RESUMO

RESUMEN El objetivo del artículo fue lustrar mediante modelos de predicción microbiológica, la probabilidad de conformidad del producto terminado, estimando el ciclo de vida de la leche en polvo una vez que ha sido reconstituida, enfocado en los aspectos fisicoquímicos y microbiológicos (Bacillus cereus), por medio del modelo cinético de Arrhenius, con redes neuronales artificiales, entrenadas para pronosticar productos conformes respecto a la bacteria. A través del algoritmo de la red neuronal, se concluyó que, una vez la leche en polvo ha sido reconstituida debe mantenerse refrigerada entre 4°C y 6°C, para obtener una vida útil alrededor de los 19 días, pronosticando el 98% de productos conformes.

ABSTRACT The aim of this paper was to show a microbiological prediction model to determine the probability of conformity in products and estimate the life cycle of reconstituted powdered milk, focusing on the physicochemical and microbiological aspects of Bacillus cereus. We used the kinetic model Arrhenius, built under artificial neural networks designed to measure conformity to Bacillus cereus products. Using the neural network algorithm, we found that once milk powder was reconstituted, the temperature should be between 4°C and 6°C to obtain a lifespan around 19 days and 98% conformity.

Assuntos

RESUMO

The growth of Salmonella enterica subs. enterica sorovar Typhimurium at 25ºC was monitored in industrialized and hospital formulated enteral feeds and the results were used to validate the mathematical model of Salmonella growth presented by the Pathogen Modeling Program (PMP) 7.0 (USDA-USA). The generation time of Salmonella in enteral feeds ranged from 21 to 34.8 min and, the maximum growth rate (µmax) varied from 1.28 to 1.95 h-1, resulting in a population increase from 5 to 6 log10 cycles within 14 to 24 h incubation. Growth was faster in the hospital formulated feed containing vegetables and eggs. The growth kinetic's parameters as lag phase; µmax and maximum population density (MPD) were similar to those predicted by the PMP 7.0, with exception of lag phase in enteral diet at pH 6.3. The results of this study validated the PMP 7.0 model for describe Salmonella growth in enteral feeds and demonstrates the appropriateness of use such model to determine the pathogen behavior in a wide range of storage conditions in this food.

O crescimento de Salmonella enterica subs. enterica sorovar Typhimurium a 25ºC foi determinado em dietas enterais industrializadas e formuladas em hospital e os resultados obtidos foram usados para validar um modelo matemático de crescimento de Salmonella apresentado no Programa de Modelagem de Patógenos (PMP), versão 7,0 (USDA-EUA). O tempo de geração de Salmonella em dietas enterais variou de 21 a 34,8 min e a velocidade específica máxima de crescimento (µmax) foi de 1,28 a 1,95 h-1, resultando em aumento de 5 a 6 ciclos logarítimos em um período de 14 a 24 h de incubação. O crescimento foi mais rápido na dieta formulada em hospital contendo vegetais e ovos. Os parâmetros cinéticos como fase lag, µmax e densidade populacional máxima (MDP) foram similares aqueles previstos no PMP 7.0, com exceção da fase lag em dietas enteral com pH 6,3. Os resultados deste estudo validaram o modelo do PMP 7,0 para descrever o crescimento de Salmonella em dietas enterais e demonstraram a propriedade desse modelo para determinar o comportamento do patógeno em uma variedade de condições nesse tipo de alimento.

Assuntos

RESUMO

The growth of Salmonella enterica subs. enterica sorovar Typhimurium at 25°C was monitored in industrialized and hospital formulated enteral feeds and the results were used to validate the mathematical model of Salmonella growth presented by the Pathogen Modeling Program (PMP) 7.0 (USDA-USA). The generation time of Salmonella in enteral feeds ranged from 21 to 34.8 min and, the maximum growth rate (µmax) varied from 1.28 to 1.95 h-1, resulting in a population increase from 5 to 6 log10 cycles within 14 to 24 h incubation. Growth was faster in the hospital formulated feed containing vegetables and eggs. The growth kinetic's parameters as lag phase; µmax and maximum population density (MPD) were similar to those predicted by the PMP 7.0, with exception of lag phase in enteral diet at pH 6.3. The results of this study validated the PMP 7.0 model for describe Salmonella growth in enteral feeds and demonstrates the appropriateness of use such model to determine the pathogen behavior in a wide range of storage conditions in this food.

O crescimento de Salmonella enterica subs. enterica sorovar Typhimurium a 25°C foi determinado em dietas enterais industrializadas e formuladas em hospital e os resultados obtidos foram usados para validar um modelo matemático de crescimento de Salmonella apresentado no Programa de Modelagem de Patógenos (PMP), versão 7,0 (USDA-EUA). O tempo de geração de Salmonella em dietas enterais variou de 21 a 34,8 min e a velocidade específica máxima de crescimento (µmax) foi de 1,28 a 1,95 h-1, resultando em aumento de 5 a 6 ciclos logarítimos em um período de 14 a 24 h de incubação. O crescimento foi mais rápido na dieta formulada em hospital contendo vegetais e ovos. Os parâmetros cinéticos como fase lag, µmax e densidade populacional máxima (MDP) foram similares aqueles previstos no PMP 7.0, com exceção da fase lag em dietas enteral com pH 6,3. Os resultados deste estudo validaram o modelo do PMP 7,0 para descrever o crescimento de Salmonella em dietas enterais e demonstraram a propriedade desse modelo para determinar o comportamento do patógeno em uma variedade de condições nesse tipo de alimento.

RESUMO

Diversos modelos matemáticos han sido desarrollados con el objeto de predecir el comportamiento de las poblaciones bacterianas en fase de crecimiento en condiciones controladas a nivel de laboratorio, muchos de estos han sido validados en condiciones de producción, transporte y almacenamiento de alimentos. El objetivo del presente trabajo fué adaptar un modelo matemático utilizando ecuaciones obtenidas como resultado del análisis con modelos secundarios de los coeficientes de regresión de la ecuación de Gompertz, a efectos de lograr predecir las poblaciones en crecimiento de Lactococcus lactis subsp. lactis en leche en polvo reconstituida y esterilizada, controlando la temperatura en el rango desde 9 a 39°C. Con este fin, los coeficientes “A” y “B” se analizaron con el modelo de la raíz cuadrada, obteniéndose valores de R2 = 0,819 y 0,991, respectivamente, mientras que el modelo hiperbólico se utilizó para modelar los coeficientes “D” (R2= 0,812) y “M” (R2= 0,995). Finalmente se obtuvo una expresión reparametrizada del modelo original de Gompertz, sustituyendo cada coeficiente de regresión por la ecuación que lo describe, en función a las diferentes temperaturas estudiadas, siendo este modelo utilizado para predecir directamente las poblaciones del microorganismo en estudio a cualquier temperatura dentro del rango estudiado. Se obtuvieron diferencias muy pequeñas entre la población observada experimentalmente y la ajustada según la aplicación del modelo desarrollado, observándose una distribución bastante equilibrada de los valores residuales. Se recomienda utilizar este tipo de modelo para describir el crecimiento de poblaciones combinadas en productos como leche pasteurizada comercial.

Several mathematic models have been developed to predict the behavior of bacteria populations in growing phase and controlled conditions at the laboratory. Many of these models have been validated under food production, transportation and storage conditions. The objective of this study was to adapt a mathematical model using equations obtained as a result of the analysis with secondary models of regression coefficients from the Gompertz equation, to predict population of Lactococcus lactis subsp. lactis in the growing phase in reconstituted and sterilized powder milk, controlling temperature in a range of 9 to 39°C. Prediction was done by the analysis of “A” and “B” coefficients, using the square root model, obtaining R2 values = 0.819 and 0.991, respectively. Furthermore, a hyperbolic model was used for modeling “D” (R2= 0.812) and “M” (R2= 0.995) coefficients. Finally, a reparametrized expression of the original Gompertz model was obtained replacing each regression coefficient by the described equation, in relation to the different studied temperatures. Thus, this model was used to directly predict the population of microorganism under study at any temperature in the studied range. Small differences were obtained between both, the experimental and the adjusted populations, according to the application of the developed model. A well balanced distribution of the residual values was observed. The use of this type of models is recommended to describe growth of combined microorganism populations in products such as commercial pasteurized milk.

Assuntos

RESUMO

La microbiología predictiva es una herramienta útil para describir y predecir el crecimiento bacteriano en los alimentos. Para comparar la aplicación de diferentes modelos sigmoidales aplicados al crecimiento de Lactococcus lactis subsp. lactis, se analizaron muestras de leches tomadas del total del ordeño matutino de dos rebaños, bufalino (n=6) y vacuno (n=6), manejados bajo las mismas condiciones, ubicados en el municipio Mara del estado Zulia, Venezuela. Se determinaron algunos parámetros, tales como pH, acidez titulable y presencia de inhibidores. Se preparó una serie de tubos de ensayo con leche esterilizada a 110 ± 2°C por 10 min. y se inocularon con Lactococcus a 1 x 104 ufc/mL. Posteriormente, fueron incubados a 36 ± 0,5°C y cada dos horas se prepararon diluciones y siembra en placas con agar M-17. Se determinó el Log10 de ufc/mL para las 0; 2; 4; 6; 8 y 10 horas de incubación. Se realizó un análisis de regresión con los modelos de Gompertz, Logístico, Stannard y Richards, por medio del algoritmo de Marquardt. Se encontró similitud en el ajuste de los modelos aplicados. Los modelos de Stannard y Richards resultaron ser iguales en la estimación de los datos y fueron los que presentaron mayor dificultad para alcanzar el criterio de convergencia. El modelo de Gompertz de tres parámetros presentó diferencia significativa con los modelos de cuatro parámetros. Se considera los modelos de Gompertz y Logístico (con cuatro parámetros), modificados por Gibson, como los mejores para modelar y predecir el crecimiento del microorganismo en estudio.

Predictive Microbiology is a useful tool to describe and predict the bacterial growing in food. Milk samples from total morning milking of two herds, buffalo (n=6) and cattle (n=6), managed under similar conditions, located in Mara Municipality, Zulia State, Venezuela, were analyzed to compare the application of different sigmoid models to the growth of Lactococcus lactis subsp. lactis strain. Sanitary quality was determined by means of pH, acidity titration and detection of inhibitors. A series of assay tubes were prepared with sterilized milk at 110 ± 2°C for 10 min. The milk in the assay tubes was inoculated with Lactococcus at 1 x 104 ufc/mL. Then, the assay tubes were incubated at 36 ± 0.5°C and every two hours dilutions and culture were prepared in Petrie dishes with M-17 agar. Log10 of ufc/mL was determined for 0; 2; 4; 6; 8 y 10 hours post incubation. A regressions analysis with Gompertz, Logistic, Stannard and Richards models through Marquardt algorithm was used. Similarity was found when models were adjusted. The Stannard and Richards models were equal for the data estimation and they presented the greatest difficulty to reach the convergent criterion. The three parameters Gompertz model showed significant difference compare to the four parameters models. The Gompertz and Logistic models (with four parameters) modified by Gibson are considered the best to model and predict the growing of the microorganism under study.

Assuntos

RESUMO

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.

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.

Assuntos