• Bulletin of Food Technology
• /
• v.22 no.2
• /
• pp.339-348
• /
• 2009

• Journal of Food Hygiene and Safety
• /
• v.16 no.2
• /
• pp.103-110
• /
• 2001

Predictive food microbiology(PFM) is an emerging area of food microbiology since the later 1980’s. It does apply mathematical models to predict the responses of microorganism to specified environmental variables. Although, at present, PFM models do not completely developed, models can provide very useful information for microbiological responses in HACCP(Hazard Analysis Critical Control Point) system and Risk Assessment. This study illustrates the possible use of PFM models(PMP: Pathogen Modeling Program win5.1) with milk in several elements in the HACCP system, such as conduction of hazard analysis and determination of CCP(Critical Control Points) and CL(Critical Limits). The factors likely to affect the growth of the pathogens in milk involved storage fixed factors were pH 6.7, Aw 0.993 and NaCl 1.3%. PMPwin5.1 calculated generation time, lag phase duration, time to level of infective dose for pathogens across a range of storage (Critical Control Points) and CL(Critical Limits). The factors likely to affect the growth of the pathogens in milk involved storage temperature, pH, Aw and NaCl content. The factors likely to affect the growth of the pathogens in milk involved storage temperature, pH, Aw and NaCl content. The variable factor was storage temperature at the range of 4~15$^{\circ}C$ and the fixed factors were pH 6.7, Aw 0.993 and NaC 1.3%. PMPwin5.1 calculated generation time, lag phase duration, time to level of infective dose for pathogens across a range of storage temperature.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.5
• /
• pp.2393-2393
• /
• 2014

• Food Science and Industry
• /
• v.43 no.1
• /
• pp.70-74
• /
• 2010

• Food Science and Biotechnology
• /
• v.15 no.5
• /
• pp.715-720
• /
• 2006

This study was performed to develop a predictive model for the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) using a response surface model with a combination of potassium lactate (PL), temperature, and pH. The growth parameters, specific growth rate (SGR), and lag time (LT) were obtained by fitting the data into the Gompertz equation and showed high fitness with a correlation coefficient of $R^2{\geq}0.9192$. The polynomial model was identified as an appropriate secondary model for SGR and LT based on the coefficient of determination for the developed model ($R^2\;=\;0.97$ for SGR and $R^2\;=\;0.86$ for LT). The induced values that were calculated using the developed secondary model indicated that the growth kinetics of L. monocytogenes were dependent on storage temperature, pH, and PL. Finally, the predicted model was validated using statistical indicators, such as coefficient of determination, mean square error, bias factor, and accuracy factor. Validation of the model demonstrates that the overall prediction agreed well with the observed data. However, the model developed for SGR showed better predictive ability than the model developed for LT, which can be seen from its statistical validation indices, with the exception of the bias factor ($B_f$ was 0.6 for SGR and 0.97 for LT).

• Korean Journal of Food Science and Technology
• /
• v.49 no.3
• /
• pp.274-278
• /
• 2017

In this study, predictive mathematical models were developed to estimate the kinetics of Staphylococcus aureus growth in processed meat product galbitang. Processed meat product galbitang was inoculated with 0.1 mL of S. aureus culture and stored at 4, 10, 20, $37^{\circ}C$. The ${\mu}_{max}$ (maximum specific growth rate) and LPD (lag phase duration) values were calculated. The primary model was used to develop a response surface secondary model. The growth parameters were analyzed using the square root model as a function of storage temperature. The developed model was confirmed by calculating RMSE (Root Mean Square Error) values as statistic parameters. The LPD decreased, but ${\mu}_{max}$ increased with an increase in the storage temperature. At 4, 10, 20 and $37^{\circ}C$, $R^2$ was 0.99, 0.98, 0.99 and 0.99, respectively; RMSE was 0.39. The developed predictive growth model can be used to predict the risk of S. aureus contamination in processed meat product galbitang; hence, it has potential as an input model for the risk assessment.

• Journal of Food Hygiene and Safety
• /
• v.26 no.2
• /
• pp.120-124
• /
• 2011

Predictive mathematical models were developed for predicting the kinetics of growth of Listeria monocytogenes in smoked salmon, which is the popular ready-to-eat foods in the world, as a function of temperature (4, 10, 20 and $30^{\circ}C$). At these storage temperature, the primary growth curve fit well ($r^2$=0.989~0.996) to a Gompertz equation to obtain specific growth rate (SGR) and lag time (LT). The Polynomial model for natural logarithm transformation of the SGR and LT as a function of temperature was obtained by nonlinear regression (Prism, version 4.0, GraphPad Software). Results indicate L. monocytogenes growth was affected by temperature mainly, and SGR model equation is $365.3-31.94^*Temperature+0.6661^*Temperature^{\wedge^2}$ and LT model equation is $0.1162-0.01674^*Temperature+0.0009303^*Temperature{\wedge^2}$. As storage temperature decreased $30^{\circ}C$ to $4^{\circ}C$, SGR decreased and LT increased respectively. Polynomial model was identified as appropriate secondary model for SGR and LT on the basis of most statistical indices such as bias factor (1.01 by SGR, 1.55 by LT) and accuracy factor (1.03 by SGR, 1.58 by LT).

• Journal of Microbiology and Biotechnology
• /
• v.19 no.9
• /
• pp.1051-1054
• /
• 2009

This cross-sectional study examined the distribution of HPV 58 sequence variation in Korean women for the first time. Among 1,750 Korean women, 53 women were positive for HPV 58 single infection, of whom 26 were without disease, 20 were with cervical intraepithelial neoplasia (CIN) 1, and 7 with CIN 2 or 3. Altogether, 36 different nucleotide sequence variations were identified with the L1, 20 within E2, 5 within E6, and 10 within E7. Further studies on variants of oncogenic HPVs are necessary, particularly for the purpose of developing more predictive HPV detection methods.

• Journal of Microbiology
• /
• v.43 no.5
• /
• pp.411-416
• /
• 2005

The high-throughput sequencing of microbial genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable research attention in recent years. This paper addresses the development of a predictive model, known as the dependence decomposition weight matrix model (DDWMM), which was designed to detect the core promoter region, including the -10 region and the transcription start sites (TSSs), in prokaryotic genomic DNA sequences. This is an issue of some importance with regard to genome annotation efforts. Our predictive model captures the most significant dependencies between positions (allowing for non­adjacent as well as adjacent dependencies) via the maximal dependence decomposition (MDD) procedure, which iteratively decomposes data sets into subsets, based on the significant dependence between positions in the promoter region to be modeled. Such dependencies may be intimately related to biological and structural concerns, since promoter elements are present in a variety of combinations, which are separated by various distances. In this respect, the DDWMM may prove to be appropriate with regard to the detection of core promoter regions and TSSs in long microbial genomic contigs. In order to demonstrate the effectiveness of our predictive model, we applied 10-fold cross-validation experiments on the 607 experimentally-verified promoter sequences, which evidenced good performance in terms of sensitivity.

• Journal of Food Hygiene and Safety
• /
• v.32 no.3
• /
• pp.206-210
• /
• 2017

This study was performed to develope mathematical models for predicting growth kinetics of Staphylococcus aureus in the processed meat product, pyeonyuk. Growth patterns of S. aureus in pyeonyuk were determined at the storage temperatures of 4, 10, 20, and $37^{\circ}C$ respectively. The number of S. aureus in pyeonyuk increased at all the storage temperatures. The maximum specific growth rate (${\mu}_{max}$) and lag phase duration (LPD) values were calculated by Baranyi model. The ${\mu}_{max}$ values went up, while the LPD values decreased as the storage temperature increased from $4^{\circ}C$ to $37^{\circ}C$. Square root model and polynomial model were used to develop the secondary models for ${\mu}_{max}$ and LPD, respectively. Root Mean Square Error (RMSE) was used to evaluate the developed model and the fitness was determind to be 0.42. Therefore the developed predictive model was useful to predict the growth of S. aureus in pyeonyuk and it will help to prevent food-born disease by expanding for microbial sanitary management guide.