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ARS Home » Research » Publications at this Location » Publication #141905

Title: KINETIC ANALYSIS OF MICROBIAL GROWTH IN FOOD SYSTEMS UNDER ISOTHERMAL CONDITIONS

Author
item Huang, Lihan

Submitted to: Journal of Food Safety
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/7/2004
Publication Date: N/A
Citation: N/A

Interpretive Summary: For many years food microbiologists rely on empirical methods to estimate the growth of foodborne pathogens (such as Clostridium perfringens) in food systems. This research developed a new methodology based on the fundamental mechanisms governing the bacterial growth in food systems. This method allows for better description of the spoilage process caused by food, and therefore more accurate estimation of the bacterial growth. It can be a new tool for food safety regulatory agencies, food industry, and retailers to assess the microbial safety of foods.

Technical Abstract: A new kinetic model was proposed for describing the population growth of microorganisms under isothermal conditions in food systems. Microbial multiplication was simplified as a two-stage process after the initial inoculation or contamination. It was hypothesized that all the microbial cells inoculated to the food system were in the lag phase. After inoculation or initial contamination, the cells in the state of lag phase must exit this state before they can actively multiply. Immediately after the inoculated cells leave the state of the lag phase, they enter the second stage, or the state of division, where microbial cells begin to actively divide and multiply. Two differential equations were developed to kinetically describe the microbial multiplication process. The growth of Clostridium perfringens in ground beef was used to validate the kinetic model together artificially generated growth curves. The two differential equations were solved to fit the growth curves data using a computer algorithm developed in this study. Numerical computation results showed that the proposed methodology accurately describes experimental growth curves with different degrees of completeness.