2019
DOI: 10.3390/microorganisms7060165
|View full text |Cite
|
Sign up to set email alerts
|

Modeling the Reduction of Salmonella spp. on Chicken Breasts and Wingettes during Scalding for QMRA of the Poultry Supply Chain in China

Abstract: The objective of this study was to develop predictive models for describing the inoculated Salmonella reductions on chicken during the scalding process in China. Salmonella reductions on chicken breasts at a 100 s treatment were 1.12 ± 0.07, 1.38 ± 0.01, and 2.17 ± 0.11 log CFU/g at scalding temperatures of 50, 60 and 70 °C, respectively. For chicken wingettes, 0.87 ± 0.02, 0.99 ± 0.14 and 1.11 ± 0.17 log CFU/g reductions were obtained at 50, 60 and 70 °C after the 100 s treatment, respectively. Greater bacter… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
5
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
8

Relationship

1
7

Authors

Journals

citations
Cited by 10 publications
(6 citation statements)
references
References 42 publications
1
5
0
Order By: Relevance
“…This further underline the necessity of assessing the risk of salmonellosis associated with different food commodities. Quantitative microbial risk assessment can be applied to model the exposure and probability of salmonellosis associated with consumption of poultry commodities, for strategizing of mitigation measures to reduce Salmonella infection burden [25].…”
Section: Discussionmentioning
confidence: 99%
“…This further underline the necessity of assessing the risk of salmonellosis associated with different food commodities. Quantitative microbial risk assessment can be applied to model the exposure and probability of salmonellosis associated with consumption of poultry commodities, for strategizing of mitigation measures to reduce Salmonella infection burden [25].…”
Section: Discussionmentioning
confidence: 99%
“…These latter additional studies covered the following topics: Predictive microbiology including modeling of microbial growth, inactivation. and survival along the meat chain [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ]; Estimation of the prevalence of contamination at several steps [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , ...…”
Section: Resultsmentioning
confidence: 99%
“…Predictive microbiology including modeling of microbial growth, inactivation. and survival along the meat chain [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ];…”
Section: Resultsmentioning
confidence: 99%
“…In the cases that no significant differences of bacterial reduction and transfer rate were observed, probability distribution model could provide all potential results [26]. Besides, bacterial reductions and transfer rates showed a large variation because of multiple uncertainties that were involved, as well as the inherent errors in microbial collection from surfaces and enumeration techniques.…”
Section: Methodsmentioning
confidence: 99%