Inactivation efficacy of a sixteen UVC LED module to control foodborne pathogens on selective media and sliced deli meat and spinach surfaces

Each year, >9 million foodborne illnesses are estimated to be caused by major pathogens acquired in the United States. Preventing these illnesses is challenging because resources are limited and linking individual illnesses to a particular food is rarely possible except during an outbreak. We developed a method of attributing illnesses to food commodities that uses data from outbreaks associated with both simple and complex foods. Using data from outbreak-associated illnesses for 1998–2008, we estimated annual US foodborne illnesses, hospitalizations, and deaths attributable to each of 17 food commodities. We attributed 46% of illnesses to produce and found that more deaths were attributed to poultry than to any other commodity. To the extent that these estimates reflect the commodities causing all foodborne illness, they indicate that efforts are particularly needed to prevent contamination of produce and poultry. Methods to incorporate data from other sources are needed to improve attribution estimates for some commodities and agents.

This contribution focuses on the presentation of GInaFiT (Geeraerd and Van Impe Inactivation Model Fitting Tool), a freeware Add-in for Microsoft Excel aiming at bridging the gap between people developing predictive modelling approaches and end-users in the food industry not familiar with or not disposing over advanced non-linear regression analysis tools. More precisely, the tool is useful for testing nine different types of microbial survival models on user-specific experimental data relating the evolution of the microbial population with time. As such, the authors believe to cover all known survivor curve shapes for vegetative bacterial cells. The nine model types are: (i) classical log-linear curves, (ii) curves displaying a so-called shoulder before a log-linear decrease is apparent, (iii) curves displaying a so-called tail after a log-linear decrease, (iv) survival curves displaying both shoulder and tailing behaviour, (v) concave curves, (vi) convex curves, (vii) convex/concave curves followed by tailing, (viii) biphasic inactivation kinetics, and (ix) biphasic inactivation kinetics preceded by a shoulder. Next to the obtained parameter values, the following statistical measures are automatically reported: standard errors of the parameter values, the Sum of Squared Errors, the Mean Sum of Squared Errors and its Root, the R(2) and the adjusted R(2). The tool can help the end-user to communicate the performance of food preservation processes in terms of the number of log cycles of reduction rather than the classical D-value and is downloadable via the KULeuven/BioTeC-homepage at the topic "Downloads" (Version 1.4, Release date April 2005).

Increased consumption, larger scale production and more efficient distribution of fresh produce over the past two decades have contributed to an increase in the number of illness outbreaks caused by this commodity. Pathogen contamination of fresh produce may originate before or after harvest, but once contaminated produce is difficult to sanitize. The prospect that some pathogens invade the vascular system of plants and establish "sub-clinical" infection needs to be better understood to enable estimation of its influence upon risk of human illness. Conventional surface sanitation methods can reduce the microbial load, but cannot eliminate pathogens if present. Chlorine dioxide, electrolyzed water, UV light, cold atmospheric plasma, hydrogen peroxide, organic acids and acidified sodium chlorite show promise, but irradiation at 1 kGy in high oxygen atmospheres may prove to be the most effective means to assure elimination of both surface and internal contamination of produce by pathogens. Pathogens of greatest current concern are Salmonella (tomatoes, seed sprouts and spices) and Escherichia coli O157:H7 on leafy greens (spinach and lettuce). This review considers new information on illness outbreaks caused by produce, identifies factors which influence their frequency and size and examines intervention effectiveness. Research needed to increase our understanding of the factors influencing microbial safety of fresh produce is addressed. Copyright © 2012 Elsevier Ltd. All rights reserved.