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Estimating Bacterial Pathogen Levels in New Zealand Bulk Tank Milk
J C Marshall1, T K Soboleva2, P Jamieson3
1Institute of Fundamental Sciences, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North 4474, New Zealand. j.c.marshall@massey.ac.nz.
Journal of Food Protection
|June 15, 2016
Summary
A predictive model was developed to estimate pathogenic bacteria levels in raw milk, using total bacterial count as a proxy for fecal contamination. This helps assess risks associated with consuming raw milk.
Area of Science:
- Food Safety
- Microbiology
- Veterinary Public Health
Background:
- Zoonotic bacteria like Campylobacter, Listeria, and Shiga toxin-producing Escherichia coli are present in bulk tank milk.
- Raw milk consumption is linked to disease outbreaks, with fecal contamination during milking being a primary source of pathogens.
Purpose of the Study:
- To develop a predictive model for pathogenic bacteria levels in bulk tank raw milk.
- To assess the prevalence of specific pathogens (Campylobacter, E. coli O157, Listeria monocytogenes, Salmonella) in raw milk on New Zealand dairy farms.
Main Methods:
- Collected raw milk samples from 80 New Zealand dairy farms (2011-2012).
- Tested milk for Campylobacter, E. coli O157, Listeria monocytogenes, and Salmonella.
- Utilized total bacterial count as a proxy for fecal contamination and incorporated farm/animal shedding rates into a predictive model.
Main Results:
- A predictive model was developed using mixture distribution to account for varying contamination levels.
- The model's predictions for E. coli O157 and Salmonella prevalence closely matched observed data.
- Predicted Campylobacter prevalence was higher than observed, suggesting potential model refinement needs.
Conclusions:
- The developed model provides a valuable tool for estimating pathogenic bacteria in raw milk.
- Findings highlight the importance of monitoring fecal contamination in dairy herds to ensure raw milk safety.
- Further research may be needed to refine predictions for specific pathogens like Campylobacter.

