Bio-Mapping Salmonella and Campylobacter Loads in Three Commercial Broiler Processing Facilities in the United States to Identify Strategic Intervention Points

Abstract

The poultry industry in the United States is one of the largest in the world. Poultry consumption has significantly increase since the COVID-19 pandemic and is predicted to increase over 16% between 2021 and 2030. Two of the most significant causes of hospitalizations and death in the United States are highly related to poultry consumption. The FSIS regulates poultry processing, enforcing microbial performance standards based on Salmonella and Campylobacter prevalence in poultry processing establishments. This prevalence approach by itself is not a good indicator of food safety. More studies have shown that it is important to evaluate quantification along with prevalence, but there is not much information about poultry mapping using quantification and prevalence. In this study, enumeration and prevalence of Salmonella and Campylobacter were evaluated throughout the process at three different plants in the United States. Important locations were selected in this study to evaluate the effect of differences interventions. Even though there were high differences between the prevalences in the processes, some of the counts were not significantly different, and they were effective in maintaining pathogens at safe levels. Some of the results showed that the intervention and/or process were not well controlled, and they were not effective in controlling pathogens. This study shows that every plant environment is different, and every plant should be encouraged to implement a bio-mapping study. Quantification of pathogens leads to appropriate risk assessment, where physical and chemical interventions can be aimed at specific processing points with higher pathogen concentrations using different concentrations of overall process improvement.

Keywords: Campylobacter enumeration; Salmonella enumeration; poultry bio-mapping.

Figure 1

Salmonella counts (log CFU/sample) and prevalence (shown as solid blue line) comparison between sampling points during processing and parts. In every box plot, the median is represented by the horizontal line, and the upper and lower vertical lines, respectively, stand for 1.5 times the upper and lower interquartile ranges. The data points are represented by the dots. ^a–d for each location, boxes with distinct letters show statistically different between sampling points (p-value < 0.01). Limit of quantification for SalQuant (LOQ) is 1% of the real LOQ (0.6 log CFU/carcass), and limit of detection (LOD) is 50% of the new LOQ (0.3 log CFU/sample), both shown as dotted lines.

Figure 2

Salmonella counts (log CFU/sample) and prevalence (shown as solid blue line) comparison between sampling points during processing and parts. In every box plot, the median is represented by the horizontal line, and the upper and lower vertical lines, respectively, stand for 1.5 times the upper and lower interquartile ranges. The data points are represented by the dots. ^a–c for each location, boxes with distinct letters show statistically different between sampling points (p-value < 0.01). Limit of quantification for SalQuant (LOQ) is 1% of the real LOQ (0.6 log CFU/carcass), and limit of detection (LOD) is 50% of the new LOQ (0.3 log CFU/sample), both shown as dotted lines.

Figure 3

Salmonella counts (log CFU/sample) and prevalence (shown as solid blue line) comparison between sampling points during processing and parts. In every box plot, the median is represented by the horizontal line, and the upper and lower vertical lines, respectively, stand for 1.5 times the upper and lower interquartile ranges. The data points are represented by the dots. ^a–d for each location, boxes with distinct letters show statistically different between sampling points (p-value < 0.01). Limit of quantification for SalQuant (LOQ) is 1% of the real LOQ (0.6 log CFU/carcass), and limit of detection (LOD) is 50% of the new LOQ (0.3 log CFU/sample), both shown as dotted lines.