Neural network models for growth of Salmonella serotypes in ground chicken subjected to temperature abuse during cold storage for application in HACCP and risk assessment

International Journal of Food Science and Technology

Volumn 52, Issue 1, 2017, Pages 214-221

  Oscar, Thomas P ^a  

^a UNIVERSITY OF MARYLAND EASTERN SHORE   (United States)

Author keywords

Ground chicken; hazard analysis and critical control point; neural network models; risk assessment; Salmonella; temperature abuse

Indexed keywords

ANIMALS; APPLICATION PROGRAMS; COLD STORAGE; DIGITAL STORAGE; FOOD MICROBIOLOGY; FOOD SAFETY; FOOD STORAGE; HAZARDS; SALMONELLA;

FAIL-DANGEROUS; FUNCTION OF TIME; GROUND CHICKEN; HAZARD ANALYSIS AND CRITICAL CONTROL POINTS; HUMAN PATHOGENS; NEURAL NETWORK MODEL; PREDICTIVE MICRO-BIOLOGY; TRAINING DATA;

RISK ASSESSMENT;

EID: 85005917666     PISSN: 09505423     EISSN: 13652621     Source Type: Journal    
DOI: 10.1111/ijfs.13242     Document Type: Article

References (11)

1. Evans, J. (1992). Consumer handling of chilled foods: perceptions and practice. International Journal of Refrigeration, 15, 290–298.
2. Kennedy, J., Jackson, V., Blair, I.S., McDowell, D.A., Cowan, C. & Bolton, D.J. (2005). Food safety knowledge of consumers and the microbiological and temperature status of their refrigerators. Journal of Food Protection, 68, 1421–1430.
3. Oscar, T.P. (2005). Development and validation of primary, secondary and tertiary models for predicting growth of Salmonella Typhimurium on sterile chicken. Journal of Food Protection, 68, 2606–2613.
4. Oscar, T.P. (2009). General regression neural network and Monte Carlo simulation model for survival and growth of Salmonella on raw chicken skin as a function of serotype, temperature, and time for use in risk assessment. Journal of Food Protection, 72, 2078–2087.
5. Oscar, T.P. (2013). Initial contamination of chicken parts with Salmonella at retail and cross-contamination of cooked chicken with Salmonella from raw chicken during meal preparation. Journal of Food Protection, 76, 33–39.
6. Oscar, T.P. (2014a). General regression neural network model for behavior of Salmonella on chicken meat during cold storage. Journal of Food Science, 79, M978–M987.
7. Oscar, T.P. (2014b). Use of enrichment real-time PCR to enumerate Salmonella on chicken parts. Journal of Food Protection, 77, 1086–1092.
8. 6 in ground chicken thigh meat during cold storage: extrapolation to other serotypes. Journal of Food Protection, 78, 1819–1827.
9. Pouillot, R., Lubran, M.B., Cates, S.C. & Dennis, S. (2010). Estimating parametric distributions of storage time and temperature of ready-to-eat foods for U.S. households. Journal of Food Protection, 73, 312–321.
10. Straver, J.M., Janssen, A.F., Linnemann, A.R., van Boekel, M.A., Beumer, R.R. & Zwietering, M.H. (2007). Number of Salmonella on chicken breast filet at retail level and its implications for public health risk. Journal of Food Protection, 70, 2045–2055.
11. Thomas, H.A. (1942). Bacterial densities from fermentation tube tests. Journal of American Water Works Association, 34, 572–576.