Quantification of Salmonella in Raw Poultry Using Droplet Digital PCR With a Whole Cell, Enrichment-free Approach
Salmonella contamination in poultry remains a persistent public health challenge, despite ongoing regulatory efforts to reduce its presence. Traditional culture-based enrichment methods commonly employed for detecting Salmonella have inherent limitations, such as biases in bacterial growth and inacc...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
|
| Series: | Journal of Food Protection |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0362028X2500050X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Salmonella contamination in poultry remains a persistent public health challenge, despite ongoing regulatory efforts to reduce its presence. Traditional culture-based enrichment methods commonly employed for detecting Salmonella have inherent limitations, such as biases in bacterial growth and inaccurate quantification of initial contamination levels. This study introduces an enrichment-free approach for quantifying Salmonella in raw poultry, utilizing the Pathotrak system for bacterial separation and concentration, followed by Droplet Digital PCR (ddPCR) for absolute quantification. Our findings highlight that whole-cell preparations within ddPCR droplets yield significantly higher detection efficiency compared to extracted DNA, likely due to the reduced loss of genetic material during processing. The method was validated by correlating theoretical inoculation levels with ddPCR measurements, resulting in a strong linear relationship (R2 = 0.903) and a slope of 0.99, indicating high accuracy. Residuals from the regression model were normally distributed, confirming its validity. This platform offers a robust, rapid, and precise tool for detecting low levels of Salmonella in poultry products without enrichment, making it a more accurate and efficient approach to bacterial quantification in complex food matrices. |
|---|---|
| ISSN: | 0362-028X |