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International Journal of Food Microbiology 128 (2008) 2–9
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International Journal of Food Microbiology
j ourna l homepage: www.e lsev ie r.com/ locate / i j foodmicro
The future of predictive microbiology: Strategic research, innovative applications andgreat expectations
Tom McMeekin ⁎, John Bowman, Olivia McQuestin, Lyndal Mellefont, Tom Ross, Mark TamplinFood Safety Centre, School of Agricultural Science and Tasmanian Institute of Agricultural Research, University of Tasmania, Private Bag 54, Hobart, Tasmania, Australia 7001
⁎ Corresponding author. Tel.: +61 3 62266280; fax: +6E-mail address: [email protected] (T. McM
0168-1605/$ – see front matter © 2008 Elsevier B.V. Aldoi:10.1016/j.ijfoodmicro.2008.06.026
A B S T R A C T
A R T I C L E I N F OArticle history:
This paper considers the futu Received 1 February 2008Received in revised form 22 May 2008Accepted 29 June 2008Keywords:Predictive microbiologyModel buildingStrategic researchEnabling technologyValue analysisModelling food and other ecosystemsMicrobial persistence and recovery
re of predictivemicrobiology byexploring the balance that exists between science,applications and expectations. Attention is drawn to the development of predictive microbiology as a sub-discipline of food microbiology and of technologies that are required for its applications, including a recentlydeveloped biological indicator. As we move into the era of systems biology, in which physiological andmolecular information will be increasingly available for incorporation into models, predictive microbiologistswill be faced with new experimental and data handling challenges. Overcoming these hurdles may be assistedby interacting with microbiologists and mathematicians developing models to describe the microbial role inecosystems other than food. Coupled with a commitment to maintain strategic research, as well as to developinnovative technologies, the future of predictive microbiology looks set to fulfil “great expectations”.
© 2008 Elsevier B.V. All rights reserved.
1. Introduction
1.1. Strategic research, innovative applications and great expectations inthe context of predictive microbiology
The Australian Research Council (www.arc.gov.au) defines “orientedstrategic basic research” (herein abbreviated to “strategic research”) as“research carried out with the expectation that it will produce a broadbase of knowledge likely to form the background to the solution ofrecognised or expected current or future problems or possibilities”. Thekey term, “innovative applications”, is defined as “the use to whichsomething, new or improved, may be put, for example, scientificknowledge, especially in industry, and equipment for the purpose”.Thesewere chosenas elements of the title to indicate that a combinationof science and technology is essential for effective application ofpredictive microbiology (McMeekin et al., 2005).
The key term, “expectations”, is defined as “the act of lookingforward, something hoped for” or (as in Charles Dickens' novel GreatExpectations “prospects for inheritance”) and, as such, represents thepractical outcomes of predictive microbiology research. This is veryrelevant to current consumer expectations of food, as clearly stated byCarol Brookins at the Global Food and Agriculture Summit in 1999:
1 3 62267444.eekin).
l rights reserved.
“Consumers are demanding miracle foods that are totally natural, havezero calories, zero fats and cholesterol, delicious taste, total nutrition,low price, environmentally friendly production, ‘green’ packaging ….and that guarantee perfect bodies, romance and immortality.” This iseven more relevant in 2008.
A related term, “expected value”, is “the predicted value of avariable calculated as all probable values multiplied by the probabilityof its occurrence”. Clearly, the concurrence of expected and measuredvalues provides an estimate of the value of a predictive model or “ameasure of its worth, desirability and utility”. Quantifying the value ofpredictive microbiology research has rarely, if ever, been attemptedbefore, in other than a rudimentary manner, but herein we willdescribe the outcomes of a value analysis—“a systematic and criticalanalysis of a process or every feature of a product”—carried out by theCentre for International Economics, Canberra, Australia, for Meat andLivestock Australia (www.mla.com.au).
1.2. The mindset engendered by predictive microbiology
In several earlier publications we have drawn attention to thewritings of Scott (1937) as the first clear enunciation of the concept ofpredictive microbiology and, in the context of this paper, it seemsreasonable to mark this as an example of creative and innovativethinking, raising expectations of changing agar-based approaches tomicrobial enumeration and, as a result, enabling proactive rather thanretrospective estimation of the microbial safety and quality of foods.
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However, without appropriate technology such as data loggers,computers and the internet, Scott’s concept was destined to remain“virtual” until those innovations were developed and had becomereadily available. Thus, the era of “modern” predictive microbiologycan be traced from the early 1960s when the science and thetechnology required for predictive microbiology came into balance.
Despite the fact that Scott’s predictive modelling concept reportedin 1937was not a practical reality until 30–40 years later, the gestationperiod should have allowed early proponents to move towards anunderstanding of the potential impact of the new approach. Expecta-tions of the transition from qualitative to quantitative microbialecology of foods would have included reduced uncertainty, possibi-lities becoming probabilities and variability in response timescharacterised by appropriate distributions (McMeekin, 2007). Theseeffects were already well understood by insurance companies, whichused the Gompertz model (Gompertz, 1825) to predict life expectancy(Gross and Clark, 1975; Teriokhin et al., 2004; Bongaarts, 2005). Stockmarket analysts whose life (or work) expectancy is likely to be highlycorrelated with volatility in the market, will also be acutely aware ofuncertainty. Variability is a biological reality described by a distribu-tion which allows its magnitude to be estimated, but not altered, forany set of conditions. Uncertainty, on the other hand, can be reducedby collection and analysis of more data, but it always retains thepotential to be activated, often as a result of human error.
A more practical prospect from the introduction of enabling tech-nologies, that would have grabbed the imagination of early predictivemicrobiologists, was the expectation that estimates of shelf life andsafety would be available in a compressed time frame; although theprospect of real-time reporting of food safety and shelf life in the 1960smay have been too large a leap of faith to comprehend.
2. Why model, who models and how are models built?
A model can be defined as “the description of a system, theory, orphenomenon that accounts for its known or inferred properties andmay be used for further study of its characteristics”. While, in commonusage, a model is often a smaller replica of a real object, in science,engineering, finance etc., the model is an often simplified descriptionof relationships between observations of the system (responses) andthe factors that are believed to cause the observed responses. Thatdescription can be expressed in words or expressed quantitatively inone or more mathematical relationships or equations. Thus, amathematical model can simply describe a collection of data or mayrepresent a hypothesis or series of hypotheses about underlyingrelationships among the independent variables that lead to theobservations or data.1 The first approach is often termed an ‘empirical’model, while the latter is described as ‘mechanistic’. Both approacheshave utility: the first simply to summarise data and the latter tosummarise “understanding” or knowledge. Either can be used topredict the response of the system to changes in the variables.
Few models are truly mechanistic, but models can at least beformulated to reflect and embody our current knowledge and/orhypotheses concerning the system being studied. In this way,predictions from the model can have utility not only to predict theoutcomes of a set of circumstances, but also to test the hypothesesembodied in the model and so to revise and improve the model andour hypotheses if the predictions do not match the observations. Thisis the “traditional” scientific method. Thus, models not only provide aframework to summarise our experience about what will happenunder a given set of circumstances, but also a systematic way toimprove our understanding of the underlying processes. With thatunderstanding we are better able to predict, or control, or improve theperformance of a system intelligently, whether it be the stock market,
1 A series of mathematical equations that are used to solve a problem (usuallyinvolving repetition of one or more operations) can also be called an algorithm.
traffic flow, agricultural soil fertility, global weather or the micro-biological stability of foods.
Specific approaches to model building will differ from discipline todiscipline and even within disciplines, depending on the system beingmodelled. Nonetheless, all models seek to link observations to thevariables believed to control themso that allmodels require quantitativedata on the magnitude of responses and the variables believed toinfluence them. Mathematical techniques to summarise or identify therelationships between independent and dependant variables are wellestablished and documented in many texts, as are principles of expe-rimental design to generate the needed data and discern the relation-ships. Issues of relevance include the range of variables studied and thelimits of applicability of model predictions, correct specification of errorbehaviour (the “stochastic assumption”), model parameterisation andparsimony (not having too many explanatory variables), etc. (seeRatkowsky, 1993). A wide range of powerful regression software toolsis now also readily available for use on laptop computers to assist in thedevelopment of reliable and robust mathematical models. Access tosuch powerful software has increased the use of modelling approachesto a wide range of human endeavours.
3. Predictive models: enabling science
Ample evidence is provided in the history of science to demon-strate that advances occur from making observations as a basis forhypotheses on which general rules describing natural phenomena areconstructed. In food microbiology, qualitative approaches to describemicrobial population responses predominated until the advent of“modern” predictive microbiology which allowed us to advance to theera of the quantitative microbial ecology of foods. In turn, thephenomenological descriptions may be strengthened by suppositions,based on initial facts, leading to better defined hypotheses and,perhaps, to mechanistic insights and underlying theories to char-acterise the phenomena involved.
Inpredictivemicrobiology,mostmodels used to date are empirical innature ranging from “black box” approaches, such as artificial neuralnetworks, to “grey box” models which include a priori knowledge todescribe well characterised microbial responses to environmentalfactors (Geeraerd et al., 2004). Progress towards a thermodynamicallybased temperature dependencemodelwas reported by Ratkowsky et al.(2005) and this has moved our state of knowledge closer to a mecha-nistic basis for temperature effects on microbial growth rates, based onreversible protein denaturation at both low and high temperatures.
Similarly, models of the effect of other constraints, such as pH andwater activity, on microbial growth rate, when combined with physio-logical studies, provide mechanistic clues. An example is the severedrain on energy reserves through induction of ATPase to expel protonsfrom cells in a low pH environment, thusmarkedly reducing cell yield atdecreasingpH levels (Krist et al.,1998). Themodel formmayalsoprovideevidence to describe the nature of combined effects on microbialgrowth, the core of the classical hurdle concept introduced by Leistnerand colleagues as the basis of “mild”processes for foodpreservation [seeLeistner and Gorris (1995) and Leistner (2000) for developments inapplication of the concept]. The prevailing consensus is that hurdlesinteract additively in reducing growth rate rather than in much soughtafter synergisms (Lambert and Bidlas, 2007).
The preamble above on models is provided to stake a claim for thecentral role of strategic research in predictive microbiology. Before theadvent of quantitativemicrobial ecology approaches, we relied heavilyon qualitative or semi-quantitative approaches, usually applied indiscrete experiments, to specific situations. For example, “The effect ofenvironmental conditions A, B and C (or antimicrobials D, E and F) onthe growth and inactivation of microorganisms P, Q and R in foodsprocessed and stored under conditions X, Y and Z” could be a generictitle applicable to many publications in the recent food microbiologyhistory. Analysis of non-review paper titles published in the Journal of
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Food Protection and the International Journal of Food Microbiology in2007 reveal that our generic title is applicable to ~14% and 5.5%,respectively. Effectively, this approach confirms that empiricismcontinues to be used in the search to uncover combinations of controlstrategies with benign effects on the sensory or nutritional propertiesof the food and the health and well being of consumers. Serendipity iswell recognised as important in scientific advances, but the opportu-nity it offers needs to be recognised and, therefore, should beconsidered as an observational starting point fromwhichmechanismsand applications are developed. Systematically “filling in the dots”between empirical and mechanistic descriptions is a longer, but morecertain, route to secure scientific advances.
The innovation arising from predictive models is that they allowthe interpretation of the effect of processing, distribution and storagepractices on the extent of growth or death of the organism for whichthe model was developed, i.e. microbial behaviour is predictable onthe basis of measured environmental parameters. Additionally, oneenvironmental history profile may be applied to several organisms, ifappropriate models are available. With addition of the modellinginnovation, the proposed generic title of our phantom paper wouldchange to “Mathematical models describing the effect of environ-mental conditions A, B and C (or antimicrobials D, E and F) on thegrowth and inactivation of microorganisms P, Q and R in foodsprocessed and stored under conditions X, Y and Z”. However, thequantitative approach would require much greater experimentaleffort and analysis with attention to the “rules” of modelling outlinedin Section 2 and described in detail by Ratkowsky (1993), experi-mental design, protocols, etc. (McMeekin et al., 1993).
The process of model building is not simply a matter of finding anequation to describe a single or a few data sets. In fact, the pattern ofmicrobial responses observed in relation to environmental variables islikely to provide more useful information than a curve fitting exercisewhich will provide a unique solution with little, or no, utility beyondthe experimental conditions under which the data were generated.Confidence in predictions is greatly increased when models fromdifferent sources provide comparable estimates of environmentalinfluences on microbial population behaviour. However, as with Scott(1937), practical application of models in foods still requirestechnological “fixes” which we will consider in the following section.
On the other side of strategic modelling science we encounter theinterfacewithmicrobial physiology (“-omics” in current parlance), thepriorities for the study of which are often set on the basis of modellingstudies, e.g. the fascinating region close to the growth/no growthboundary (Ratkowsky and Ross, 1995; McMeekin et al., 2002). Fromphysiological and molecular studies, one can predict both theidentification of specific targets for control of microorganisms andpredictivemarkers to identify the onset of significant biological eventssuch as sporulation and germination (Oomes et al., 2007).
As the emergence of modern predictivemicrobiology depended onthe invention of computers, identifying the mechanisms of physiolo-gical responses to environmental stimuli (i.e. systemsbiology) requiressophisticated computational tools to sort through huge knowledgedatabases and “map” complex networks. Fortunately, such tools arerapidly emerging as a result of accelerated research in networksciences, a discipline that describes networks which are comprised oflinks (edges), nodes and “hubs” that influence responses in virtually allsystems [see Barabasi (2002)].
4. Technological innovations: enabling devices
Before considering types of devices to monitor environmental con-ditions, it is important to understand the pivotal role of databases suchas ComBase (www.combase.cc) which allow us to “join the dots” or“network”, thereby moving from discrete data sets useful in a stand-alone situation, possibly compared with similar data obtained bybrowsing the literature, to a much more knowledge powerful compen-
dium of stored information to which new data may be added goingforward. Importantly,much information ismade freely available to usersthrough such databases which are a virtual science network, geared tothe refinement and dissemination of knowledge and increasinglyobviate the need for reinvention of expensive experimental wheels.
4.1. Traceability technologies
Traceability technology, such as barcoding or radio frequency iden-tification technology (RFID), allows a product to be followed step by stepor to be recognised as an object towhich an original and unique codewasapplied. This authenticates that it is genuine or has an undisputed origin,provided that illegal product substitution has not occurred. Traceabilityand authenticity technologies may be as simple as a “one up, one down”paper trail by which each business operator can identify their supplierandcustomerand, ondemand, provide this information to the competentauthorities (McMeekin et al., 2006a). An early example of authenticitybeing confirmed by a pen and paper systemwas used for N100 years bythe AmericanWalthamWatch Company to record the details of millionsof timepieces against a unique serial number stamped in themechanism.Thiswouldhavebeenuseful for specialist access byowners and collectorswishing to authenticate the potential value of their horological devices,but now that the information is digitised, general access has been greatlyincreased (see www.walthamwatchcompany.com).
For most consumers, barcodes, machine readable codes in the formof a pattern of stripes printed on and identifying a commodity, especiallyfor stock control, will be the most commonly recognised traceabilitytechnology. Variants on barcodes include information dense microbialdots (www.burntsidepartners.com) and the fortuitous dots code de-scribed by Neimeyer-Stein (2006).
Electronic chain distribution monitoring systems, many based onRFID technology, have attracted significant attention in the quest foradvances in product monitoring in the logistics industry. In addition toproduct identification via barcodes, many systems also allowtemperature measurements to be transmitted in real-time at anypoint in the supply chain (Frederiksen et al., 2002). Thus, the additionof another function has increased the utility of the system by allowingidentification of weak points in the cold chain. An importantconsideration is the need for complementarity of RFID technologiesthrough common standards and a common identifier, the Global TradeItem Number (see www.epcglobalinc.org) (McMeekin et al., 2006a).
4.2. Temperature function integration (TFI) and temperature monitors
Temperature is a major factor that determines the rate of spoilageof food and the rate at which pathogenic bacteria will grow in a food.Temperature potentiates the effect of other factors and, in manysituations, is the factor most likely to fluctuate. Integrating tempera-ture/time history, when combined with a thermal death model suchas that proposed by Bigelow (1921) and Esty and Meyer (1922), hasbeen standard practice to evaluate the efficacy of thermal processes inthe canned food industry for many years.
TFI has also beenwidely used to evaluate the hygienic equivalence ofprocesses, particularly in the meat industry, and to estimate the safetyand stability of foods during transit and storage [see McMeekin (2007)for a succinct review of the contributions of Dr. C.O. Gill in this field]. Forthese purposes, the choice of temperature monitoring device has beenamong chemical and physical monitors and electronic temperatureloggers, with the latter becoming more widely used (Labuza, 2006).
However, a biological temperature indicator was suggested as apotential solution to the non-alignment of chemical and biologicalreaction kinetics by McMeekin and Ross (1996). The innovativetechnology to translate the idea into a unique, practical and marketabledevice was provided by Cryolog with their TRACEO® Retail and (eO)®
Take away products (www.cryolog.com). Development of the under-pinning models was described by Ellouze et al. (2008).
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Regardless of the type of device chosen for temperature monitoringand integration, application is basedon two innovative concepts, specifiedspoilage levels and relative rates, developed by Professor June Olley andher colleagues in the 1970s. The term “specified spoilage level”, whichrepresents the time to reach a certain point in a sequence of events, wasintroduced by Olley and Ratkowsky (1973a,b). In the same publications,they refineda linear relative spoilage rateproposedbyNixon (1971) toonedescribed by Arrhenius kinetics. Of note is mention of a “universal”spoilage model as it described a variety of deteriorative processes withsimilar activation energies, e.g. spoilage of various species of shark andmortality of abalone (Olley, 1971). Professors Olley and Ratkowsky havecontinued their search forauniversal spoilagemodel since that time,mostrecently publishing a thermodynamically based model describing tem-perature effects on the growth rate of awide range of bacteria (Ratkowskyet al., 2005). Themechanistic explanation is based on the denaturation ofglobular proteins at low and high temperatures. Subsequent, as yet un-published,work has demonstrated themodel also describes the influenceof temperature on the growth of a wide range of Archaea.
4.3. Real-time reporting
The above describes the use of temperature loggers for processcontrol in situ, but, during transport and storage, there may be prob-lems with data recovery, retrospective analysis of information andmanual examination for “progress reports”. However, these can beresolved by innovative technologies enabling real-time reporting. InAustralia, Smart-Trace provides a good example of an electronicsystem and was described in detail by McMeekin et al. (2006b) andMcMeekin (2007).
The Cryolog temperature integrators (Section 4.2), which are basedon the growth rate response of lactic acid bacteria to temperaturefluctuations, also contain the important element of real-time report-ing, in this case at the retail and consumer levels. The TRACEO® Retailproduct is an adhesive label applied on the barcode which is readablebefore the specific use-by-date of the product is reached and changescolour and becomes opaque, preventing scanning of the barcode,when the product has reached its use-by-date, or because the producthas undergone a series of cold chain disruptions.
The (eO)® Take away product is designed to operate on single unitsof refrigerated takeaway foods with a colour switch from green to redindicating end of shelf life. A decision to remove stock from displaywould be taken by retail staff, while a decision not to purchase orsubsequently not to consume the product, would be taken directly bythe consumer. Characteristic of the Cryolog products is that the colourtransition from “bon” to “pas bon” occurs rapidly and within a fewhours of the predicted end of shelf life for a specific product. Thisdifferentiates the Cryolog products from several other strip integratorsin which colour changes gradually and interpretation of the extent ofchange is required (I-point AB Sweden, now Vitsab). The I-pointindicator range is listed in Olley and Lisac (1985) and type 3270 wasshown to be the most useful in the 0–15 °C range. Ironically, this typewas not produced subsequently by Vitsab (June Olley, pers. comm.).However, it is important to understand that Cryolog products aretemperature integrators which have taken into account the impact ofthe temperature fluctuations during distribution and storage on thequality of the product. This is unlike go/no go indicators that indicate asingle deviation beyond a critical temperature or after a best beforedate calculated on the basis of storage time at a specified temperature.
The decision to reduce the level of interpretation required by theconsumer on the end of shelf life of a takeaway product (currently thepurpose of one Cryolog product) has some interesting implications formaking decisions on food safety. Whilst science and technology havebeen the traditional pillars of food safety, this needs to be combinedwith innovative approaches to educate and train industry staff andregulators to change the food safety culture and minimise humanerror (McMeekin, 2007).
Changing the workplace culture has led to significant success inreducing crashes in the aviation industry (Maurimo et al., 1995) and insurgical practices resulting in reduced traumatic outcomes for patients.A detailed treatise of human error was published by Reason (1990).There can be little argument that consumers should also be educated (ortaught by rote) the basic elements of food safety and, further, that thisshould start early in life. In Australia, the Food Safety InformationCouncil has this mission and estimates that ~25% of foodborne illness isattributable to consumers (www.foodsafety.asn.au). The first text onhuman error in food safety, authored by Frank Yiannaswill be publishedin 2008 (Yiannas, in press).
However, it appears that, regardless of significant efforts to educateconsumers about food safety, the simple messages are not retained or,perhaps, are not prioritised when time-pressure is a significant factor ofeveryday life. Real-time predictive microbiology technologies, such asCryolog, designed for application on individual packages at the retail andconsumer levels of the supplychain, provide instant information, instantrecommendations and instant decisions for time-pressured consumers.
5. Value analysis of predictive modelling R&D: the RefrigerationIndex (RI) case study
The Refrigeration Index (RI) became themajor outcome of significantresearch funding of predictive microbiology over several years by Meatand Livestock Australia (MLA) when it was incorporated into the revisedExport Control (Meat andMeat Products)Order in 2005by theAustralianQuarantine and Inspection Service (AQIS, 2005) (see http://www.daffa.gov.au/aqis/export/meat/elmer-3). As a result, it is mandatory that thechilling of meat carcasses in Australian export abattoirs is based on anEscherichia coli growthmodel used to interpret coolingprofilesmeasuredby electronic temperature loggers which estimate the potential for E. coligrowth and express it as the RI (www.mla.com.au).
A value analysis of the return on research and development inpredictive microbiology funded by MLAwas carried out by the Centrefor International Economics (www.thecie.com.au), taking into accountinvestment and other inputs, outputs (measures of scientific worth),outcomes (applications arising from the R&D) and impact (return ofinvestment for stakeholders) (see http://www.ml.com.au/HeaderAnd-Footer/AboutMLA/Corporate%2Bdocuments/Evaluation/default.htm#past%20summaries).
The impacts for the raw meat sector of the Australian meat exportindustry over 30 years from adoption were:
1. Inputs: MLA AU$3.2 M, Australian Research Council and AustralianQuarantine Inspection Service AU$0.6 M.
2. Outputs: Tool developed and validated by MLA; 11 scientificarticles; extensive industry training by MINTRAC (Meat IndustryTraining Advisory Council).
3. Outcomes: RI mandated in the Export Control (Meat and MeatProducts) Orders 2005.
4. Impacts: Lower compliance costs; increased regulatory confidence;more than 800 meat industry personnel trained.
5. Benefits: AU$44 M in red meat industry added value; benefit:costratio of 11:1; NAU$60M in benefits to Australian consumers by 2028.
The impacts for the processed meat industry over 30 years fromadoption were:
1. Inputs: MLA partnership with the University of Tasmania as apreferred research provider; advice to theMeat Standards Commit-tee (at that time the domestic market food safety regulator).
2. Outputs: RI tool and other software products; 11 refereed scientificpapers; improved relationship between industry and regulators.
3. Outcomes: adoption of predictive microbiology to ensure efficientregulation and implementation of Australian standards.
4. Impacts: lower compliance costs; increased regulatory confidence;reduced illness and death from listeriosis.
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5. Benefits: AU$162 M in industry added value (pork industry majorbeneficiary); AU$281 M in social benefits to Australian consumersby 2028.
In addition, the Australian meat export industry will gainconsiderable benefit from highly qualified regulatory and qualityassurance staff trained to implement the new approach. The staff willbe able to validate food safety programs and will have access to thelatest relevant R&D outcomes from Australia and overseas. It isanticipated that this human resource will contribute to market access,particularly when involved with alternative procedures in an out-comes based regulatory environment.
6. Modelling food and other ecosystems
Predictive foodmicrobiology in its “modern” formhas evolved overthe last 30–35 years to the point where it was described (albeit tooearly, at that time) as a new paradigm in food microbiology (Labuza,1994) and has been accepted by industry (Membré and Lambert, 2008)and regulatory authorities (AQIS, 2005) as an alternative, perhapspreferable, approach to predict the shelf life and safety of foods.
However, there are other groups of microbiologists who areattempting to model different aquatic and terrestrial ecosystems topredict the microbial contribution to environmental degradation (and,conversely, develop bioremediation strategies), occurrence of algalblooms (Recknagel et al., 1997; Schoemann et al., 2005), Vibrio choleraeepidemics (Lobitz et al., 2000) and microbial roles in biogeochemicalcycles which may impact on the production or feedback inhibition ofgreenhouse gases and, thereby, the acceleration or slowing of globalwarming and its consequences (Rose et al., 2001; Rosenberg and Ben-Haim,2002; BallyandGarrabout, 2007). Unfortunately, for thedisciplineof predictive modelling as a whole, different groups of microbiologiststend to work within the confines of sub-discipline interest groups, inselection of journals for publication of their research, prefer to attendspecialist conferences and select specific sessions at conferences with awider range of topics on the program.
Crystal ball-2007 [Environmental Microbiology 9(1): 1–11, 2007]featured leading researchers in the field of environmental microbiologywho speculated on the technical and conceptual developments that willdrive innovative research and open new vistas over the next few years.The scenewas set byCurtis (2007)whowrote, “Thisfield is hideboundbythe difficulty of experimentation and is, therefore, contaminated by self-congratulatorymathematical castles in the air with invented parametersand little verification”. And Hugenholtz (2007) observed that, to makesense of massive data sets, “modelling will assume a central role inmicrobial ecology. As a result, it will transition from amainly qualitative,descriptive discipline to a quantitative predictive one”.
As foodmicrobiologists, dowe require amodicumof self-examinationto ensurewe have not built castles, windmills or other structureswithoutadequate foundationswithwhich othersmay joust successfully?Haveweinvented the numbers and engaged only sparsely in validation? Alter-natively, are the environmental writers not familiar with the voluminousliterature in predictive foodmicrobiology? Or havewe, as predictive foodmicrobiologists, been too insular and self-contented to spread the word?Clearly, these are questions worthy of informed and considered debatebetween food and environmental microbial modellers.
6.1. Food ecosystems
The ability to model microbial behaviour in foods with reasonableaccuracy may, in large part, be due to the characteristics of many foodenvironments. These range from typical fresh foods which contain ahigh level of nutrients supporting rapid microbial growth rates andrapid lag phase resolution. They are also predominantly batch systemsof short duration in which a dominant population of low diversity isselected. Such features greatly reduce variability inmicrobial response
times and uncertainty is minimised as the physico-chemical environ-ment of many fresh foods is very well characterised. This representsthe “classical” microbiology space in the dichotomy described byBridson and Gould (2000) which is amenable to description by kinetic(deterministic) models.
Fresh foods, because of rapid colonisation bymicroorganisms, havea short shelf life and over eons of time humans have devised ways toextend the keeping time and perhaps, by the same strategies,maximise food safety. A consequence of food preservation is thatprocessed foods represent a harsher environment and the growth rateof the original population is reduced and the lag phase increased. Acorollary is that the changed conditions select for a new spoilageassociation or different pathogen. The selection pressure continues asthe conditions become increasingly harsh and eventually onlyorganisms with a highly specialised physiology survive and grow.Examples include extreme halophiles in heavily salted fish (Chandlerand McMeekin, 1989) and thermophiles on dairy processing equip-ment where temperatures N70 °C are attained in pasteurisers andevaporators (Langeveld et al., 1995). Only severe treatment with heator ionising radiation can ensure sterility of a product, but at theexpense of decreased sensory and nutritional properties. Any less thana complete kill will allow survivors, capable of repair and growth, tocolonise the ecosystem again (Knight et al., 2004).
Harsher environments and inevitably increased response times per-haps suggest a transition from the classical to the “quantalmicrobiology”space (Bridson and Gould, 2000). Note that quantal, not quantum(Graeme Gould, pers. comm), was used as no evidence has beenpresented to date that cannot be described by conventional physics(Bothmaet al., 2007).What is certain, fromamodellingviewpoint, is thatas response time increases so does variability, eventually to the pointwhere kinetic models are replaced by probability models (Ratkowskyet al., 1996). The point at which we choose to change approach fromkinetic to probabilistic depends on the required level of confidence (i.e.probability of “failure” and the consequences of failure (i.e. the “risk”).
6.2. Ecosystems other than food
Environmental microbial ecologists are often confronted withdescribing and/or quantifyingmicrobial roles and activity in ecosystemsthat are complex andexist onmanyscales frommicrocosms tooceans. Incontrast to most food environments they are open (continuous flow)systems rather than the batch systems most frequently encountered infood microbiology.
Also, as opposed to food ecosystems, they are frequently nutrientdepleted and, thus, support low populations but with diverse climaxcommunities that employ various feedback (homeostatic) mechanismsto maintain equilibrium in the community (Atlas and Bartha, 1987).Whilst food ecosystems come and go, in a time frame of days, weeks ormonths, many environmental systems exist over very long time frames,both in terrestrial and aquatic habits (Whitman et al.,1998; Karner et al.,2001). Environmental microbiologists also appear to be much moreconcerned by the implications of unculturablemicrobiota andmicrobialdiversity than food microbiologists (Prosser et al., 2007).
Many natural microbial environments are subject to regularfluctuations which determine the growth or death of components ofthe ecosystems. An example is the seasonal appearance of Vibrioparahaemolyticus in seawater in summer, in response to rising tempe-ratures, resulting in an increase in foodborne illness due to thisorganism (CDC, 1998; Cook et al., 2002). On the decline side of theledger, E. coli has been shown to be inactivated much more rapidlyunder light than under dark conditions due to the effect of UVradiationwith wavelengths up to 450 nm contributing to inactivation.This was demonstrated by several groups ~1980 (Chamberlin andMitchell, 1978; Fujioka et al., 1981; McCambridge and McMeekin,1981), but it is salutary to note that the same phenomenon, sameeffective depths and comparable rates were reviewed in the book
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“Microorganisms in Water” by Frankland and Frankland (1894) inwhich Chapter IX dealt with the “Action of Sunlight onMicrooganismsin Water”.
Whilst preventing foodborne illness and food spoilage is sociallyresponsible and saves multi-million dollars for consumers and thefood industry, environmental microbial ecology is central to under-standingmicrobial roles in biogeochemical cycles. These include thosereleasing or sequestering gases associated with “big picture” sciencequestions such as, is global warming due to anthropogenic activity ornatural variation in climatic cycles? Because of worldwide concern,current levels of research interest and, therefore, funding, will placeenvironmental microbial ecologists in pole position to advancefundamental understanding of microbial ecology questions. However,it will be interesting to discover if predictive food microbiology canprovide direction to future studies in environmental microbialecology, should ever the twain meet.
7. Summary and general projections
The research pioneers of predictive foodmicrobiology, including TinoGenigeorgis, Terry Roberts, Bob Buchanan, June Olley and DavidRatkowsky, like very troublesome microorganisms, showed remarkablepersistence in developing the field. Thiswas achieved in the face ofmuchscepticism that accurate models could be constructed, and usedsubsequently, to predict microbial population behaviour by consideringonlyenvironmental factors. Indeed, one leading foodmicrobiologistwentso far as to describe predictive modelling as numerology. While the titlePredictive Microbiology may conjure up images of crystal-balls, it cer-tainly is not the study of the supposed occult significance of numbers.
In all probability, such scepticism encouraged the early researchersto continue and to build networks of microbiologists, statisticians, pro-cess engineers, food technologists, electronics experts, et al., required toprovide the strategic science and innovative technologies needed for theconcept (Scott, 1937) to succeed. At this point in time, it is reasonable toconclude that we have succeeded with widespread acceptance ofpredictivemodels which, in some jurisdictions, are now incorporated infood safety legislation and with major food manufacturers using boun-dary models to formulate mildly preserved products with requiredstability, i.e. a quantified hurdle concept. Predictive models and theirattendant databases have also been crucial in the operation ofquantitativemicrobial risk assessmentwhich is likely tohave “drowned”in uncertainty in the absence of the knowledge summarised in models.The quantitative approach tomicrobial ecology has also identified areasof particular interest for detailed physiological and molecular studiesand these links will inevitably connect the modellers with systemsbiologists and network analysts who will be required to interpret thedata deluge.
However, most of this research has been carried out in the pre-dictive food microbiology “club”, which evolved from an informalgroup of scientists with common research interests who met for thefirst time at an international conference in Tampa, Florida in 1992 andfor the fifth time in Athens, Greece in 2007. The group in 2008 wasformalised as a Professional Development Group of the InternationalAssociation of Food Protection.
Now that we have reached a state of maturity and acceptance in thefood microbiology community, the time is right to expand our horizonsand to interact with microbiologists developing models to describe themicrobial role in ecosystems other than food. Such interactions willexpose us to different thought processes, to new experimentaldifficulties, to spatial and temporal boundaries currently way beyondour “ken”. Nevertheless, these interactions and the challenges posedwillbenefit both food microbiology and environmental microbiology. Pro-vided, of course, thatwemaintain commitment to strategic research as abasis to develop theoryandmechanisms and couple thiswith innovativetechnologies to meet the great expectations that will arise from dis-covering and understanding more of the microbial world.
The preceding sentences express a general viewabout the benefits ofnetworking and collaboration. But general views and the objectives ofhigh level vision or mission statements often dissipate due to lack of aspecific task or case study to demonstrate the probability of a goodoutcome. Thus, we choose to conclude by suggesting a specific researchdirection.
8. Moving forward through improved models of microbialpersistence and recovery
8.1. A common research interest
An area of common interest in food and environmentalmicrobiologyis the enhanced ability of some cells to persist under very harsh con-ditions. Spores are the ultimate survival machines resisting severephysical and chemical challenges by dint of their tank-like construction.However, vegetative cell populations of E. coli have been demonstratedto contain a subpopulation of resistant cells in a genetically homo-geneous population (Balaban et al., 2004); Smits et al. (2006) identifiedphenotypic variation and the role of feedback regulation leading tostates described as multistationarity and multistability; and toxin–antitoxin systems, referred to as suicide modules, are chromosomally-encoded genes that can mediate self-destruction of a cell (Aizenmanet al., 1996; Engelberg-Kulka and Glaser, 1999; Gerdes, 2000). Thehypothesis is that these systems in bacterial cells are analogous to theapoptotic machinery in multicellular organisms and that sacrificingindividual cells benefits the population as a whole.
This serves to emphasise that survival strategies operate at bothsingle cell and population levels and suggests further study of“multicellularity” in bacterial populations (Shapiro, 1998). At thepopulation level, biofilms exemplify a ‘strength in numbers’ tacticwith the protection inherent in individual cells supplemented byextracellular defences. Within these maze-like structures, akin tofortified mediaeval villages with myriads of underground passages,the microbial inhabitants communicate, extract nutrients, may co-metabolise and effectively resist the ingress of inimical substances.
The study of persistence and biofilmswill also require different typesof modelling, e.g. attachment to and removal from surfaces (Schaffner,2003) and for the “-omics” researchers, differentiation of persistent cellsfromothers in the populationwould be aworthwhile researchobjective.And, while dealing with persistence, the mechanisms of recovery fromvarious “dormant” states will present significant research challenges.Optimising recovery on various media has been a long-term interest infood microbiology, but many studies have been conducted empiricallyrather than progressing towards a mechanistic explanation such as theaddition of catalase or pyruvate to scavenge free radicals (Mackey andDerrick, 1982; Mackey and Seymour, 1987).
8.2. Selecting organisms for collaborative studies
Are there organisms that would be suitable for parallel study byenvironmental and food microbiologists? From the viewpoint of foodsafety, two of the most troublesome organisms in food microbiology aretoxigenicE. coli and Listeriamonocytogenes. The latter iswell knownas anenvironmental organism and has been isolated from soil, vegetation,sewage and water. From these repositories they may colonise food-processing environments with some strains displaying remarkablepersistence (Holah et al., 2004). In the same publication, colonisation offood processing premises by persistent strains of E. coli was demon-strated and, subsequently, the question “Escherichia coli 0157: burger bugor environmental pathogen?” was posed by Strachan et al. (2006) whoconcluded that acquisition of infection in NE Scotland was 100-timesmore likely to occur when visiting a pasture than eating a burger.
The pathogenic marine vibrios, V. cholerae, V. parahaemolyticus andV. vulnificus, are another microbial group in which shared researchinterests between environmental and predictive food microbiologists
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could readily occur. Recently, FAO/WHO reported the results ofmicrobial risk assessment on these organisms (V. cholerae in shrimpin international trade; V. parahaemolyticus in seafood and V. vulnificusin raw oysters) (FAO/WHO, 2006). As a result, much information wasassembled and important data gaps were identified. For example, therisk assessment relied on a single report of the effect of temperatureon the growth rate of V. parahaemolyticus in bacteriological broth(Miles et al., 1997). This was adjusted by a factor of four for consistencywith a single report showing a slower growth rate for V. parahaemo-lyticus at 26 °C in oysters (Gooch et al., 2002). Thus, the need isemphasised for robust models and databases that can fill major datagaps, reduce uncertainty and increase confidence in the estimatedlevels of risk.
The seasonal occurrence of marine vibrios, their widespreadoccurrence in marine environments, the potential for their temporaland spatial distribution to be affected by increased water tempera-tures and their association with foodborne illnesses ranging fromthose associated high-risk individuals, as with V. vulnificus, to pan-demics with V. cholerae 01 and 0139, make these organisms a suitablegroup for collaborative modelling studies. For example, Lobitz et al.(2000) reported the use of satellite data to monitor temporal andspatial changes in cholera cases in Bangladesh from 1992–1995,reporting that sea surface temperature, measured by satellite infraredmeasurement, correlated with cholera case data. Remote sensingtechnology was also used by Phillips et al. (2007) to evaluate riskassociated with V. parahaemolyticus in Gulf Coast oysters.
8.3. Models of persistence and recovery to inform the broader horizon ofmicrobial ecology
Persistence and recovery are also key features in microbial dis-persal and colonisation of new habitats, giving rise to importantquestions in microbial ecology such as:
- speciation: are species ubiquitous because of unrestricted dis-persal or are they restricted to island communities because ofbarriers to dispersal; are there ecospecies (ecotypes) as proposedby Cohan (2002).
- diversity:what is the extent ofmicrobial diversity, howmany speciescannot be cultivated and what is their role in natural environments?(Prosser et al., 2007).
- “resting stages”: what is the significance of inactive cells for survivalof a species: Is there such a state as viable but non culturable (VBNC),or is recovery of a small number of very persistent cells responsiblefor the reappearanceof viable cells in anecosystem? Should the termbe ‘active but non culturable’ (ABNC) as proposed by Kell et al. (1998)as an operational definition and to remove the oxymoron status ofVBNC? [For more detail on the points above, and other pertinentquestions, see Prosser et al. (2007)].
Vibrios and related organisms have featured prominently in the A orVBNC debate, going back to the research of Morita and colleaguesworking with the Vibrio-like organism ANT 300. Now classified asMoritella marina, ANT 300 gained “celebrity status” amongst marinemicrobial ecologists with its ability, following starvation, to form dwarfcells (~11-fold reduction in volume), ditch all reserve and capsularmaterial and low molecular weight carbohydrates and decrease endo-genous respiration by 99% (Novitsky and Morita, 1976). Conversely, thedwarf cells multiplied rapidly, produced a sheathed flagellum anddisplayed a wide range of chemotactic responses and remarkable sub-strate capture ability (Torrella and Morita, 1981). This appears tocontradict the self-preservation and nutritional competence (SPANC)balance which holds that, in E. coli, high stress resistance is associatedwith reduced ability to compete for growth substrates at suboptimalconcentrations (Ferenici and Spira, 2007). So, perhaps appropriately, weend with a conundrum, further emphasising that much remains to beunderstood in both environmental and food microbial ecology. Perhaps
the ‘hybrid vigour’ obtained by combining different lineages of theoryand practice will yield increased insight and innovation to define thecrucial role of microbial activity in the biosphere?
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