A novel microfluidic mixer-based approach for determining inactivation kinetics of Escherichia coli O157:H7 in chlorine solutions

Zhang, Boce; Luo, Yaguang; Zhou, Bin; Wang, Qin; Millner, Patricia D.

doi:10.1016/j.fm.2015.01.013

Cited by 20 publications

(7 citation statements)

References 28 publications

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“…From the range given for the contact time above, we find that k w 2[150,300], while k c ranges from about 0.0048 to 0.0096. Performing similar calculations with inactivation of E. coli O157:H7 data from (Zhang et al, 2015), we find that k w z 276 and k c z .02. Here we use the result that 5 log 10 reduction is achieved in 0.25 s with 10 mg/l of FC (Zhang et al, 2015).…”

Section: Parameters Specific To Canadian Processingmentioning

confidence: 72%

“…Performing similar calculations with inactivation of E. coli O157:H7 data from (Zhang et al, 2015), we find that k w z 276 and k c z .02. Here we use the result that 5 log 10 reduction is achieved in 0.25 s with 10 mg/l of FC (Zhang et al, 2015). Because k c barely affects model outputs as it varies across its range, for simplicity we fix k c ¼ .0069 and do not include it in the sensitivity analysis.…”

Section: Parameters Specific To Canadian Processingmentioning

confidence: 72%

“…While the effective contact of FC with carcass surfaces (and therefore with microbes attached to carcass surfaces) during immersion chilling is, to our knowledge, not well documented in the literature, there are multiple studies quantifying inactivation rates of microbes in solution via FC; see (Helbling & VanBriesen, 2007;Zhang, Luo, Zhou, Wang, & Millner, 2015) and references therein. If we let k w > 0 be the inactivation rate of microbes in the chiller water, then we argue that the inactivation rate via FC of microbes on carcass surfaces can be written as ak w , where a2(0,1).…”

Section: Average Microbial Load On Carcasses and Organic Materials In mentioning

confidence: 99%

“…Parameter/Reference Baseline Range s 2 Â 10 4 CFU/kg 10 3 e10 6 (Cavani et al, 2010;Northcutt et al, 2008) r 0.15 0.05e0.30 Estimated q 0.011 0.005e0.03 (Northcutt et al, 2008;Tsai et al, 1992) b 0.01 (kg min) À1 0.001e0.1 (Munther & Wu, 2013;Northcutt et al, 2008), estimated b 0.077 min À1 0.04e0.1 (Northcutt et al, 2008) a 0.05 0.001e0.1 estimated k w 216 l/(mg min) 150e300 (Helbling & VanBriesen, 2007;Zhang et al, 2015) l 1.7 l/carcass 1e4 (Northcutt et al, 2008;CFIA, 2014) c 1 25 mg/l 0e50 (USDA, 2012;CFIA, 2014) h J 0.0017 (kg min) À1 0.0011e0.0022 (Tsai et al, 1992) Similarly, from Fig. 2(B) and (C), the cross-contamination term ranges over [10 À6.75 ,10 À3.3 ] and W * ranges over [10 À4.4 ,10 À3 ], respectively.…”

Section: Rules Of Thumb For E Coli Cross-contaminationmentioning

confidence: 99%

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Modeling cross-contamination during poultry processing: Dynamics in the chiller tank

et al. 2016

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a b s t r a c tUnderstanding mechanisms of cross-contamination during poultry processing is vital for effective pathogen control. As an initial step toward this goal, we develop a mathematical model of the chilling process in a typical high speed Canadian processing plant. An important attribute of our model is that it provides quantifiable links between processing control parameters and microbial levels, simplifying the complexity of these relationships for implementation into risk assessment models. We apply our model to generic, non-pathogenic Escherichia coli contamination on broiler carcasses, connecting microbial control with chlorine sanitization, organic load in the water, and pre-chiller E. coli levels on broiler carcasses. In particular, our results suggest that while chlorine control is important for reducing E. coli levels during chilling, it plays a less significant role in the management of cross-contamination issues.

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Section: Parameters Specific To Canadian Processingmentioning

confidence: 72%

Section: Parameters Specific To Canadian Processingmentioning

confidence: 72%

Section: Average Microbial Load On Carcasses and Organic Materials In mentioning

confidence: 99%

Section: Rules Of Thumb For E Coli Cross-contaminationmentioning

confidence: 99%

See 2 more Smart Citations

Modeling cross-contamination during poultry processing: Dynamics in the chiller tank

et al. 2016

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“…While the effective contact of FC with carcass surfaces during immersion chilling is complex due to surface morphology af fecting various degrees of microbial attachment [12], we take a sim plified approach (similar to the approach in [10]). For instance, mul tiple studies provide clear quantification of inactivation rates of microbes in solution via FC; see [13,14] and references therein. If we let k" > 0 be the inactivation rate of microbes in the chiller water, then we argue that the inactivation rate via FC of microbes on carcass surfaces can be written as akw, where a e (0, 1) (see [10] for more details).…”

Section: Microbial Dynamics In the Tankmentioning

confidence: 99%

Individual based modeling and analysis of pathogen levels in poultry chilling process

McCarthy

Smith

Fazil

et al. 2017

Mathematical Biosciences

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Pathogen control during poultry processing critically depends on more enhanced insight into contamination dynamics. In this study we build an individual based model (IBM) of the chilling process. Quantifying the relationships between typical Canadian processing specifications, water chemistry dynamics and pathogen levels both in the chiller water and on individual carcasses, the IBM is shown to provide a useful tool for risk management as it can inform risk assessment models. We apply the IBM to Campylobacter spp. contamination on broiler carcasses, illustrating how free chlorine (FC) sanitization, organic load in the water, and pre-chill carcass pathogen levels affect pathogen levels of post-chill broilers. In particular, given a uniform distribution of Campylobacter levels on incoming poultry we quantify the efficacy of FC control in not only reducing pathogen levels on average, but also the variation of pathogen levels on poultry exiting the chill tank. Furthermore, we demonstrate that the absence/presence of FC input dramatically influences when, during a continuous chilling operation, cross-contamination will be more likely.

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Microfluidic Platforms for Microbial

Zhou

Lin

2017

Cell Analysis on Microfluidics

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A novel microfluidic mixer-based approach for determining inactivation kinetics of Escherichia coli O157:H7 in chlorine solutions

Cited by 20 publications

References 28 publications

Modeling cross-contamination during poultry processing: Dynamics in the chiller tank

Modeling cross-contamination during poultry processing: Dynamics in the chiller tank

Individual based modeling and analysis of pathogen levels in poultry chilling process

Microfluidic Platforms for Microbial

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