Quantitative microbiology: a basis for food safety.

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

Classical nonlinear models to describe the growth curve for Murrah buffalo breed

With the objective of to adjust nonlinear models for the growth curves for a buffaloes herd raised in floodable lands in Rio Grande do Sul state, monthly records measured from birth to two years-old of 64 males and 63 females born between 1982 and 1989 were used. The models used were: Von Bertalanffy, Brody, Gompertz and Logistic. The parameters were estimated by NLIN procedure and the criteria used to evaluate the adjustment given by the models were: asymptotic standard deviation; coefficient of determination; average absolute deviation of residues and asymptotic index. Von Bertalanffy and Brody models overestimated the male asymptotic weight (A) in 15.9 and 171.3kg, respectively, and the Gompertz and Logistic models underestimated it in 4.5 and 13.4kg, respectively. For females, the Logistic model underestimated the asymptotic weight (-2.09kg), and Gompertz, Von Bertalanffy and Brody overestimated this parameter in 8.04, 17.7, and 280.33kg, respectively. The biggest average deviation was estimated by Brody model for both sexes, characterizing the biggest index. Considering the criteria, it is recommended the Gompertz and Logistic models for adjust females and males Murrah buffaloes breed growth curves.Com o objetivo de ajustar modelos não-lineares ao crescimento ponderal para búfalos criados em terras baixas no Estado do Rio Grande do Sul, foram utilizados registros mensais mensurados do nascimento aos dois anos de idade de 64 machos e 63 fêmeas, nascidos no período de 1982 a 1989. Utilizaram-se os modelos: Von Bertalanffy, Brody, Gompertz e Logístico. Os parâmetros foram estimados usando o procedimento NLIN e os critérios utilizados para verificar o ajuste dos modelos foram: desvio padrão assintótico; coeficiente de determinação; desvio médio absoluto dos resíduos e o índice assintótico. Os modelos Von Bertalanffy e Brody superestimaram o peso assintótico (A) para os machos em 15,9 e 171,3kg, respectivamente, e os modelos Gompertz e Logístico, subestimaram em 4,5 e 13,4kg, respectivamente. Para as fêmeas, o modelo Logístico subestimou o peso assintótico (-2,09kg) e os modelos Gompertz, Von Bertalanffy e Brody superestimaram esse parâmetro em: 8,04; 17,7 e 280,33kg, respectivamente. O maior desvio médio absoluto foi estimado pelo modelo Brody para ambos os sexos, caracterizando o melhor índice. Considerando os critérios, recomenda-se o modelo Gompertz e o modelo Logístico para ajustar a curva de crescimento de fêmeas e machos da raça Murrah