Selective medium for investigation of C. perfringens in food, water and beverage was developed to substitute for FM-CW Agar due to no production of this medium in Japan. Five formulas of media: Clostridium welchii Brain Heart Infusion Agar (CWB), Clostridium welchii Agar (CWA), Loctose Egg-Yolk Agar (LEY), Liver Veal Agar (LV) and Modified Brain Heart Infusion Agar (Mod.BHI), were selected in this research. The quality and efficiency of these media was compared to FM-CW by using the reference standard culture of C. perfringens. The results showed that Mod.BHI is the most effective medium for observing the appearance of colony, the lecithinase reaction in egg yolk and Nagler reaction of organism. Therefore it was found that the Mod.BHI must be adjusted pH to 7.6 and boiled for sterilization then neomycin 400/ug/m/ was added. The developed medium and FM-CW were used to investigate C. perfringens, isolated from 35 samples of food, water and beverage and C. bifermentans isolated from 5 samples of food. The result showed that the developed medium, Mod. BHI, can be used to investigate C. perfringens in stead of FM-CW Agar.

In Japan, strategies for ensuring food safety have been developed without reliable scientific evidence on the relationship between foodborne diseases and food sources. This study aimed to provide information on the proportions of foodborne diseases caused by seven major causative pathogens (Campylobacter spp., Salmonella, enterohemorrhagic Escherichia coli [EHEC], Vibrio parahaemolyticus, Clostridium perfringens, Staphylococcus aureus, and norovirus) attributed to foods and to explore factors affecting changes in these source attribution proportions over time using analysis of outbreak surveillance data. For the calculation of the number of outbreaks attributed to each source, simple-food outbreaks were assigned to the single-food category in question, and complex-food outbreaks were classified under each category proportional to the estimated probability. During 2007 to 2018, 8,730 outbreaks of foodborne diseases caused by seven pathogens were reported, of which 6,690 (76.6%) were of unknown source. We estimated the following source attribution proportions of foodborne diseases: chicken products (80.3%, 95% uncertainty interval [UI] 80.1 to 80.4) for Campylobacter spp.; beef products (50.1%, UI 47.0 to 51.5) and vegetables (42.3%, UI 40.9 to 45.5) for EHEC; eggs (34.6%, UI 27.8 to 41.4) and vegetables (34.4%, UI 27.8 to 40.8) for Salmonella; finfish (50.3%, UI 33.3 to 66.7) and shellfish (49.7%, UI 33.3 to 66.7) for V. parahaemolyticus; grains and beans (57.8%, UI 49.7 to 64.9) for S. aureus; vegetables (63.6%, UI 48.5 to 74.6), chicken products (12.7%, UI 4.6 to 21.5), and beef products (11.1%, UI 8.5 to 13.1) for C. perfringens; and shellfish (75.5%, UI 74.7 to 76.2) for norovirus. In this study, we provide the best available evidence-based information to evaluate the link between foodborne diseases and foods. Our results on source attribution for Campylobacter spp. and EHEC suggest that the strict health regulations for raw beef were reflected in the proportions of these diseases attributed to this food.

Saeng-sikis a powdered ready-to-eat food with very low moisture that contains dried raw materials such as grains, fruits,mushrooms, and seaweeds. This product is consumed as a convenient and nutritious meal replacement. The objective of this study was to develop a mathematical model for predicting the survival of Clostridium perfringens vegetative cells and spores in saeng-sikas a function of temperature and to validate the model using saeng-siksamples with different microbial communities analyzed by matrix-assisted laser desorptionionization time-of-flight mass spectrometry. Kinetic data for C. perfringens survival in saeng-sikfit well to the Weibull model with high goodness off it (R(2) = 0.92 to 0.98). The obtained δ values (required time for first decimal reduction) for each temperature were 19.62 to 864.86 h, and concave curves (p < 1) were observed under all experimental conditions (5 to 40 degree C). Kinetic parameters were further described in a secondary model as a function of temperature using a Davey model (R(2) =0.99). The developed model was validated by the bias factor, accuracy factor, and root mean square error, and the values were within acceptable ranges for predictive models, even for saeng-sik samples with different microbial communities. When saeng-sikwas rehydrated according to the manufacturer’s recommendations, germination and outgrowth of C. perfringens was observed when the sample was subjected to unusual temperatures during storage, such as at 30 degree C for 15 h. C. perfringens spores survived in saeng-sik with very low water activity. Because C. perfringens could germinate and grow under such conditions, care must be taken to avoid initial contamination of C. perfringens during the manufacturing process. Our model developed with samples with different microbial communities provides useful information for next-generation microbiological risk assessment taking into consideration the ecology of the food-associated microbial community.

A predictive model was made for the logarithm of the thermal decimal reduction time (logD) of Salmonella enterica (D = time to 90% reduction by inactivation). The model was fitted with multiple linear regression from 521 logD-values reported in literature for laboratory media and foods highly varying in water activity and pH. The single regression model with temperature as the only variable had a high residual standard error (RSE) of 0.883 logD and no predictive value (fraction of variance explained (R²) < 0.001). Adding water activity, sugar content and pH as predictors resulted in a model with a lower RSE of 0.458 logD and an adjusted R² of 0.73. The model was validated by comparing 985 predicted with observed logD for S. enterica from other publications. The model was subsequently validated with 1498 published logD-values for inactivation of vegetative cells of nine other pathogenic bacteria genera (mainly Listeria monocytogenes, Escherichia coli, Clostridium perfringens, Cronobacter spp., Staphylococcus aureus, Yersinia enterocolitica) in or on a variety of laboratory media, meat, fish, dairy, nuts, fruits and vegetables. Regression analyses for validation with the 985 logD of S. enterica and 2483 logD of all genera show deviations from the expected slope of 1 (both 0.81) and the expected intercept of 0 (0.04 and 0.19 logD respectively). However, only 0.7% and 2% respectively of the new logD (expected: 0.5%) were observed above the 99% prediction interval of the original S. enterica model based on 521 logD. The findings suggest that i) the variability of thermal resistance of strains within species is larger than between genera and species; ii) one generic predictive model, also accounting for variability, suffices for designing the thermal inactivation of a variety of vegetative pathogenic bacteria in many food types.

A predictive model was made for the logarithm of the thermal decimal reduction time (logD) of Salmonella enterica (D = time to 90% reduction by inactivation). The model was fitted with multiple linear regression from 521 logD-values reported in literature for laboratory media and foods highly varying in water activity and pH. The single regression model with temperature as the only variable had a high residual standard error (RSE) of 0.883 logD and no predictive value (fraction of variance explained (R2) < 0.001). Adding water activity, sugar content and pH as predictors resulted in a model with a lower RSE of 0.458 logD and an adjusted R2 of 0.73. The model was validated by comparing 985 predicted with observed logD for S. enterica from other publications. The model was subsequently validated with 1498 published logD-values for inactivation of vegetative cells of nine other pathogenic bacteria genera (mainly Listeria monocytogenes, Escherichia coli, Clostridium perfringens, Cronobacter spp., Staphylococcus aureus, Yersinia enterocolitica) in or on a variety of laboratory media, meat, fish, dairy, nuts, fruits and vegetables. Regression analyses for validation with the 985 logD of S. enterica and 2483 logD of all genera show deviations from the expected slope of 1 (both 0.81) and the expected intercept of 0 (0.04 and 0.19 logD respectively). However, only 0.7% and 2% respectively of the new logD (expected: 0.5%) were observed above the 99% prediction interval of the original S. enterica model based on 521 logD. The findings suggest that i) the variability of thermal resistance of strains within species is larger than between genera and species; ii) one generic predictive model, also accounting for variability, suffices for designing the thermal inactivation of a variety of vegetative pathogenic bacteria in many food types.