Growth of psychrotrophic bacteria in raw and UHT‐treated goats'milk

Cox, Julian; MacRae, I. C.

doi:10.1111/j.1365-2672.1988.tb05097.x

Cited by 9 publications

(3 citation statements)

References 11 publications

(3 reference statements)

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“…[1]) was used and growth parameters were directly obtained from nonlinear regression analysis (Table 2). The maximum growth rates for storage at 4°C, 15°C, and 29°C were 0.056, 0.17, and 0.46 h −1 , respectively, and the MGT were 6.42, 2.82, and 0.81 h, respectively, which are in agreement with published data for milk (Cox and MacRae, 1988).…”

Section: Growth Curves At Constant Temperaturesupporting

confidence: 90%

Prediction of growth of Pseudomonas fluorescens in milk during storage under fluctuating temperature

Hao

Shavezipur

Yousef

et al. 2016

Journal of Dairy Science

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Accurate prediction of growth of undesirable organisms (e.g., Pseudomonas fluorescens) in perishable foods (e.g., milk), held under sub-ideal storage conditions, can help ensure the quality and safety of these foods at the point of consumption. In this investigation, we inoculated sterile milk with P. fluorescens (~10(3) cfu/mL) and monitored inoculum growth behavior at constant and fluctuating storage temperatures. Three storage temperatures, 4 °C, 15 °C and 29 °C, were selected to simulate proper refrigeration conditions (4 °C) and temperature abuse, respectively. To simulate temperature fluctuation, milk held at 4 °C was subjected to temperature shifts to 15 °C or 29 °C for 4 to 6h. A modified logistic model was used to obtain the best-fit curve for the microbial growth under constant storage temperature. The specific growth rates at 4 °C, 15 °C, and 29 °C, obtained from experimental data, were 0.056 ± 0.00, 0.17 ± 0.05, and 0.46 ± 0.02 h(-1), respectively, and the lag time values were 29.5 ± 4.2, 12.7 ± 4.4, and 2.8 ± 0.3h, respectively. A model predicting bacterial growth under different temperature fluctuations was obtained using the growth parameters extracted from constant temperature experiments. Growth behavior predicted by the fluctuating temperature model and that obtained experimentally were in good agreement. Lag time exhibited a larger variation compared with specific growth rate, suggesting that it depends not only on growth temperature but also on the sample population and temperature gradient. Additionally, experimental data showed that changing the temperature during the lag phase induced an additional lag time before growth; however, no significant lag time was observed under the temperature fluctuation during the exponential phase. The results of this study provide information for precise shelf-life determination and reduction of food waste, particularly for milk and milk-containing food products.

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Section: Growth Curves At Constant Temperaturesupporting

confidence: 90%

Prediction of growth of Pseudomonas fluorescens in milk during storage under fluctuating temperature

Hao

Shavezipur

Yousef

et al. 2016

Journal of Dairy Science

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“…is not affected by other selected groups of bacteria; however, the bacteria indigenous to food may be more vigorous and competitive. Cox & MacRae (1988) observed that the growth of some psychrotrophic strains of Pseudomonas spp. inoculated into raw milk was slower than when inoculated in UHT milk.…”

Section: Discussionmentioning

confidence: 99%

“…According to some published data, our model can also underpredict the growth rate of pseudomonads in some situations. Examples for this are the data of Cox & MacRae (1988), in inoculated UHT goats’ milk, and those of Gill & Newton (1977), in inoculated aseptically obtained meat slices. With these data, the percentage bias indicators were negative, about −25% and −11%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Validating predictive models of food spoilage organisms

Sutherland

Baranyi

1999

Journal of Applied Microbiology

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C . P IN , J. P. S UT HE R LA ND A ND J. B AR AN Y I. 1999. The accuracy and bias of a predictive model for the maximum specific growth rate of Pseudomonas spp. were studied by means of percentage discrepancy and bias indicators. These were calculated for observations obtained both in laboratory media and in food. When independent pseudomonad data generated in broth were compared with model predictions, the error was smaller than in the case of food. The extent to which the food structure and composition of the microflora contribute to the overall error of the model was quantified.

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