Navigation

P3-48 A Predictive Growth Model of Aeromonas hydrophila on Chicken Breasts under Various Storage Temperatures

Wednesday, August 3, 2016
America's Center - St. Louis
Sung Dae Yang, BrainKorea21 Plus and Chung-Ang University, Ansung, Korea, The Republic of
Hyeon-Jo Bang, BrainKorea21 Plus and Chung-Ang University, Ansung, Korea, The Republic of
Seung-Hun Lee, BrainKorea21 Plus and Chung-Ang University, Ansung, Korea, The Republic of
Soo-Jin Jung, BrainKorea21 Plus and Chung-Ang University, Ansung, Korea, The Republic of
Shin Young Park, BrainKorea21 Plus and Chung-Ang University, Ansung, Korea, The Republic of
Yong-Soo Kim, Korea Health Industry Development Institute, Chungju, Korea, The Republic of
Sang-Do Ha, BrainKorea21 Plus and Chung-Ang University, Ansung, Korea, The Republic of
Introduction: Aeromonas hydrophila poses a threat to poultry meat because it can grow at refrigeration temperatures. A predictive model is a useful tool to improve food safety.

Purpose: This study developed a predictive growth model of A. hydrophila on chicken breast as a function of storage temperature (5~40oC) using a response surface model (RSM). The model can be used for controlling A. hydrophila without the need for detection of the organism and may be used for controlling growth.

Methods: Culture (102 CFU/g) as a cocktail of A. hydrophila (KCTC2358, KCTC12847, and KCCM11533) was inoculated on 5-10 spots on the surface of the breast. The breasts were stored at 5, 10, 20, 30, or 40oC. The lag time and growth rate fitted to the modified Gompertz equation and the relationship of the lag time and growth rate to the growth curves was modeled using an RSM. The assessment of the RSM for the growth A. hydrophila was evaluated using mean square error (MSE), bias (Bf) and accuracy factors (Af).

Results: The primary models of SGR and LT showed R2≥0.968 using the modified Gompertz equation. The SGRs at 5, 10, 20, and 30oC were 0.195, 0.239, 0.360, and 0.500 h-1, respectively. The LTs at 5, 10, 20, and 30 oC were 24.04, 19.19, 1.896, and 0.748 h, respectively. Secondary models were determined by nonlinear regression: SGR = 0.15014 + 0.00769*T + 0.00013*T2, LT = 37.17741 − 2.53399*T + 0.04334*T2. The appropriateness of secondary models was validated by MSE (0.00037 for SGR, 0.00174 for LT), Bf (1.0005 for SGR, 1.0006 for LT), Af (0.9995 for SGR, 0.9994 for LT), and R2 (0.999 for SGR, 0.961 for LT). Growth data at five randomized temperatures were predicted using these models, suggesting that these models can predict the A. hydrophila growth on chicken breasts.

Significance: Ultimately, the models of A. hydrophila growth could be used for effective monitoring of A. hydrophilacontamination on chicken breasts.

Back to: Poster Session 3 – Antimicrobials, Laboratory and Detection Methods, Microbial Food Spoilage, Modeling and Risk Assessment, Retail and Food Service Safety and Sanitation
<< Previous Abstract | Next Abstract >>