K. Koyama

发表

Modeling Stochastic Variability in the Numbers of Surviving Salmonella enterica, Enterohemorrhagic Escherichia coli, and Listeria monocytogenes Cells at the Single-Cell Level in a Desiccated Environment

Shuso Kawamura, Shigenobu Koseki, Kento Koyama, 2016, Applied and Environmental Microbiology.

Determination of “Hass” Avocado Ripeness During Storage Based on Smartphone Image and Machine Learning Model

S. Koseki, B. Cho, K. Koyama, 2020, Food and Bioprocess Technology.

Modeling Invasion of Campylobacter jejuni into Human Small Intestinal Epithelial-Like Cells by Bayesian Inference

S. Koseki, K. Koyama, Hiroki Abe, 2020, Applied and Environmental Microbiology.

Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling

K. Koutsoumanis, S. Koseki, K. Koyama, 2019, Front. Microbiol..

Estimation of the probability of bacterial population survival: Development of a probability model to describe the variability in time to inactivation of Salmonella enterica.

S. Kawamura, S. Koseki, K. Koyama, 2017, Food microbiology.

Describing the Individual Spore Variability and the Parameter Uncertainty in Bacterial Survival Kinetics Model by Using Second-Order Monte Carlo Simulation

S. Koseki, K. Koyama, Hiroki Abe, 2020, Frontiers in Microbiology.

Development of a Maillard Reaction–Based Time-Temperature Integrator/indicator (TTI) for Visual Monitoring of Chilled Beef During Long-term Storage and Distribution

T. Ross, S. Koseki, K. Koyama, 2020, Food and Bioprocess Technology.

Stochastic evaluation of Salmonella enterica lethality during thermal inactivation.

S. Kawamura, S. Koseki, K. Koyama, 2018, International journal of food microbiology.

A New Dose-Response Model for Estimating the Infection Probability of Campylobacter jejuni Based on the Key Events Dose-Response Framework

S. Koseki, K. Koyama, Hiroki Abe, 2021, Applied and environmental microbiology.

Prediction of growth/no growth status of previously unseen bacterial strain using Raman spectroscopy and machine learning

S. Koseki, K. Koyama, J. N. Taylor, 2023, LWT.

Predicting sensory evaluation of spinach freshness using machine learning model and digital images

S. Koseki, B. Cho, K. Koyama, 2021, PloS one.

Antibacterial Properties of Melanoidins Produced from Various Combinations of Maillard Reaction against Pathogenic Bacteria

S. Koseki, K. Koyama, Satoshi Kukuminato, 2021, Microbiology spectrum.

Does the firmness vary within a single kiwifruit? Estimation of firmness distribution in individual fruit by compressed air deformation measurement

S. Koseki, K. Koyama, Rio Okaniwa, 2021, Journal of Food Measurement and Characterization.

Determination of ‘Hass’ avocado ripeness during storage by a smartphone camera using artificial neural network and support vector regression

S. Koseki, B. Cho, K. Koyama, 2021, Journal of Food Measurement and Characterization.

Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning.

S. Koseki, K. Koyama, Kyosuke Kubo, 2021, Journal of microbiological methods.

Competitive growth kinetics of Campylobacter jejuni, Escherichia coli O157:H7 and Listeria monocytogenes with enteric microflora in a small‐intestine model

S. Koseki, K. Koyama, Hiroki Abe, 2021, Journal of applied microbiology.

Implementing a New Dose-response Model for Estimating Infection Probability of Campylobacter Jejuni based on the Key Events Dose-response Framework

S. Koseki, K. Koyama, Hiroki Abe, 2021 .

Experimentally observed Campylobacter jejuni survival kinetics in chicken meat products during model gastric digestion tended to be lower than model predictions.

S. Koseki, K. Koyama, Hiroki Abe, 2021, Food microbiology.

Do bacterial cell numbers follow a theoretical Poisson distribution? Comparison of experimentally obtained numbers of single cells with random number generation via computer simulation.

S. Kawamura, S. Koseki, K. Koyama, 2016, Food microbiology.

Why Does Cronobacter sakazakii Survive for a Long Time in Dry Environments? Contribution of the Glass Transition of Dried Bacterial Cells

S. Koseki, K. Kawai, K. Koyama, 2021, Microbiology spectrum.

Classification of food spoilage bacterial species and their sodium chloride, sodium acetate and glycine tolerance using chemometrics analysis and Raman spectroscopy.

S. Koseki, K. Koyama, J. N. Taylor, 2021, Journal of microbiological methods.

Data mining for prediction and interpretation of bacterial population behavior in food

S. Koseki, K. Koyama, Shinji Nakaoka, 2022, Frontiers in Food Science and Technology.

Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database

S. Koseki, K. Koyama, Satoko Hiura, 2021, Scientific Reports.

Rapid detection and enumeration of aerobic mesophiles in raw foods using dielectrophoresis.

S. Koseki, K. Koyama, U. Ogawa, 2021, Journal of microbiological methods.

Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling

S. Koseki, J. Ranta, K. Koyama, 2021, Applied and environmental microbiology.

Survival Kinetics of Salmonella enterica and Enterohemorrhagic Escherichia coli on a Plastic Surface at Low Relative Humidity and on Low-Water Activity Foods.

S. Kawamura, S. Koseki, K. Koyama, 2016, Journal of food protection.

Stochastic modeling of variability in survival behavior of Bacillus simplex spore population during isothermal inactivation at the single cell level using a Monte Carlo simulation.

S. Kawamura, S. Koseki, K. Koyama, 2019, Food microbiology.

Development of automatic detection model for stem-end rots of ‘Hass’ avocado fruit using X-ray imaging and image processing

S. Koseki, K. Koyama, Tatsuki Kamata, 2022, Postharvest Biology and Technology.

Bayesian statistical modeling to describe uncertainty of thermal inactivation behaviour of bacterial spores

Hiroki Abe, Shigenobu Koseki, Shinya Doto, 2021 .

Recent advances in predictive microbiology: theory and application of conversion from population dynamics to individual cell heterogeneity during inactivation process

S. Koseki, K. Koyama, Hiroki Abe, 2021 .

Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty

S. Koseki, K. Koyama, Hiroki Abe, 2021, Frontiers in Microbiology.

Automated detection of internal fruit rot in Hass avocado via deep learning-based semantic segmentation of X-ray images

S. Koseki, H. Sugimori, K. Koyama, 2023, Postharvest Biology and Technology.

Soft-labeling approach along with an ensemble of models for predicting subjective freshness of spinach leaves

K. Koyama, Suxing Lyu, 2022, Comput. Electron. Agric..

-1-Implementing a new dose-response model for estimating infection probability of 1 Campylobacter based on the key events dose-response framework

S. Koseki, K. Koyama, Hiroki Abe, 2021 .

Calculating stochastic inactivation of individual cells in a bacterial population using variability in individual cell inactivation time and initial cell number.

S. Kawamura, K. Koyama, Hiroki Abe, 2019, Journal of theoretical biology.

Stochastic simulation for death probability of bacterial population considering variability in individual cell inactivation time and initial number of cells.

S. Kawamura, K. Koyama, Hiroki Abe, 2019, International journal of food microbiology.

Estimation of the probability of bacterial population survival: Development of a probability model to describe the variability in time to inactivation of Salmonella enterica.

S. Kawamura, K. Koyama, Hidekazu Hokunan, 2017, Food microbiology.