Comparative studies on the correlation of Campylobacter spp. at different stages in the broiler production chain. (March 2022) - Dubovitskaya, O.; Valero, A.; Seinige, D.; Bungenstock, L.; Schill, F.; Kehrenberg, C.; Reich, F.
Abstract: Knowing the Campylobacter spp. load at each stage of production can be helpful in selecting appropriate control methods. The aim of the present study was therefore to evaluate the quantitative contamination of broilers with Campylobacter spp. at different levels of production (pre-harvest, harvest, and post-harvest) by using the culture method, and to identify the types of samples that can accurately predict the contamination level of the meat. For this purpose, broilers from two different processors were examined. The data were statistically modeled to describe the Campylobacter spp. counts in the samples and to identify correlations between counts in the different sample types, including fecal samples before slaughtering, cecal content at slaughter, neck skin after chilling, cut meat pieces, and finally meat cuts at the end of shelf-life. The Campylobacter spp. counts in pooled fecal samples at pre-harvest level were generally lower than those in individual cecal samples after slaughtering. The results of the meat samples at slaughter level showed that skinless breast fillet samples were less contaminated than legs with skin and neck skin after chilling. The examination of samples at the end of shelf-life showed that breast fillets and legs had lower Campylobacter spp. counts compared to the samples at the beginning of the storage. From these results, it can be summarized that a simple calculation of Campylobacter spp. counts from fecal samples collected on the farm did not allow a direct prediction of Campylobacter spp. loads in cecal content or neck samples. The cecal samples on the other hand can be appropriate to predict Campylobacter spp. counts in neck skin and breast fillet samples. Highlights: An average difference of 1 log CFU  Campylobacter  spp. between fecal and caecum samples was observed. Correlations of Campylobacter counts were found for different sample matrix comparisons. Tobit regression was performed to explore significance of input variables on Campylobacter counts. Campylobacter counts were significantly different between processors.