aw, nitrite and ascorbate. Int. J. Food Microbiol. 23 (1994), pp. 377–390. Duffy et al., 1994bG. Duffy, J.J. Sheridan, R.L. Buchanan, D.A. McDowell and I.S. Blair , The effect of aeration, initial inoculum and meat microflora on the growth kinetics of Listeria monocytogenes in selective enrichment broths. Food Microbiol. 11 (1994), pp. 429–438. Dufrenne et al., 1977J. Dufrenne, E. Delfgou, W. Ritmeester and S. Notermans , The effect of previous growth conditions on the lag phase time of some foodborne pathogenic micro-organisms. Int. J. Food Microbiol. 34 (1977), pp. 89–94. Duh and Schaffner, 1993Y.-H. Duh and D.W. Schaffner , Modelling the effect of temperature on the growth rate and lag time of Listeria innocua and Listeria monocytogenes. J. Food Prot. 56 (1993), pp. 205–210. Farber et al., 1992J.M. Farber, F. Coates and E. Daley , Minimum water activity requirements for the growth of Listeria monocytogenes. Lett. Appl. Microbiol. 15 (1992), pp. 103–105. Gay et al., 1996M. Gay, O. Cerf and K.R. Davey , Significance of pre-incubation temperature and inoculum concentration on subsequent growth of Listeria monocytogenes at 14 °C. J. Appl. Bacteriol. 81(1996), pp. 433–438. George et al., 1998S.M. George, L.C.C. Richardson, I.E. Pol and M.W. Peck , Effect of oxygen concentration and redox potential on recovery of sublethally heat-damaged cells of Escherichia coli O157:H7, Salmonella enteritidis and Listeria monocytogenes. J. Appl. Microbiol. 84 (1998), pp. 903–909. Hills and Wright, 1995B.P. Hills and K.M. Wright , A new model for bacterial growth in heterogeneous systems. J. Theor. Biol. 168 (1995), pp. 39–41. Hudson, 1993J.A. Hudson , Effect of pre-incubation temperature on the lag time of Aeromonas hydrophila. Lett. Appl. Microbiol. 16 (1993), pp. 274–276. Jakob et al., 1989R. Jakob, S. Lippert and J. Baumgart , Automated turbidometry for the rapid differentiation and enumeration of bacteria in food. Int. J. Food Res. Technol. 189 (1989), pp. 147–148. Jason, 1983A.C. Jason , A deterministic model for monophasic growth of batch cultures of bacteria. Antonie van Leeuwenhoek 49 (1983), pp. 513–536. Jones et al., 1984D. Jones, P.A. Pell and P.H.A. Sneath , Maintenance of bacteria on glass beads at −60 °C to −76 °C. In: B.E. Kirsop and J.J.S. Snell, Editors, Maintainance of Microorganisms, Academic Press, London (1984). Kaprelyants et al., 1999A.S. Kaprelyants, G.V. Mukamolova, S.V. Kormer, D.H. Weichart, M. Young and D.B. Kell , Intercellular signalling and the multiplication of prokaryotes: bacterial cytokines. In: R.R. England, Editor, Microbial Signalling and Communication: Fifty-seventh Symposium of the Society for General Microbiology, Held at the University of Edinburgh, April, Cambridge Univ. Press (1999), pp. 33–69. Knabel and Thielen, 1995S.J. Knabel and S.A. Thielen , Enhanced recovery of severely heat-injured thermotolerant Listeria monocytogenes from USDA and FDA primary enrichment media using a novel, simple, strictly anaerobic method. J. Food Prot. 58 (1995), pp. 29–34. Knøchel and Gould, 1995S. Kn(chel and G. Gould , Preservation microbiology and safety: Quo vadis?. Trends Food Sci. Technol. 6 (1995), pp. 127–131. Korkeala et al., 1992H. Korkeala, T. Alanko and T. Tiusanen , Effect of sodium nitrite and sodium chloride on growth of lactic acid bacteria. Acta Vet. Scand. 33 (1992), pp. 27–32. Lanciotti et al., 1992R. Lanciotti, S. Massa, M.E. Guerzoni and G. Di Fabio , Light butter: natural microbial population and potential growth of Listeria monocytogenes and Yersinia enterocolitica. Lett. Appl. Microbiol. 15 (1992), pp. 256–258. Mackey, 2000B.M. Mackey , Injured bacteria. In: B.M. Lund, A. Baird-Parker and G.W. Gould, Editors, The Microbiological Safety and Quality of Food, Aspen Publishers, Gaithersburg, MA (2000), pp. 315–341. Mackey and Derrick, 1983B.M. Mackey and C.M. Derrick , The effect of sublethal injury by heating, freezing, drying and gamma radiation on the duration of the lag phase of Salmonella typhimurium. J. Appl. Bacteriol. 53 (1983), pp. 243–251. Mackey and Derrick, 1984B.M. Mackey and C.M. Derrick , Conductance measurements of the lag phase of injured Salmonella typhimurium. J. Appl. Bacteriol. 57 (1984), pp. 299–308. Mackey and Kerridge, 1988B.M. Mackey and A.L. Kerridge , The effect of incubation temperature and inoculum size on growth of salmonellae in minced beef. Int. J. Food Microbiol. 6 (1988), pp. 57–65. Mattila, 1987T. Mattila , Automated tubidometry—a method for enumeration of bacteria in food samples. J. Food Prot. 50 (1987), pp. 640–642. McClure et al., 1991P.J. McClure, T.M. Kelly and T.A. Roberts , The effects of temperature, pH, sodium chloride and sodium nitrite on the growth of Listeria monocytogenes. Int. J. Food Microbiol. 14 (1991), pp. 77–91. McKellar and Knight, 2000R.C. McKellar and K.P. Knight , A combined discrete continuous model for describing the lag phase of Listeria monocytogenes. Int. J. Food Microbiol. 54 (2000), pp. 171–180. McMeekin et al., 1993T.A. McMeekin, J.N. Olley, T. Ross and D.A. Ratkowsky Predictive Microbiology: Theory and Application, Research Studies Press, Taunton (1993). Membré et al., 1999J.-M. Membré, T. Ross and T.A. McMeekin , Behaviour of Listeria monocytogenes under combined chilling processes. Lett. Appl. Microbiol. 28 (1999), pp. 216–220. Miller, 1992A.J. Miller , Combined water activity and solute effects on growth and survival of Listeria monocytogenes Scott A. J. Food Prot. 55 (1992), pp. 414–418. Mukamolova et al., 1998G.V. Mukamolova, A.S. Kaprelyants, I. Danielle, M. Young and D.B. Kell , A bacterial cytokine. Proc. Natl. Acad. Sci. 95 (1998), pp. 8916–8921. Papegeorgiou and Marth, 1989D.K. Papegeorgiou and E.H. Marth , Behaviour of Listeria monocytogenes at 4 and 22 °C in whey and skim milk containing 6 or 12% sodium chloride. J. Food Prot. 52 (1989), pp. 625–630. Porter et al., 1995J. Porter, C. Edwards and R.W. Pickup , Rapid assessment of physiological status in Escherichia coli using fluorescent probes. J. Appl. Bacteriol. 79 (1995), pp. 399–408. Razavilar and Genigeorgis, 1988V. Razavilar and C. Genigeorgis , Prediction of Listeria spp. growth as affected by various levels of chemicals, pH, temperature and storage time in a model broth. Int. J. Food Microbiol. 40 (1988), pp. 149–157. Robinson et al., 1998T.P. Robinson, M.J. Ocio, A. Kaloti and B. Mackey , The effect of the growth environment on the lag phase of Listeria monocytogenes. Int. J. Food Microbiol. 44 (1998), pp. 83–92. Spangler and Winslow, 1943C.D. Spangler and C.-E. Winslow , The influence of the sodium ion on the viability of washed cells of Bacillus cereus. J. Bacteriol. 45 (1943), pp. 373–384. Strange and Shon, 1964R.E. Strange and M. Shon , Effects of thermal stress on viability and ribonucleic acid of Aerobacter aerogenes in aqueous suspension. J. Gen. Microbiol. 34 (1964), pp. 99–114. Strange et al., 1961R.E. Strange, F.A. Dark and A.G. Ness , The survival of stationary phase Aerobacter aerogenes stored in aqueous suspension. J. Gen. Microbiol. 39 (1961), pp. 61–76. Stephens et al., 1997P.J. Stephens, J.A. Joynson, K.W. Davies, R. Holbrook, H.M. Lappin-Scott and T.J. Humphrey , The use of an automated growth analyser to measure recovery times of single heat-injured Salmonella cells. J. Appl. Microbiol. 83 (1997), pp. 445–455. Stephens et al., 2000P.J. Stephens, P. Druggan and G. Nebe-von Caron , Stressed salmonellae are exposed to reactive oxygen species from two independent sources during recovery in conventional culture media. Int. J. Food Microbiol. 60 (2000), pp. 269–285. Ueckert et al., 1995J. Ueckert, P. Breeuwer, T. Abee, P. Stephens, G. Nebe von Caron and P.F. ter Steeg , Flow cytometry applications in physiological study and detection of foodborne pathogens. Int. J. Food Microbiol. 28 (1995), pp. 317–326. Van Impe et al., 1992J.F. Van Impe, B.M. Nicolai, T. Martens, J. De Baerdemaeker and J. Vandewalle , Dynamic mathematical model topredict microbial growth and inactivation during food processing. Appl. Environ. Microbiol. 58 (1992), pp. 2901–2909. Winslow and Brooke, 1927C.-E. Winslow and O.R. Brooke , The viability of various species of bacteria in aqueous suspensions. J. Bacteriol. 13 (1927), pp. 235–243.
(order Full Text from publisher)
|
© 2005
Transgalactic Ltd (manufacturer of Bioscreen C software) |
Privacy Statement | P.O. Box
1393, 00101 Helsinki, Finland,
Last modified: May 25, 2005
| ||||||
l
samples spread on TSA plates. 
)
that is an expression of the ‘readiness of cells to grow’ using the following
transformation
_163.gif){kind=small}
)
and stationary phase (•) inocula of L. monocytogenes incubated in (a)
TSB, (b) TSB with 1.2 M NaCl, (c) TSB with 1.6 M NaCl. Solid lines are drawn
through the means of each data set. Dashed lines show the mean detection times
predicted from independently determined growth rates and inoculum sizes,
assuming no inoculum size effect for exponential (- - -) and stationary phase
(………) inocula. _163.gif)
_163.gif){kind=small}
)
inocula were incubated in (a) TSB, (b) TSB with 1.2 M NaCl, (c) TSB with 1.6 M
NaCl. The histogram bin size for each condition is equal to the corresponding
generation time. _163.gif)
).
Predicted values calculated from a Poisson distribution are also given (
).
_163.gif)
)
spent medium (50% v/v). Solid lines are drawn through the mean of each data
set. The dashed line indicates the predicted response assuming no inoculum
size effect. 