[Emerging Infectious Diseases * Volume 3 * Number 4 * October - December 1997]

Special Issue

Quantitative Microbiology: A Basis for Food Safety

T. A. McMeekin, J. Brown, K. Krist, D. Miles, K. Neumeyer, D.S. Nichols, J.
Olley, K. Presser, D. A. Ratkowsky, T. Ross, M. Salter, and S. Soontranon
University of Tasmania, Hobart, Australia
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      Because microorganisms are easily dispersed, display
      physiologic diversity, and tolerate extreme conditions, they
      are ubiquitous and may contaminate and grow in many food
      products. The behavior of microbial populations in foods
      (growth, survival, or death) is determined by the properties of
      the food (e.g., water activity and pH) and the storage
      conditions (e.g., temperature, relative humidity, and
      atmosphere). The effect of these properties can be predicted by
      mathematical models derived from quantitative studies on
      microbial populations. Temperature abuse is a major factor
      contributing to foodborne disease; monitoring temperature
      history during food processing, distribution, and storage is a
      simple, effective means to reduce the incidence of food
      poisoning. Interpretation of temperature profiles by computer
      programs based on predictive models allows informed decisions
      on the shelf life and safety of foods. In- or on-package
      temperature indicators require further development to
      accurately predict microbial behavior. We suggest a basis for a
      "universal" temperature indicator. This article emphasizes the
      need to combine kinetic and probability approaches to modeling
      and suggests a method to define the bacterial growth/no growth
      interface. Advances in controlling foodborne pathogens depend
      on understanding the pathogens' physiologic responses to growth
      constraints, including constraints conferring increased
      survival capacity.

Ensuring the microbial safety and shelf life of foods depends on minimizing
the initial level of microbial contamination, preventing or limiting the
rate of microbial growth, or destroying microbial populations. With many
foods, these strategies have been practiced successfully for thousands of
years. However, in the last decade, the incidence of foodborne disease has
increased in the industrialized world (1), despite the introduction of the
Hazard Analysis and Critical Control Points (HACCP) concept and the
promulgation of regulations in food safety. The increased incidence of
foodborne disease is caused by changes in agricultural and food processing
practices, increasing international trade in foods, and social changes
(which include changed eating habits and increased population mobility)
(2).

This article develops the propositions that available quantitative
information, properly applied, is a basis for improved food safety; the
information available, largely an empiric description of microbial behavior
in foods, highlights a lack of understanding of the physiology of foodborne
pathogens; and knowledge of the physiology may lead to more precise control
of foodborne bacteria and novel protocols to ensure the microbiologic
safety of foods.

Characteristics of Bacteria

Bacteria have inhabited the earth for approximately three and a half
billion years and have colonized almost every conceivable habitat (3). In
fact, the development of microbial populations is probably precluded only
when liquid water is absent or conditions are so extreme that rapid
denaturation of proteins outpaces their rate of replacement. The major
characteristics that underpin the success of prokaryotes are small size and
ease of dispersal, physiologic diversity, and tolerance of extreme
conditions (4). The temperature range over which microbial populations
develop is -12C (the temperature at which intracellular water freezes) to
+112C (the temperature at which liquid water is maintained only under
elevated pressure). The pH range is pH 1 to pH 12, and the salinity range
is zero to saturated (4).

Langeveld et al. (5), who studied microbial development in biofilms in a
tubular heat exchanger used to pasteurize milk, report the exploitation of
different ecologic niches by bacteria. Through the ascending temperature
range of the tube (~20C to ~90C), the dominant microbiota changed from
gram-negative bacteria such as Acinetobacter, to coliforms to Streptococcus
thermophilus to thermophilic bacilli. At the highest temperature, the wall
of the exchanger was colonized by Thermus thermophilus. Thus, it appears
that contaminants deposited along the length of the tube were selected by
the in situ temperature, with the fastest-growing organism dominating.

Factors Affecting Microbial Behavior in Foods

Most studies in food microbiology are concerned with the rapid growth of
populations, but in many ecosystems, the survival characteristics of the
population also need to be considered. The longevity of bacterial spores
and their resistance to harsh conditions are well documented. However, the
ability of vegetative cells to resist stressful conditions is increasingly
recognized as an important ecologic trait (6). Attention also needs to be
given to relatively slow-growing populations in various situations, e.g.,
when the shelf life of a product is extended by control of rapidly growing
spoilage organisms. The behavior of foodborne microorganisms, be it the
growth or death of microbial populations, is based on the time of exposure
to environmental factors affecting population development; for example,
equivalent kills of bacteria in milk are achieved by low temperaturelong
time pasteurization (60C/30 min) and high temperatureshort time
pasteurization (72C/15 sec). When populations are in the biokinetic range,
the rate at which they develop is determined by factors such as
temperature, water availability, and pH applied in food preservation
procedures. The extent of microbial growth is a function of the time the
population is exposed to combinations of intrinsic food properties (e.g.,
salt concentration and acidity) and extrinsic storage conditions (e.g.,
temperature, relative humidity, and gaseous atmosphere).

Different factors assume dominance in different foods and preservation
strategies. In many foods, the full preservation potential of a single
property is restricted because of considerations related to the esthetic,
organoleptic, and nutritional properties of the product. However, several
properties or conditions may be combined to provide a desired level of
stability (7). In situations where the preservation strategy is designed to
slow the rate of population growth, the effect will always be increased by
storage temperature. Temperature control in processing, distribution, and
storage (the cold chain) is crucial to ensure the adequate shelf life and
safety of many common foods, including meat, fish, poultry, and milk. Newer
technologies, including modified atmosphere packaging and sophisticated
products such as sous-vide meals, do not obviate the need for strict
temperature control. Indeed, the requirement for vigilance increases with
increased shelf life and the possibility of growth of psychrotrophic
pathogens over an extended period.

Predictive Microbiology

Predictive microbiology involves knowledge of microbial growth responses to
environmental factors summarized as equations or mathematical models. The
raw data and models may be stored in a database from which the information
can be retrieved and used to interpret the effect of processing and
distribution practices on microbial proliferation. Coupled with information
on environmental history during processing and storage, predictive
microbiology provides precision in making decisions on the microbiologic
safety and quality of foods. The term "quantitative microbial ecology" has
been suggested as an alternative to "predictive microbiology" (8).

The development, validation, and application of predictive microbiology has
been extensively reviewed in the last decade (9,10). Modeling studies have
concentrated on descriptions of the effect of constraints on microbial
growth (rather than survival or death), often using a kinetic model
approach (rather than probability modeling) and most often describing the
effect of temperature as the sole or one of a number of controlling
factors. For example, the temperature dependence model for growth of
Clostridium botulinum demonstrated a good fit to data, but the authors
noted that "care must be taken at extremes of growth, as no growth may be
registered in a situation where growth is indeed possible but has a low
probability" (11).

The emphasis in modeling efforts on temperature (often in combination with
other factors) may be justified, given its crucial role in the safe
distribution and storage of foods. Surveys carried out over several decades
in the United Kingdom, United States, Canada, and Australia point to the
predominant role of temperature abuse in outbreaks of foodborne disease
(12-14).

Problems with Predictive Microbiology and Research Needs

Several commonly perceived problems with predictive modeling (8) are
reviewed below. While considerable progress has been made in defining
philosophic approaches and experimental protocols for growth model
development and many models have been developed and published, more
validation studies are required, particularly involving independent and
industry-based trials. More emphasis should be placed on modeling the death
kinetics of foodborne pathogens with low infective doses.

Measurement of environmental factors (e.g., temperature) can be achieved
with precision, but in some situations, (e.g., in chilling of meat
carcasses), it is more difficult. Location of the sensor can be an
important consideration (15,16). In meat chilling, where control of
microbial development is a function of the combined effects of falling
temperature and water activity, development of a technique to measure water
activity in situ at the carcass surface would provide valuable information.
Furthermore, development of techniques to measure constraints such as water
activity, pH, or redox potential on a microscale might provide useful
information for a complex food such as salami. This would allow definition
of the role of the microenvironment in determining microbial behavior. The
concept of a microenvironment is well developed in soil microbiology (17)
but has been neglected in food microbiology.

The inherent variability of response times (generation time and lag phase
duration) as an issue in predictive microbiology was first raised by
Ratkowsky et al. (18), who related the variance of responses on a
transformed rate scale such as V(k) or V(lnk) to the variances of
responses on a time (0) scale. The variance was shown to be proportional to
the square or cube of the response time. It was later confirmed (19-21)
that nonnormal gamma and inverse Gaussian distributions described the
distribution of response times in predictive microbiology, which indicate
that variance is proportional to the square or cube of the response time,
respectively.

The practical implication of these findings for the application of kinetic
models is that inherent biologic variability increases markedly with
increasing response times, and thus the confidence limits associated with
predictions also increase markedly. However, if the probability
distribution of the response time is known, one can determine the
probability that an organism will grow more quickly than a predicted
response time (21). Thus, kinetic models are appropriate to describe
consistent microbial growth responses, but under extreme conditions a
probability approach may be required.

Models are normally developed under static conditions (growth rates and lag
times are measured at a series of set temperatures, water activity values,
and pH levels), and the results are combined to describe the effects of
each factor or a combination of factors on population development.
Subsequently, models must be validated in foods under conditions that mimic
situations encountered in normal practice, e.g., decreasing temperature and
water activity during active chilling of meat carcasses or fluctuating
temperatures during the distribution and storage of many food commodities.

Shaw (22) and later several other authors (23-26) reported on the effect of
fluctuating temperatures. Depending on the magnitude of the temperature
deviation, the organism may change its rate of growth to a rate
characteristic of the new temperature, or it may stop growing if a lag
phase is introduced. In both situations, Salmonella Typhimurium has
responded nearly as predicted by the model (24,25). Baranyi et al. (26)
presented similar results for the spoilage bacterium Brocothrix
thermosphacta. When cycled between 25C and 5C, the model predicted
behavior well in both the growth rate and lag phase duration. However, a
temperature shift from 25C to 3C caused deviations from model predictions
due to decline in viable cell numbers or extended lag phases. During the
final extended phase of growth at 2.8C, the rate resumed at that
predicted. Baranyi et al. (26) also examined the perceived problem of
modeling lag phase duration. The difficulty in predicting lag phase
duration in foods is not due to the lack of a suitable model: the
difficulty comes from the lack of knowledge of the physiologic status of
the organisms contaminating the food. The organisms may include cells that
are actively growing, exhibiting a physiologic lag phase, damaged and under
repair, exhibiting physiologic (endospores) or exogenous dormancy (VNC
cells), damaged but unable to reproduce because of ineffective repair
mechanisms, and dead. At least part of the confusion surrounding the
measurement of lag phase duration arises from experiments in which inocula
of different physiologic statuses were used (27,28).

Methods to define the physiologic status of foodborne contaminants under
various conditions need to be developed. This will require observations on
individual cells or small populations of cells either directly by
microscopy or an indicator of single-cell metabolic activity (26).
Luminescent Salmonella strains have been used as real-time reporters of
growth and recovery from sublethal injury (29). Alternatively, a parameter
to describe the suitability of cells to grow in a new environment may be
incorporated in the model (26).

Current Status of Predictive Microbiology

Some problems with predictive microbiology have been discussed, and, for
each, needed research has been suggested. Opinions vary on the efficacy of
models to predict outcomes under real life conditions. At one end of the
scale, accuracy such as that described for the growth of Pseudomonas in
minced beef (Figure 1) can be obtained in trials conducted independently of
the laboratory in which the model was developed. At the other end, models
developed in laboratory broth systems have been reported to be
inappropriate to describe growth on food (30). Dalgaard (31) reported
similar discrepancies and suggested an iterative approach to model
development using food, rather than laboratory media, as the growth
substrate for model development. Such reports emphasize the need for
rigorous validation of models under practical conditions. Deviations from
predictions do not necessarily imply that the model is defective but more
likely that knowledge of some food ecosystems is incomplete and factors
other than those used in model development have an effect on microbial
behavior.

[Figure Not Available in ASCII]

 Figure 1. Validation of Pseudomonas predictor in minced beef . Printed
 with permission of G. Thomson, Defence Force Food Science Centre,
 Scottsdale, Tasmania, Australia.

The common theme of the problems in predictive microbiology discussed above
is that of uncertaintyuncertainty in terms of the starting conditions
(e.g., initial microbial numbers and types) and the microbial response in a
give

n or changing environment. Uncertainty translates to variability if the
distribution of response times is understood and the variance can be
described. As we have indicated above, the variability associated with very
long response times limits the utility of kinetic models and requires a
probability approach. Thus, while in the last decade predictive modelers
were justified in their selection of temperature as a primary factor to
model in kinetic approaches, the next decade may see a return to
probability modeling as pioneered by Genigeorgis (32) and Roberts (33).
This shift will derive impetus from the emergence of dangerous pathogens
with very low infective doses, and continued kinetic modeling will
concentrate on survival and death rather than growth of populations.

The first kinetic death model to find widespread use in the food industry
was for thermal destruction (34). One can consider a model describing a
12-log cycle reduction in C. botulinum spores in a short time with
considerable certainty. However, as we move toward less severe processes
with longer response times and the added complications of "shoulders" and
"tails" to define the growth/no growth interface, biologic variability will
again dictate a probability approach to describe the survival and slow
decline of microbial populations.

Defining the Growth/No Growth Interface

Because growth of pathogenic bacteria in foods always increases the risk
for foodborne disease, defining the conditions at which no growth is
possible is of considerable practical significance for food manufacturers
and regulators. Bacterial growth/no growth interface models quantify the
combined effect of various hurdles on the probability of growth and define
combinations at which the growth rate is zero. Increasing the level of one
or more hurdles at the interface by only a small amount will significantly
increase the probability of "fail safe" events and decrease the probability
that a few cells in the population will resolve the lag phase and begin to
grow (a "fail dangerous" event) (7). The growth/no growth interface also
has great physiologic significance because at that point biosynthetic
processes are insufficient to support population growth, and survival
mechanisms are in place.

A procedure to derive the interface was proposed by Ratkowsky and Ross
(35); it employs a logistic regression model to define the probability of
growth as a function of one or more controling environmental factors. From
this model, the boundary between growth and no growth, at some chosen level
of probability, can be determined. The form of the expression containing
the growth limiting factors is suggested by a kinetic model, while the
response at a given combination of factors is either presence or absence
(i.e., growth/no growth) or probabilistic (i.e., the fraction of positive
responses in n trials). This approach represents an integration of
probability and kinetic approaches to predictive modeling.

Microbial Responses to Stress and Microbial Physiology

Bacteria have physiologic mechanisms enabling them to survive in
environments that preclude their growth. While some tolerance to
environmental insults is adaptive, a wide range of protective mechanisms is
induced when cells enter a stationary phase or become starved. These
phenomena are under the control of the rpoS gene, which codes for a
stationary-phasespecific sigma factor, expression of which triggers the
development of a semidormant state in which bacteria can better resist
multiple physical challenges (36). This factor and the gene products whose
expression it controls are of vital significance to food microbiology; they
form the basis for a global stress response in which one stress can confer
protection to a wide range of other stresses. Under the influence of this
factor, bacterial cells respond very quickly to unfavorable changes in
their environment. Often these responses are phenotypic and remain in place
only during stress (37).

Low pH Tolerance

Brown et al. (37) demonstrated "acid habituation" (38), a phenotypic
response to an environmental insult, for five strains of Escherichia coli.
These strains showed a wide range of intrinsic acid tolerance, which for
each strain was enhanced by exposure to nonlethal acidity (pH 5) before
exposure to a lethal acid challenge (pH 3). Neutralization of the growth
medium partially reversed tolerance to acid stress, underlining that acid
habituation is a phenotypic response. Furthermore, acid tolerance was
correlated with changes in the fatty acid composition of the cell membrane.
During acid habituation, monounsaturated fatty acids (16:1w7c and 18:1w7c)
in the phospholipids of E. coli were either converted to their cyclopropane
derivatives (cy17:0 and cy19:0) or replaced by saturated fatty acids. The
degree of acid tolerance of the five strains of E. coli was highly
correlated with the membrane cyclopropane fatty acid content, which may
enhance the survival of cells exposed to low pH.

Low Water Activity Tolerance

Bacterial cells, when confronted by lowered water activity, regulate the
internal environment by rapidly accumulating compatible solutes such as
glycine betaine or carnitine (39). The solutes, which may be scavenged by
the cell, exert their influence at very low concentrations; the effect is
demonstrated both in limits and rate growth. These compounds appear also to
provide cryotolerance as well as osmotolerance (40; Figure 2).

 [Figure Not Available in ASCII]
 Figure 2. Effect of betaine on the growth of Escherichia coli
 in glucose-minimal medium. Without added NaCl the growth
 rate yield and minimum growth temperature are the same with
 and without betaine. With 4% NaCl the growth rate and yield
 are lower without betaine and the actual minimum temperature
 for growth is approximately 9C lower than with betaine
 (K. Krist, unpub. data).

Energy Diversion

Microbial responses to stressful conditions may constitute a drain on the
energy resources of the cell, e.g., in relation to the accumulation of
compatible solutes (41). Knochel and Gould (42) argued "that restriction of
the availability of energy will interfere with a cell's reaction to osmotic
stress." The energy diversion hypothesis was supported by McMeekin et al.
(9) on the basis of observations on the growth of Staphylococcus xylosus at
nine different levels of water activity. Though Tmin, the theoretic minimum
temperature for growth, remained constant, the actual minimum temperature
at which growth was observed increased with decreasing water activity,
suggesting energy expenditure to cope with aw stress.

Krist and Ross (unpub. data), however, challenged this explanation because
of findings from growth rate and yield experiments on E. coli growing in a
glucose-limited minimal minerals medium. With both decreasing temperature
and water activity, the growth rate declined gradually, but the yield was
not greatly affected until close to the point where growth ceased. As the
substrate was converted to the same amount of biomass, this suggests that
the stresses imposed by suboptimal temperature or water activity are not a
major drain on the cell's energy reserves. Compatible solutes likely
ameliorate the effect of both factors by maintaining enzymes in an active
configuration (39). With pH, the growth rate of many organisms is
unaffected across a wide range of pH values. To maintain intracellular pH,
the cell uses considerable energy to export protons (43), and thus it is
anticipated that yield will be affected.

Increasing knowledge of the physiologic response of bacterial cells to
individual constraints and combinations of constraints will provide greater
precision in defining growth-limiting conditions and possibly allow
development of novel protocols to ensure the microbial safety of foods. As
an example, the remarkable effect of compatible solutes on the growth rate
and growth range conditions is an obvious advantage for bacteria growing
under stressful conditions (Figure 2). Compatible solutes, such as betaine
and carnitine, are widely distributed in foods of plant and animal origin
and are easily available to bacteria and rapidly taken up by specific
transport mechanisms (39,40). It is unlikely that growth might be
controlled by "creating a hostile environment devoid of osmolytes," as
suggested by Smith (44). However, it might be possible to use the specific
uptake mechanism to deliver a compatible solute analogue with lethal
effects on the cell. Alternatively, if the cell is moved from an
environment in which growth is possible to one where growth ceases,
compatible solutes may also allow enzymic reactions to continue within the
cell, depleting energy reserves and inducing a greatly extended lag phase
or death. This hypothesis is supported by the observations of many authors
that survival is better at low rather than ambient temperatures. For
example, Clavero and Beuchat (45) state, "Regardless of the pH and aw,
survival of E. coli O157:H7 was better at 5C than at 20C or 30C."
Furthermore, preliminary experiments in this laboratory suggest that E.
coli declines more rapidly in the presence of betaine than in its absence
(Krist, unpub. data).

Application of Predictive Models

The incorporation of predictive models into devices such as temperature
loggers has been described for E. coli (46) and Pseudomonas (47), as has
the development of expert systems from predictive modeling databases
(48,49).

The current food poisoning crisis indicates that existing quantitative
information on microbial growth, survival, and death, if properly applied,
would have an immediate impact on the incidence of foodborne disease in the
industrialized world. Even without the synthesis of data into mathematical
models, simply logging the temperature history of food processing,
distribution, and storage operations would provide much useful information.
Loggers provide a hard copy of a temperature profile in real time and thus
evidence of temperature abuse and the source of the abuse.

For loggers with appropriate software (46,47), the temperature profile may
be interpreted in terms of microbial growth. However, the interpretation
must be based on an informed analysis of the temperature history by a
trained operator. The operator may, for example, be required to enter
default values for initial bacterial numbers or provide an estimate of lag
phase duration under specified conditions. Estimates of both imply general
knowledge of food microbiology and specific knowledge of the process and
products under consideration. The equivalence of an estimate of microbial
growth derived from temperature profile to that obtained from conventional
microbiologic criteria may also be necessary (15).

An alternative to the use of temperature loggers is the development of in-
or on-package temperature tags as recommended in the U.S. Food Safety
Initiative draft document Food Safety from Farm to Table (50). With
temperature tags, informed interpretation is not required because abuse is
indicated directly by the tag response. Therefore, the tag must indicate
the significance of the environmental history for microbial behavior. The
time/temperature tags available are based on physical or chemical changes
that follow Arrhenius kinetics (9). While these may give a reasonable
approximation of microbial growth in the normal range, the deviation of
microbial responses becomes increasingly large as conditions move from
normal to stressful. The intriguing possibility of a universal
time/temperature indicator was flagged (51) on the basis of observations
made of temperature effects on foodborne pathogens in this laboratory and
by Snyder (52). The universal indicator is based on a relationship that
describes the maximum specific growth rate of a continuum of organisms from
psychrophiles to thermophiles in terms of Arrhenius kinetics with an
apparent activation energy of ~80 kJ/mole. This value can be related to the
activation energy/growth rate at any other temperature by a relative rate
function derived from Belehradek (square root) kinetics.

Conclusions

We have argued that a thorough understanding of microbial ecology and
physiology offers the best opportunity to control microbial populations in
food and reverse the upward trend in the incidence of foodborne disease.
Many food preservation strategies have their origin in empirical
observations practiced for thousands of years. The systematic collection
and collation of data on microbial behavior in foods spawned the discipline
of food microbiology, within which predictive microbiology (quantitative
microbial ecology) has accelerated our understanding of the microbial
ecology of foodborne bacteria. Studies in microbial physiology will further
enhance our knowledge and offer new possibilities for food preservation.

The most disturbing aspect of the current crisis is that simple application
of existing knowledge would lead to a marked reduction in the incidence of
foodborne disease. Education of food handlers and consumers in basic
hygiene and the consequences of temperature abuse is urgently needed as is
a greater depth of understanding for those in technical positions in the
food industry or those with regulatory responsibilities. Furthermore, an
appreciation of the need for shared responsibility for food safety within
all sectors of the continuum from farm to table, including the consumer,
has to be developed. The U.S. Food Safety Initiative draft document
emphasizes this point, as does the structure of the Australian Meat
Research Corporation's Microbial Food Safety Key Program (53).

Address for correspondence: Professor T.A. McMeekin, Department of
Agricultural Science, University of Tasmania, GPO Box 252-54, Hobart, TAS
7001, Australia; fax: 61-3-62-26-2642; e-mail: Tom.McMeekin@utas.edu.au.

Acknowledgments

The work described in this manuscript was funded largely by the Australian
Meat Research Corporation (MRC) through a Core Funding Project and the
Microbial Food Safety Key Program. The authors are indebted to Dr. Stefan
Fabiansson for his support and encouragement of research into the ecology
and physiology of foodborne pathogens.

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