SciELO - Scientific Electronic Library Online

vol.39 número4Identificación rápida y sensibilidad a toxinas killer de levaduras aisladas de micosis no sistémicasDiversidad bacteriana en un biorreactor de lecho fluidificado durante el tratamiento de agua contaminada con nafta índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados




  • No hay articulos citadosCitado por SciELO

Links relacionados


Revista argentina de microbiología

versión impresa ISSN 0325-7541versión On-line ISSN 1851-7617

Rev. argent. microbiol. v.39 n.4 Ciudad Autónoma de Buenos Aires oct./dic. 2007


Simplified prediction of Staphylococcus aureus growth in a cooked meat product exposed to changing environmental temperatures in warm climates

R. Baeza1, C. E. Rössler1, 2, D. M. Mielnicki1, 2 , M. C. Zamora1, 3*, J. Chirife1

1 Facultad de Ciencias Agrarias, Pontificia Universidad Católica Argentina (UCA), Cap. Gral. Ramón Freire 183 (C1426AVC) Ciudad Autónoma de Buenos Aires, Argentina;
2 Programa de Estudios de Procesos Atmosféricos en el Cambio Global (PEPACG, UCA-CONICET);
* Correspondence. E-mail:


In this work, a simplified method is used to estimate the growth of Staphylococcus aureus in a pasteurized meat product left for several hours at environmental temperatures (diurnal time) in warm climates of different cities in Argentina. Hourly temperature data for a warm January (the hottest month of the year) day, and literature data on the kinetics of S. aureus growth inoculated in a pasteurized meat product were used for calculations. As shown by results, if a cooked meat product is left exposed to environmental temperature at diurnal time, predictions made when using a constant temperature value (i.e. average daily) may not be accurate. Growth estimations in contaminated food left under ambient conditions during diurnal time, should consider the changing environmental temperature for correct results.

Key words: Staphylococcus aureus; Temperature; Environment; Predictive microbiology; Cooked meat; Generation time


Predición simplificada del crecimiento de Staphylococcus aureus en productos cárnicos cocidos expuestos a temperaturas ambientes cambiantes en climas cálidos. En este trabajo se utiliza un método simplificado para predecir el crecimiento de Staphylococcus aureus en un producto cárnico pasteurizado dejado por varias horas a temperatura ambiente diurna en zonas de clima cálido. En la predicción, se utilizaron datos de la temperatura horaria para un día caluroso típico de enero (mes más caliente del año) en varias ciudades de la Argentina y datos de la literatura sobre tiempos de generación y tiempo lag de la bacteria inoculada en un producto cárnico pasteurizado. Los resultados indicaron que cuando el producto se deja a temperatura ambiente diurna durante varias horas, no se debe utilizar para la predicción un valor de temperatura promedio (ej.: temperatura media diaria), sino que hay que tener en cuenta la evolución de este parámetro a lo largo del período considerado.

Palabras clave: Staphylococcus aureus; Temperatura; Ambiente; Microbiología predictiva; Carne cocida; Tiempo de generación


Staphylococcus aureus is a bacterium with strains that are capable of producing a highly heat-stable toxin that causes illness in humans. Intoxication is caused by ingesting enterotoxins produced in food by S. aureus, usually because the food has been left at ambient temperature (13). Foods that require considerable handling during preparation and that are kept without refrigeration are usually involved in staphylococcal food poisoning; this bacterium is able to grow in a wide range of temperatures (7-48 °C) with an optimum at 35-37 °C, a range which may be frequent in warm climates. The toxin produced by S. aureus is very heat-stable and is not destroyed at normal cooking temperatures.
Many kinds of cooked foods are known to be displayed in restaurant windows at ambient temperatures for several hours. Cooked meats, ham, poultry, egg products, tuna, potato and macaroni salads are good environments for this bacterium to produce the toxin. Many foods are often prepared under unsanitary conditions and stored for long periods at ambient temperature before selling (for example, street foods), and the time lapse between food preparation and consumption is an important factor to consider in terms of hazard. For example, vendors cook the food in the morning and then store it at ambient temperatures for the rest of the day (3, 7).
It must be acknowledged that the term "ambient" temperature in relation to the risk assessment of bacterial growth is vaguely defined, since daily temperature is not constant and contaminated food products undergo a changing temperature environment. Thus, a constant temperature prediction may not be so useful. Risk assessment studies of food borne bacterial pathogens areusually performed at constant temperature which mimics some average environmental temperatures. This procedure may not adequately reflect the actual temperature profile to which the bacterium is exposed at a given time period of the day.
It is the purpose of the present paper to estimate the extent of S. aureus growth which would occur when a cooked meat product is left for several hours at changing environmental temperatures during diurnal time under various warm climates. Temperature data corresponding to selected Argentinean cities were used for calculations.


Data Analysis

Environmental (ambient) temperatures
Table 1 shows the geographical location of selected Argentine cities, namely Buenos Aires, Córdoba, Formosa, La Rioja, Posadas, Resistencia, San Juan, San Luis and Santiago del Estero, as well as their mean environmental (surface) temperatures for the month of January (the hottest month in summer) in the decade 1981-1990. Mean temperature values for January ranged around 24-27 °C for the different places. Data were obtained from the Argentinean Meteorological Service, and conventional temperature monitor stations were used to measure the environmental temperature.

Table 1. January mean environmental temperatures for decade 1980-1990 in selected Argentine cities

Hourly surface temperature records (24 measurements/day) for a single hot January day, (January 9th, 2006) in the selected locations were also obtained. The corresponding hourly temperature profiles in the selected cities are shown in Figure 1 (a, b, c).

Figure 1. Hourly environmental temperatures for January 9th, 2006 in selected Argentine cities: (a) Buenos Aires, Formosa, Posadas and Resistencia (b) Córdoba, San Luis and Santiago del Estero (c) La Rioja and San Juan

For the purpose of the present work we considered that the cooked meat food was prepared (and contaminated) early in the morning (e.g. at 8:00 am) and then stored at environmental temperature during diurnal time before selling/consumption. As shown by the temperature profiles (Figure 1) at 8:00 am, temperature was about 25-30 °C in all locations; then slowly increased, remained several hours at 35-40 °C (close to the optimum temperature for S. aureus growth) and finally began to decline.

Growth kinetics of Staphylococcus aureus in a cooked meat product stored at constant temperatures.
The International Commission on Microbiological Specification for Food (ICMSF) (5) reported the generation time, (GT) (also called doubling time) and lag phase duration of S. aureus inoculated (3 log counts/g) in a pasteurized beef and kidney pie having pH 5.8 and water activity (aw) 0.98, stored at various constant temperatures. During the lag phase, cells are adjusting to their new environment; during the exponential phase of growth, bacteria multiply by binary fission and the so-called stationary phase occurs when the bacterial level ( S. aureus, in this case) reaches a high critical concentration (e.g. around 1 x 109). Figure 2 shows the correlation of these kinetic parameters (lag time and GT) with temperature in the range of interest of this work. It can be seen that the lag phase time (Figure 2a) rapidly decreased with increasing temperature approaching some sort of asymptotic value; GT (Figure 2b) also decreased with increasing temperature, both behaviours are within expectations (14).

Figure 2. Correlation of kinetic parameters for growth of Staphylococcus aureus in cooked beef and kidney pie (ICMSF, 1996) (a) Lag time versus temperature (b) Generation time versus temperature

In the present work, S. aureus growth estimations were made by using a simplified approach. It consists in using the well- known relationship between the number of bacteria at a given time (Nt), the original number of bacterial cells (No), GT, and time (t),

Nt = No . 2 t /GT                                 equation 1

where t is the time elapsed after completion of lag phase. For present predictions in cooked meat, No is taken as 3 log counts/ g (inocula value) and the lag phase time is known (Figure 2a). The use of Equation 1 is valid only for predictions in the exponential growth phase of bacterial curve. In the present work, maximum predicted value of S. aureus counts in the cooked meat product is limited to, Nt = 1 x 106 cfu/g, which is within the exponential growth phase.
GT (4) were empirically correlated with temperature (in the range of 25-42 °C) by the following equation obtained from curve fitting of the data,

GT = - 0.0185 T3 + 2.1314 T2 - 82.205 T + 1083.5                                 equation 2

where T is temperature (°C).

Since we assumed the cooked food was prepared (and contaminated) at 8:00 am, the lag phase time was taken as 3 hours, which is the average of lag times at 25-30 °C (Figure 2a), since this is the environmental temperature range in all locations at that morning time. Subsequent growth of S. aureus, exposed at the changing environmental temperatures in selected cities was calculated (equation 1) by accumulated sum of various one- hour intervals as the time interval considered. For each interval, GT was evaluated (equation 2) at the middle point temperature of any one hour interval. Calculations ended by the time the staphylococcal population (Nt) reached 1 x 106 CFU/g, which is a count number likely to be associated with enterotoxin production. The number of staphylococci required to cause illness cannot be predicted with certainty because many variables may affect the amount of enterotoxin produced in a contaminated food. Stewart et al. (12) cited that according to a 1992 guideline given by the U.S. Food and Drug Administration, the effective dose of enterotoxin may be achieved when the population of S. aureus reaches a level of > 105 CFU/g. Niskanen and Nurmi (9) reported that contaminations of S. aureus over 2 x106 CFU/g were associated with enterotoxin production in dry sausage, and Walls and Scott (13) determined that the toxin was not produced in a cooked meat product (1.2% NaCl, pH 5.6 incubated at 35 °C) until counts increased to a level greater than 1.2 x 106 CFU/g.


Predictive microbiology involves the use of mathematical models to predict the growth, survival and inactivation responses of microorganisms to different environmental conditions. Sigmoidal functions have been used to accurately model the typical microbial growth curve from the lag to the stationary phase and provide the mathematical basis for estimating parameters, such as the maximum growth rate, (1, 4, 10, 15). 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.
A great concern for predictive microbiology is estimating the growth under changing temperature conditions (4). Shaw (11) and later other authors (1, 2, 8) reported on the effect of fluctuating temperatures on microbial growth. Depending on the magnitude of the temperature deviation, the organism may change its growth rate to a characteristic rate of the new temperature, or it may stop growing if a lag phase is introduced. Langeveld and Cuperus (6) found that bacteria within the exponential phase immediately respond to a change in temperature. Zwietering et al. (14) also indicated that shifts during the exponential phase in a moderate temperature range, result in immediate exponential growth at the growth rate associated with the new temperature. However, shifts to or from low temperatures resulted in an adaptation period.
Present predictions considered that S. aureus cells contaminating the cooked meat continuously adapt to a new growth rate characteristic of the new temperature, when exposed to changing environmental temperature at selected diurnal times (Figure 1). This assumption is based on two important facts: 1) the changing environmental temperature occurs in a range which is close to the optimum for S. aureus growth, and b) the average rate of diurnal temperature increase in the growth period is low, being somewhere between 0.7-1.2 °C/hour in Resistencia, Posadas, Formosa, San Luis and Córdoba and 1.4- 2.0 °C for Buenos Aires, Santiago del Estero, La Rioja and San Juan (Figure 1) . These factors mean that a "heat shock" (which may affect bacterial cells) never occurred. Furthermore, environmental temperature changes (to or from) are far away from the minimal value for growth, (7.0-8.0 °C).
Table 2 shows calculated times for a 3-fold log increase (from inocula of 103 CFU/g) of S. aureus count in cooked beef and kidney pie using different environmental temperature approaches: a) hourly temperatures at a specified diurnal period on January 9th, 2006; b) mean temperature for January 9th, 2006 and c) mean January temperature for the decade 1981-1990. Time for a 3-fold log counts goes from as low as 3.7 hours for La Rioja to 5.4 hours for Buenos Aires. Growth times predicted using daily mean environmental temperature are higher than those calculated using the actual (changing) environmental temperature; and this is due to the cooling effect of nights. Also, using the mean January temperature in the decade 1981-1990 lead to even higher predicted times to reach 3- fold log counts (total count 1 x 106 CFU/g). Figure 3 illustrates this behaviour for Buenos Aires and La Rioja.

Table 2. Time for a 3-fold log increase (from inocula of 103 CFU/g) of S. aureus count in cooked beef and kidney pie: calculations using different environmental temperature approaches.

Figure 3. Comparison of S. aureus growth in cooked meat product predicted using different environmental temperatures approaches. (1) specified hourly temperatures for January 9 th, 2006; (2) mean temperature for January 9th, 2006, (3) mean January temperature over decade 1981-1990.

It may be concluded that mean environmental temperatures (i.e. mean daily values) may not adequately model S. aureus response when a contaminated food is left for several hours at "ambient" conditions during diurnal time (time abuse-temperature). The present results are also useful to compare growth estimates which would occur in different warm climates.

Acknowledgements: The authors acknowledge Dr. Pablo Canziani from PEPACG, UCA-CONICET.


1. Baranyi J, Roberts TA. Mathematics of predictive food microbiology. Int J Food Microbiol 1995; 26: 199-18.
2. Baranyi J, Robinson TP, Kaloti A, Mackey BM. Predicting growth of Brocothrix thermosphacta at changing temperature. Int J Food Microbiol 1995; 27: 61-75.
3. Caballero Torres A, Carrera Vara JA, Lengomín Fernández ME. Rev Cubana Aliment Nutr 1998; 12: 7-10.
4. Gibson AM, Ratchell N, Roberts TA. Predicting microbial growth: growth responses of salmonellae in a laboratory medium as affected by pH, sodium chloride and storage temperature. Int J Food Microbiol 1988; 6: 155-78.
5. ICMSF (International Commission on Microbiological Specification for Food). Microorganismos de los alimentos. Características de los patógenos microbianos. E. Acribia S.A., España, 1996, p. 358-9.
6. Langeveld LPM, Cuperus F. The relation between temperature and growth rate in pasteurized milk of different types of bacteria which are important to the deterioration of that milk. Neth Milk Dairy J 1980; 34: 106-25.
7. Mensah P, Yeboah-Manu D, Owusu-Darko K, Ablordey A. Street foods in Accra, Ghana: how safe are they? Bull World Health Organization 2002; 80: 546-54.
8. Mitchell GA, Brocklehurst TF, Parker R, Smith AC. The effect of transient temperatures on the growth of Salmonella typhimurium LT. I: cycling within the growth region. J Appl Bacteriol 1994; 77: 113-9.
9. Niskanen A, Nurmi E. Effect of starter culture on staphylococcal enterotoxin and thermonuclease production in dry sausage. Appl Environ Microbiol 1976; 31: 11-20.
10. Ratkowsky DA, Lowry RK, McMeekin TA, Stokes AN, Chandler RE. Model for bacterial culture growth rate throughout the entire biokinetic temperature range. J Bacteriol 1983; 154: 1222-6.
11. Shaw MK. Effect of abrupt temperature shift on the growth of mesophilic and psychrophilic yeasts. J Bacteriol 1967; 93: 1332-6.
12. Stewart CM, Cole MB, Schaffner DW. Managing the risk of staphylococcal food poisoning from cream- filled baked goods to meet a food safety objective. J Food Protection 2003; 66: 1310-25.
13. Walls I, Scott VN. Use of predictive microbiology in microbial food safety risk assessment. Int J Food Microbiol 1997; 36: 97-102.
14. Zwietering MH, de Wit JC, Cuppers HGAM, van't Riet K. Modelling of bacterial growth with shifts in temperature. Appl Environ Microbiol 1994; 60: 204-13.
15. Zwietering MH, Jongenburger I, Rombouts FM, van't Riet. Modelling of the bacterial growth curve. Appl Environ Microbiol 1990; 56: 1875-81.         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]         [ Links ]

Recibido: 22/05/06 - Aceptado: 03/07/07

Creative Commons License Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons