This study investigated bacterial transfer rates between hands and other common surfaces involved in food preparation in the kitchen. Nalidixic acid–resistant Enterobacter aerogenes B199A was used ...

Human hands are an important source of microbial contamination of foods. However, published data on the effectiveness of handwashing and glove use in a foodservice setting are limited. Bacterial tr...

Many factors have been shown to influence bacterial transfer between surfaces, including surface type, bacterial species, moisture level, pressure, and friction, but the effect of inoculum size on bacterial transfer has not yet been established. Bacterial cross contamination rates during performance of common food service tasks were previously determined in our laboratory using nalidixic acid-resistant Enterobacter aerogenes . Eight different transfer rates were determined, each involving a minimum of 30 volunteers. The influence of source inoculum level on the percentage of bacteria transferred (percent transfer rates) and log ₁₀ CFU per recipient surface was determined using statistical analysis. The effect of inoculum size on transfer rate was highly statistically significant ( P < 0.0001) for all transfer rate data combined (352 observations) and for each individual cross contamination rate, except for data on contamination via transfer from chicken to hand through a glove barrier ( P = 0.1643). Where inoculum size on the source was greater, transfer rates were lower, and where inoculum size on the source was less, transfer rates were higher. The negative linear trend was more obvious for activities that had a larger range of inoculum sizes on the source surface. This phenomenon has serious implications for research seeking to determine bacterial cross contamination rates, since the different transfer efficiencies that were previously shown to be associated with certain activities may actually be the result of differing initial inoculum levels. The initial inoculum size on the source and the amount of bacteria transferred must both be considered to accurately determine bacterial transfer rates.

Percentage is widely used to describe different results in food microbiology, e.g., probability of microbial growth, percent inactivated, and percent of positive samples. Four sets of percentage data, percent-growth-positive, germination extent, probability for one cell to grow, and maximum fraction of positive tubes, were obtained from our own experiments and the literature. These data were modeled using linear and logistic regression. Five methods were used to compare the goodness of fit of the two models: percentage of predictions closer to observations, range of the differences (predicted value minus observed value), deviation of the model, linear regression between the observed and predicted values, and bias and accuracy factors. Logistic regression was a better predictor of at least 78% of the observations in all four data sets. In all cases, the deviation of logistic models was much smaller. The linear correlation between observations and logistic predictions was always stronger. Validation (accomplished using part of one data set) also demonstrated that the logistic model was more accurate in predicting new data points. Bias and accuracy factors were found to be less informative when evaluating models developed for percentage data, since neither of these indices can compare predictions at zero. Model simplification for the logistic model was demonstrated with one data set. The simplified model was as powerful in making predictions as the full linear model, and it also gave clearer insight in determining the key experimental factors.

Mathematical models were developed to predict the probability of yeast spoilage of cold-filled ready-to-drink beverages as a function of beverage formulation. A Box-Behnken experimental design included five variables, each at three levels: pH (2.8, 3.3, and 3.8), titratable acidity (0.20, 0.40, and 0.60%), sugar content (8.0, 12.0, and 16.0 °Brix), sodium benzoate concentration (100, 225, and 350 ppm), and potassium sorbate concentration (100, 225, and 350 ppm). Duplicate samples were inoculated with a yeast cocktail (100 μl/50 ml) consisting of equal proportions of Saccharomyces cerevisiae , Zygosaccharomyces bailii , and Candida lipolytica (∼5.0 × 10 ⁴ CFU/ml each). The inoculated samples were plated on malt extract agar after 0, 1, 2, 4, 6, and 8 weeks. Logistic regression was used to create the predictive models. The pH and sodium benzoate and potassium sorbate concentrations were found to be significant factors controlling the probability of yeast growth. Interaction terms for pH and each preservative were also significant in the predictive model. Neither the titratable acidity nor the sugar content of the model beverages was a significant predictor of yeast growth in the ranges tested.

Cross-contamination between foods and surfaces in food processing environments and home kitchens may play a significant role in foodborne disease transmission. This study quantifies the cross-contamination rates between a variety of fresh-cut produce and common kitchen surfaces (ceramic, stainless steel, glass, and plastic) using scenarios that differ by cross-contamination direction, surface type, produce type, and drying time/moisture level. A five-strain cocktail of rifampin-resistant Salmonella was used in transfer scenarios involving celery, carrot, and watermelon, and a five-strain cocktail of rifampin-resistant Escherichia coli O157:H7 was used in transfer scenarios involving lettuce. Produce or surface coupons were placed in buffer-filled filter bags and homogenized or massaged, respectively, to recover cells. The resulting solutions were serially diluted in 0.1% peptone and surface plated onto tryptic soy agar with 80 μg/ml rifampin and bismuth sulfite agar with 80 μg/ml rifampin for Salmonella or sorbitol MacConkey agar with 80 μg/ml rifampin for E. coli O157:H7. When the food contact surface was freshly inoculated, a high amount (>90%) of the inoculum was almost always transferred to the cut produce item. If the inoculated food contact surfaces were allowed to dry for 1 h, median transfer was generally >90% for carrots and watermelon but ranged from <1 to ∼70% for celery and lettuce. Freshly inoculated celery or lettuce transferred more bacteria (<2 to ∼25% of the inoculum) compared with freshly inoculated carrots or watermelon (approximately <1 to 8%). After 1 h of drying, the rate of transfer from inoculated celery, carrot, and lettuce was <0.01 to ∼5% and <1 to ∼5% for watermelon. Surface moisture and direction of transfer have the greatest influence on microbial transfer rates.