Droplet Digital Polymerase Chain Reaction Assay for Quantifying Salmonella in Meat Samples
<i>Salmonella</i>, a major global foodborne pathogen, is a leading cause of salmonellosis. Quantitative detection of <i>Salmonella</i> provides a scientific basis for establishing microbiological criteria and conducting risk assessments. The plate count method remains the primary approach for bacterial quantification, whereas the most probable number (MPN) method is commonly used for detecting low levels of bacterial contamination. However, both methods are time-consuming and labor-intensive. Validated digital polymerase chain reaction (dPCR) techniques are emerging as promising alternatives because they enable rapid, absolute quantification with high specificity and sensitivity. Herein, we developed a novel droplet dPCR (ddPCR) assay for identifying and quantifying <i>Salmonella</i> using <i>invA</i> as the target. The assay demonstrated high specificity and sensitivity, with a limit of quantification of 1.1 × 10<sup>2</sup> colony-forming units/mL in meat samples. Furthermore, the log<sub>10</sub> values obtained via ddPCR and plate counting exhibited a strong linear relationship (R<sup>2</sup> > 0.99). Mathematical modeling of growth kinetics further confirmed a high correlation between plate count and ddPCR measurements (Pearson correlation coefficient: 0.996; calculated bias factor: 0.88). Collectively, these results indicate that ddPCR is a viable alternative to the MPN method and represents a powerful tool for the quantitative risk assessment of food safety.
Authors
- yingying liang
- Yangtai Liu (ORCID: https://orcid.org/0000-0003-2409-8666)
- Tianqi Shi (ORCID: https://orcid.org/0000-0003-4815-4175)
- X. Liu
- Huanyu Wu
- Hongzhi Zhang
- Jin Ding
- Min Chen
Institutions
- University of Shanghai for Science and Technology (CN)
- Shanghai Pudong Institute of Education Development (CN)
- Shanghai Municipal Center For Disease Control Prevention (CN)
Publication Details
- Journal
- Foods
- Published
- 2026-01-16
- DOI
- https://doi.org/10.3390/foods15020337
- Primary Topic
- Biosensors and Analytical Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00