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. 2012 Aug;75(8):1382-93.
doi: 10.4315/0362-028X.JFP-11-548.

Detection and enumeration of four foodborne pathogens in raw commingled silo milk in the United States

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Free article

Detection and enumeration of four foodborne pathogens in raw commingled silo milk in the United States

Emily E Jackson et al. J Food Prot. 2012 Aug.
Free article

Abstract

A nationwide survey was conducted to obtain qualitative and quantitative data on bacterial contamination of raw commingled silo milk intended for pasteurization. The levels of total aerobic bacteria, total coliforms, Enterobacteriaceae, Escherichia coli, and Staphylococcus aureus were determined using the TEMPO system. The prevalence rates and levels of presumptive Bacillus cereus, E. coli O157:H7, Listeria monocytogenes, and Salmonella spp. were determined in 214 samples. B. cereus was detected in 8.91% of samples, at 3.0 to 93 CFU/ml. E. coli O157:H7 was detected in 3.79 to 9.05% of samples, at <0.0055 to 1.1 CFU/ml, depending on the assay utilized. Salmonella spp. were recovered from 21.96 to 57.94% of samples, at <0.0055 to 60 CFU/ml. L. monocytogenes was detected in 50.00% of samples, at <0.0055 to 30 CFU/ml. The average log-transformed counts of total viable bacteria were slightly lower in samples containing no pathogens. No correlation was observed between the levels of organisms detected with the TEMPO system and the presence or levels of any pathogen except E. coli O157:H7. A higher average log-transformed count of total viable bacteria was observed in samples positive for this organism. The high prevalence rates of target pathogens may be attributed to a variety of factors, including detection methods, sample size, and commingling of the milk in the silo. The effects of commingling likely contributed to the high prevalence rates and low levels of target pathogens because of the inclusion of milk from multiple bulk tanks. The high prevalence rates also may be the result of analysis of larger sample volumes using more sensitive detection methods. These quantitative data could be utilized to perform more accurate risk assessments and to better estimate the appropriate level of protection for dairy products and processing technologies.

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