Exposure assessment model to combine thermal inactivation (log reduction) and thermal injury (heat-treated spore lag time) effects on non-proteolytic Clostridium botulinum

SAFETY, RELIABILITY AND RISK ANALYSIS: THEORY, METHODS AND APPLICATIONS, VOLS 1-4(2009)

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摘要
Clostridium botulinum is one of the most severe hazards causing foodborne disease. There are well-established control procedures that have been identified to destroy or inhibit growth of non-proteolytic Clostridium botulinum spores. One of these used in the manufacture of chilled foods is application of the 'non-proteolytic C. botulinum cook', based on the characteristic that spores of non-proteolytic strains are considerably more heat sensitive than spores of proteolytic strains. It is generally agreed that a temperature of 90 degrees C for 10 min will deliver a 6D inactivation of non-proteolytic C. botulinum and this is a commonly used performance standard for the heat processing of chilled foods. However, under stress conditions provided by intrinsic (e.g. pH) or extrinsic factors (e.g. storage at chilled temperatures), heat-treated surviving spores need a certain time (namely lag time) before recovering from injury, germinating and then starting to grow. The objective of this work was to explore the possibility of identifying milder heat-treatments than 10 minutes at 90 degrees C, by incorporating this lag time with the 'traditional' thermal inactivation effect. To do so, an exposure assessment model, based on the Degree of Protection (DoP) concept was developed. Results are promising; for example, with a product having a pH of 6.0 and heat-treated at 85 degrees C for 10 min, shelf-lives above 21 days are expected. Under the same heat-treatment condition, if the pH is reduced to 5.7, shelf-lives above 50 days are expected as a significant pH effect has been identified. These examples are based on calculations including realistic chilled supply-chain temperatures, i.e. including some retailer/consumer refrigerator temperatures at 10 degrees C. This incorporation is possible with probabilistic methods since they take multiple-value inputs into account.
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