Computed tomography and predictive microbiology for non-invasive evaluation of the impact of dry-cured ham production process conditions on the behaviour of Listeria monocytogenes and Clostridium botulinum.

Variability of salt content in dry-cured ham production can pose microbiological food safety issues, especially in salt reduced and/or non-nitrified products. In this regard, computed tomography (CT) could help to non-invasively characterised the product to further adjust the production process and ensure its safety. The aim of this work was to study the application of CT to estimate a w in dry-cured ham to be used by predictive microbiology to evaluate the impact of the production process on the behaviour of Listeria monocytogenes and Clostridium botulinum. Effect of nitrite elimination and fat content of hams was also evaluated. Thirty hams with two different fat content levels were characterised analytically and using CT at different key points in the process. The safety of the process was evaluated by applying predictive microbiology using both analytical and CT data as model inputs. Results showed that nitrite and fat content had an impact on the predicted growth potential of the pathogens evaluated. After the resting period, if no nitrite is added, the time needed for 1 log increase (t inc) of L. monocytogenes would shorten by 26% and 22% in lean and fat hams, respectively. After week 12, important differences on t inc values for C. botulinum were found between both groups of hams (ca. 40% shorter in fat hams). CT can provide reliable pixel-to-pixel information for predictive microbiology to evaluate the growth of relevant pathogens, but further studies are needed to validate this combination as a tool to evaluate the safety of the production process. • Computed tomography (CT) allows non-invasive analysis of dry-cured ham (DCH). • Pathogens potential growth using predictive microbiology (PM) is higher in fat hams. • C. botulinum potential growth is 33% shorter in fat hams when no nitrite is added. • CT percentiles to be used as inputs in predictive microbiology (PM) were defined. • CT provides reliable information for PM to study the safety of DCH production. [ABSTRACT FROM AUTHOR]