TY - JOUR
T1 - Modeling pathogen growth in meat products
T2 - Future challenges
AU - Shimoni, Eyal
AU - Labuza, Theodore P.
PY - 2000/11
Y1 - 2000/11
N2 - Meat products are perishable foods and unless stored under proper conditions spoil quickly. In addition, if pathogens are present, meat products may become hazardous for consumers. Pathogens such as Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. can grow and cause illness by the ingestion of the bacterial cells, therefore, assurance of meat safety and quality is of utmost importance. The emergence of low infectious dose pathogens, i.e. those that may cause disease at 1-10 organisms ingested, presents a significant challenge to predictive microbiology. In order to become a better tool for the meat industry and consumers, the mathematical models that form the basis for predicting microbial growth should (1) be validated in the actual food rather than in lab media, (2) take into account the cumulative effect of any temperature fluctuation that regularly occurs in distribution, and (3) keep in mind that pathogen initial count is usually unknown, and may be below the detection limit. This review presents some background on how to address these challenges.
AB - Meat products are perishable foods and unless stored under proper conditions spoil quickly. In addition, if pathogens are present, meat products may become hazardous for consumers. Pathogens such as Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. can grow and cause illness by the ingestion of the bacterial cells, therefore, assurance of meat safety and quality is of utmost importance. The emergence of low infectious dose pathogens, i.e. those that may cause disease at 1-10 organisms ingested, presents a significant challenge to predictive microbiology. In order to become a better tool for the meat industry and consumers, the mathematical models that form the basis for predicting microbial growth should (1) be validated in the actual food rather than in lab media, (2) take into account the cumulative effect of any temperature fluctuation that regularly occurs in distribution, and (3) keep in mind that pathogen initial count is usually unknown, and may be below the detection limit. This review presents some background on how to address these challenges.
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U2 - 10.1016/S0924-2244(01)00023-1
DO - 10.1016/S0924-2244(01)00023-1
M3 - Article
AN - SCOPUS:0034950356
SN - 0924-2244
VL - 11
SP - 394
EP - 402
JO - Trends in Food Science and Technology
JF - Trends in Food Science and Technology
IS - 11
ER -