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The upper and lower limits of microbial colony count on agar plate are essential for microbial cell calculation of food samples. In the present study, the limits presented by the three standards of Ministry of Health, Labor, and Welfare of Japan, Food and Drug Administration of US (FDA), and Japanese Industrial Standards (JIS) were studied from the stochastic point of view. Here colony counts per plate were assumed to follow the Poisson distribution from our previous studies. The probability of acceptance of a pair of colony counts, P A for cell calculation by each standard was then calculated for various values of the mean of the Poisson distribution theoretically and by simulation with random sampling numbers. The values of P A at the lower and upper limits of the standards were low. For example, P A at the mean of 25 which is the lower limit of the FDA standard was 0.278. The values of P A of colony count pairs by the three standards were demonstrated in a wide range of the mean colony count. The largest range of the mean at P A >0.5 was observed in the JIS standard among the three. A new method to determine the limits for colony counts was developed with the risk of not acceptance. These findings would be helpful to consider the limits of colony counts for cell calculation.

Microbial colony counts of food samples in microbiological examinations are one of the most important items. The probability distributions for the colony counts per agar plate at the dilution of counting had not been intensively studied so far. Recently we analyzed the colony counts of food samples with several probability distributions using the Pearson's chi-square value by the "traditional" statistics as the index of fit [Fujikawa and Tsubaki, Food Hyg.Saf.Sc., 60, 88-95 (2019)]. As a result, the selected probability distributions depended on the samples. In this study we newly selected a probability distribution, namely a statistical model, suitable for the above data with the method of maximum likelihood from the probabilistic point of view. The Akaike's Information Criterion (AIC) was used as the index of fit. Consequently, the Poisson model were better than the negative binomial model for all of four food samples. The Poisson model was also better than the binomial for three of four microbial culture samples. With Baysian Information Criterion (BIC), the Poisson model was also better than these two models for all the samples. These results suggested that the Poisson distribution would be the best model to estimate the colony counts of food samples. The present study would be the first report on the statistical model selection for the colony counts of food samples with AIC and BIC.

Microbial risk assessment in food safety is a valuable tool to reduce the risks of infection by pathogens. The dose-response relation is aimed to establish the relationship between the dose of a pathogen that populations are exposed to and the probability of the adverse health effect by the pathogen. Among many dose-response models ever proposed, the exponential and beta-Poisson models have been internationally applied, but the decision on which model is selected between them solely depends on the goodness of fit to specific data sets. On the other hands, the log-logistic model, one of the alternative models, has been little studied on the dose-response relation. In the present study, thus, the application of the log-logistic model to dose-response relation was studied with hypothetical and experimental data sets of infection (or death), comparing to the above two models. Here the experimental data sets were for pathogenic organisms such as pathogenic Escherichia coli, Listeria monocytogenes, and Cryptosporidium pavrum. Consequently, this model successfully fit to those data sets in comparison to the two models. These results suggested that log-logistic model would have the potential to apply to the dose-response relation, similar to the exponential and beta-Poisson models.

Staphylococcus aureus food poisoning is caused by the intoxication of staphylococcal enterotoxin (SE) produced in foods. Staphylococcal food poisoning is mostly due to staphylococcal enterotoxin type A (SEA) among SEs. There have been many studies on the growth and SEA production of S. aureus in various foods, but few studies in bread. Thus, the SEA production by S. aureus in dough during fermentation and the SEA inactivation in dough during baking were studied in the normal production processes of bread in this study. No growth of S. aureus or SEA production in dough, whose total weight was about 470 g, was observed during the fermentation at 25 and 35℃ for four hr, suggesting that the risk of SEA production in dough during fermentation under these conditions would be negligible. Any SEA injected at 6.0 and 0.56 ng/g in dough could not be detected after 20 and 10 min of baking at 200℃, respectively. These results showed that the baking process, which was completed in 25 min, was enough to inactivate SEA at those doses of SEA in the dough. The results on the production and inactivation of SEA in dough during the production processes in this study would be useful information on microbiological food safety of bread making.

Microbial colony counts of concern of food products are one of the most important items in microbiological examinations. The distributions of colony counts per agar plate of food samples are considered to be reflected with microbial cell distributions in food homogenates. However, (i) the probabilistic distributions of the colony counts per agar plate at the dilution of counting and (ii) the relationship between the colony counts per plate and the number of agar plates for food samples have not been intensively studied so far. In this study, therefore, these two points were studied with raw food samples of raw minced beef and chicken and raw milk and microbial culture samples of Escherichia coli, Staphylococcus aureus, and Saccharomyces cerevisiae. Among four major probabilistic distributions, it was found that aerobic plate counts per plate of the foods were well described with negative binomial, Poisson, and normal distributions and that the colony counts per plate of microbial cultures were described well with binomial, Poisson, and normal distributions. The effect of the number of agar plates on the estimation of the mean of colony counts per plate of a sample was then studied with the data randomly resampled from the experimental data. The resampled data showed that with more number of plates the mean of counts fluctuated less and the coefficients of variation of colony counts per plate decreased further, which were coincident to the estimated by the central limit theory. Our study would provide useful information on the characteristics of colony counts per plate of food samples which are routinely examined.

Microbial concentrations of thermally processed foods are mostly low. Recently we proposed a stochastic method based on the MPN technique for estimating microbial concentration in a food product whose concentration is low. Namely, with the number of target pathogen-positive samples in the total samples of a food product by a qualitative examination, the concentration of the target microbe in the product was estimated with the stochastic method. However the credible interval of the estimate with the method was not yet studied. Also the method was not validated with foods yet. The present study showed the 95% credible interval of the estimation by the method with the maximum likelihood analysis. Also the present study showed the reliability of the method experimentally by using a powdered formulae for infants product and a liquid food model injected with Salmonella cells. This method could estimate as low as seven and two cells/1,000 g or 1,000 mL, respectively. Next, the estimated concentrations and 95% credible intervals of Enterobacteriaceae in fresh meat for eating raw were demonstrated at various Enterobacteriaceae-positive numbers of the sample with the present method. Further, the characteristics of the present method on the sample size and the aliquot of sample were clarified. These results suggested that the present method could be a good tool to verify microbiological safety of food products.

Microbial concentration in samples of a food product lot has been generally assumed to follow the log-normal distribution in food sampling, but this distribution cannot accommodate the concentration of zero. In the present study, first, a probabilistic study with the most probable number (MPN) technique was done for a target microbe present at a low (or zero) concentration in food products. Namely, based on the number of target pathogen-positive samples in the total samples of a product found by a qualitative, microbiological examination, the concentration of the pathogen in the product was estimated by means of the MPN technique. The effects of the sample size and the total sample number of a product were then examined. Second, operating characteristic (OC) curves for the concentration of a target microbe in a product lot were generated on the assumption that the concentration of a target microbe could be expressed with the Poisson distribution. OC curves for Salmonella and Cronobacter sakazakii in powdered formulae for infants and young children were successfully generated. The present study suggested that the MPN technique and the Poisson distribution would be useful for qualitative microbiological test data analysis for a target microbe whose concentration in a lot is expected to be low.

Recently we developed a novel method for estimating viable Salmonella cell numbers by means of a 5'-nuclease real-time PCR [Fujikawa et al., J. Food Hyg. Japan, 47, 151-156 (2006)]. The method was based on the increase kinetics of the target DNA region (inv A) of the microorganism growing in a culture medium during incubation. The index for the PCR was the threshold cycle. In this study, we validated the method for application in food. Namely, Salmonella cells spiked into ground chicken, pork, and beef and raw hamburger patty at various cell concentrations were cleaned up using buoyant density centrifugation and the Salmonella cell numbers were estimated with our method. Linear decreases in the threshold cycle value were observed during incubation of the samples. The standard curves for the cell number in all food samples were almost identical. With a standard curve using the mean parameter values, we successfully estimated viable Salmonella cell concentrations of the foods. The results indicate that our method is applicable for viable cell number estimation of the target microorganism in foods. Further, we used this method to study Salmonella growth in ground chicken stored at a constant temperature.

A predictive program for microbial growth under various temperature conditions was developed with a mathematical model. The model was a new logistic model recently developed by us. The program predicts Escherichia coli growth in broth, Staphylococcus aureus growth and its enterotoxin production in milk, and Vibrio parahaemolyticus growth in broth at various temperature patterns. The program, which was built with Microsoft Excel (Visual Basic Application), is user-friendly; users can easily input the temperature history of a test food and obtain the prediction instantly on the computer screen. The predicted growth and toxin production can be important indices to determine whether a food is microbiologically safe or not. This program should be a useful tool to confirm the microbial safety of commercial foods.

A novel method for estimating viable Salmonella Enteritidis cell counts with 5'-nuclease real-time PCR was developed in this study. Our method was based on the increase kinetics of the target DNA region (invA) of the microorganism growing in a food/clinical sample in a culture medium during incubation. The index of increase in the target DNA region studied here was threshold cycle, CT. A test Salmonella strain was grown in buffered peptone water at the optimal temperature (39 degrees C). As Salmonella cells were grown, the value of CT decreased with time, generating a downward sigmoidal curve. The slope of the curve was constant at various initial cell concentrations. With higher initial cell concentration, the CT value evaluated from the slope at a given time was lower. With this relationship, a novel method for estimating the initial viable cell concentration of a sample was developed. Dead Salmonella cells or bacteria other than the target cell caused deviation in the CT curve. Incubation in a selective media suppressed the deviation caused by other bacterial cells. We think that this method could be applied to many other microorganisms cultivable in a suitable medium.

We recently studied the growth characteristics of Escherichia coli cells in pouched mashed potatoes (Fujikawa et al., J. Food Hyg. Soc. Japan, 47, 95-98 (2006)). Using those experimental data, in the present study, we compared a logistic model newly developed by us with the modified Gompertz and the Baranyi models, which are used as growth models worldwide. Bacterial growth curves at constant temperatures in the range of 12 to 34 degrees C were successfully described with the new logistic model, as well as with the other models. The Baranyi gave the least error in cell number and our model gave the least error in the rate constant and the lag period. For dynamic temperature, our model successfully predicted the bacterial growth, whereas the Baranyi model considerably overestimated it. Also, there was a discrepancy between the growth curves described with the differential equations of the Baranyi model and those obtained with DMfit, a software program for Baranyi model fitting. These results indicate that the new logistic model can be used to predict bacterial growth in pouched food.

Characteristics of the growth kinetics of Escherichia coli cells in pouched mashed potatoes under various conditions were studied with a mathematical model. Bacterial cells were inoculated in sterile mashed potatoes and then sealed in vinyl pouches, in which a very small amount of air was included. The growth curves of cells in the pouched mashed potatoes at constant temperature (12-34 degrees C) were sigmoidal with time on a semi-logarithmic plot and were successfully described with a new logistic model recently developed by us. The rate constant of growth showed a highly linear relationship to the temperature with the square-root model, and the lag period was longer at lower temperatures. The growth curve in glass tubes containing a large volume of air was similar to that in pouches, showing that the rate of growth was not affected by the volume of the surrounding air. The growth curves in pouched mashed potatoes were very similar to those in nutrient broth or on the surface of nutrient agar, which we previously reported. These results suggested that the growth kinetics of the bacterial cells under various conditions of rich nutrition might be almost identical, and can be described with a simple growth model like ours.

Recently Fujikawa et al. [J. Food Hyg. Soc. Japan, 44, 155-160 (2003)] developed a new logistic model for bacterial growth. In the present study, an adjustment factor in the model was improved. The improved model could successfully describe growth curves of Escherichia coli and Salmonella in liquid media. In particular, the model could describe the linear growth at the early logarithmic phase more accurately than the previous model, being similar in this respect to the Baranyi model. However, the improved model more accurately predicted the rate constant of growth and the duration of the lag time as compared with the Baranyi model. These results showed that the improved model has the potential to successfully predict microbial growth.

A new logistic model for bacterial growth was developed in this study. The model is based on a logistic model, which is often applied for biological and ecological population kinetics. The new model is described by a differential equation and contains an additional term for suppression of the growth rate during the lag phase, compared with the original logistic equation. The new model successfully described sigmoidal growth curves of Escherichia coli and Salmonella under various initial conditions. Data for E. coli were obtained from our experiments and data for Salmonella from the literature. When the new model was compared with a modified Gompertz model, which is widely used by many predictive microbiology researchers, it proved to be superior to the Gompertz model. Further, Salmonella growth at varying temperature could be well simulated by the new model. These results indicate that the new model will be a useful tool to predict bacterial growth under various temperature profiles.

Recently we have developed new kinetic methods of bacterial cell estimation in food products by measurement of the increase in bacterial adenosine-triphosphate (ATP) content during incubation using a conventional firefly luminometer [Fujikawa and Morozumi: Jpn. J. Food Hyg. (2002)]. The methods were the end point method and the delay method. We, in this study, evaluated these methods for bacterial concentration estimation in commercial foods. The methods were successfully applied to food products inoculated with Escherichia coli or Staphylococcus aureus. The methods were then used to estimate bacterial concentrations in commercial foods. For estimation, the kinetic parameter values were determined first from the data of bacterial strains studied previously. The bacterial concentrations estimated with these parameter values were slightly smaller than the measured concentrations for most of the food samples. The parameter values obtained with the least-squares method gave better estimations in both methods. The delay method predicted the bacterial concentrations in food products better than the end point method. This study showed that with appropriate parameter values, these methods could be useful tools for bacterial concentration estimation of commercial foods.

New estimation methods of bacterial cell concentration in samples by measurement of the increase in bacterial adenosine-triphosphate (ATP) content during incubation using a conventional firefly luminometer were established. When an Escherichia coli cell suspension was incubated in nutrient broth, the increase in the ATP content of the suspension during the incubation period followed a sigmoidal curve. The increase ratio of the ATP content of the suspension at a given period of incubation (5 hours in this study) to the initial ATP content was greater at higher initial cell concentrations. With this relationship, the initial cell concentration of a test suspension could be predicted from the measured ratio; this was called the end point method. On the other hand, the lag period in the ATP increase curve was longer at lower initial cell concentrations. A highly linear relationship was observed between the lag period and the logarithm of the initial cell concentrations. Based on this relationship, a delay method was developed for prediction. The two relationships were also observed for bacterial suspensions of Klebsiella sp., Staphylococcus aureus, Bacillus subtilis, and Pseudomonas sp. These results suggested that the two methods have the potential to estimate the bacterial cell concentration of a sample suspension.

Production of volatile substances from edible oils and their constituents by Penicillium corylophilum was studied to clarify the mechanism of flavor production from a non-stick oil by the organism in a rice cake system. First, edible oils from plant and animal origins were tested for flavor production. Among the oils tested, coconut oil was the only one from which the flavor was produced. Second, triacylglycerols consisting of fatty acids with various lengths of carbon chain (C6 to C13) were studied for flavor production. Among the triacylglycerols tested, flavors were produced from those consisting of fatty acids with carbon chains of C6 to C11. The flavors consisted of methylketones and secondary alcohols, whose carbon chains were one carbon shorter than the precursor fatty acid molecules of the triacylglycerols. Flavors similar to that from the non-stick oil were produced from tricaprylin (C8), trinonanoin (C9), and tridecanoin (C10) among the triacylglycerols tested. Formation of mould spores was more strongly suppressed by triacylglycerols with shorter chain fatty acids. Third, fatty acids with various lengths of carbon chain (C7 to C15) were studied for flavor production. Among the fatty acids tested, flavors were produced from decanoic (C10) and undecanoic (C11) acids only. The flavors also consisted of methylketones and secondary alcohols one carbon shorter than the precursor fatty acids. Fatty acids with short carbon chains (C7 to C9) completely inhibited the mould growth. Our study showed that the range of carbon chain length of fatty acids capable of the flavor production (C10 to C11) was narrower than that of triacylglycerols (C6 to C11). It was also found that the non-stick oil and coconut oil contain tricaprylin and tridecanoin as triacylglycerols and decanoic acid as fatty acid.

Food hygiene in Japanese-style confectionery factories is hard to practice because the businesses are small. In a supporting system of voluntary-based hygienic management in this field, we microbiologically investigated the production processes of "Monaka" in a workshop in Tokyo. We microbiologically assessed the processing environments as well as the products in the workshop, then proposed some improvements in the production of the confectionery. After the improvements, microbial contamination of the processing environments was reduced and no microbial contamination was found in the sugared bean, or "An" produced, though the product "Monaka" was still contaminated, especially by molds. It was clarified that the molds came from contaminated baked wheat shells, or "Kawa" and further that the wheat shells were contaminated by molds during storage.

We studied how predictive microbiology models could practically be applied to HACCP plans with two predictive software programs that are currently available. The software programs were the Food Micromodel elaborated by the Ministry of Agriculture, Fisheries, and Food, U.K. and the Pathogen Modeling Program of Eastern Regional Research Center, U.S. Department of Agriculture. They successfully provided useful information on (i) the determination of Critical Control Points (CCPs), (ii) the estimation of critical limits at CCPs, (iii) the decision of abused products, (iv) the assessment of equivalence of HACCP plans, and further (v) the development of new products. With the information simulated by the software programs, HACCP teams could make scientific and objective decisions for developing their individual plans. It was also confirmed that microbiological process standards for food processing are indispensable for the application of the predictive programs to HACCP plans.

Natural flavor was accidentally produced from rice cake products in Japan. A non-stick oil had been sprayed on the products during the production process. It was found that a Penicillium corylophilum strain, a contaminant of the oil, produced the flavor from the oil. The ingredients of the flavor were four volatile substances, 2-heptanone, 2-nonanone, 2-heptanol, and 2-nonanol. Challenge tests with the mould strain in a rice cake system were performed under various conditions. The volatile substances were produced in the largest amounts at 25 degrees C, followed by 20 or 30 degrees C then 10 degrees C. 2-Heptanone was produced most remarkably at 25 degrees C, followed by 2-nonanone, 2-heptanol, and 2-nonanol. The growth patterns of the mould were similar between 20-30 degrees C, and the growth at 10 degrees C was delayed. The non-stick oil itself had neither flavor nor volatile substance. The flavor was also produced from coconut oil, which was one of the materials of the non-stick oil. No bacteria or yeasts tested produced any flavor from the non-stick oil, whereas most of the moulds tested produced flavor components.