Researcher basic information

• Ph.D (Agriculture), Hokkaido University, Mar. 2019

• https://researchmap.jp/koyama_kento?lang=en

• https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201901016277670444
• https://orcid.org/0000-0003-1521-2981
• https://www.afpe-hu.jp/
• https://www.afpe-hu.jp/en.html

• https://orcid.org/0000-0003-1521-2981

• 201901016277670444

• Statistical model
• Post‐harvest
• Predictive microbiology
• Sensory evaluation
• X-ray image

• Life Science, Food sciences
• Environmental Science/Agriculture Science, Agricultural environmental engineering and agricultural information engineering, Postharvest Biology and Technology
• Informatics, Statistical science

• Bachelor's degree program, School of Agriculture
• Master's degree program, Graduate School of Agriculture
• Doctoral (PhD) degree program, Graduate School of Agriculture

Research activity information

• Mar. 2024, 日本食品科学工学会北海道支部, 最優秀講演賞
  Viable but non-culturable の Campylobacter jejuni の感染力評価:培養能 回復・ヒト小腸上皮様細胞への侵入の検討
  Hiroya Hoshino, Tomohiro Murakami, Kento Koyama, Shige Koseki
• Oct. 2023, 第9回北海道大学部局横断シンポジウム, 銅賞
  非破壊測定と深層学習によるアボカドの内部腐敗の検出と抑制
  松井 貴大;杉森 博行;小関成樹;小山健斗
• Oct. 2023, 第9回北海道大学部局横断シンポジウム, 奨励賞
  Web上のデータベースと機械学習を用いた 細菌挙動の予測
  細江 隼平;砂川 純也;中岡 慎治;小関成樹;小山 健斗
• Jun. 2023, 12th International Conference on Predictive Modelling in Food, ICFMH Research Paper Award
  How do the survival kinetics of cross-contaminated foodborne bacteria differ in ground meat during thermal inactivation process?
  Hidemoto Yabe;Hiroki Abe;Kento Koyama;Shigenobu Koseki
• Apr. 2022, Leave a Nest Co., Ltd, Leave a Nest Grant -Global Challenge Award-
  Developing automatic detection model for fungal contamination of fruit using X-ray imaging and investigating fungal control
  Kento Koyama
• Sep. 2019, 11th International Conference of Predictive Modelling in Foods, Best Oral presentation Award
  Stochastic evaluation for survival bacterial numbers and the time-to-inactivation by combination of Weibull modelling and Monte Carlo simulation
  Satoko Hiura, Hiroki Abe, Kento Koyama, Shige Koseki
• Jul. 2019, 国際食品工業展（FOOMA）, FOOMA AP賞
  目で見てわかる温度の変化：農畜水産物の生産流通における積算温度インジケータの活用
  酒井健太郎;岡庭理央;小山健斗;小関成樹
• Jul. 2018, International Association for Food Protection, Student Travel Scholarship Award
  Kento Koyama
• Sep. 2017, 10th International Conference of Predictive Modelling in Foods, Best Poster Award
  Development of stochastic predictive model for survival probability of Salmonella enterica during thermal inactivation
  Hiroki Abe;Kento Koyama;Shuso Kawamura;Shige Koseki

• Spatio-temporal monitoring of maturation, ripening, and fungal rot development in “Hass” avocados using quantitative magnetic resonance imaging
  Takahiro Matsui; Guylaine Collewet; Kento Koyama; Maja Musse
  Postharvest Biology and Technology, 234, 114157, 114157, Elsevier BV, Apr. 2026, [Peer-reviewed]
  Scientific journal
• Development of a novel procedure for detecting bloody milk based on an objective quantification using opal-glass methods
  Takashi Sugawara; Shige Koseki; Kento Koyama
  Food Chemistry, 485, 144502, 144502, Elsevier BV, Sep. 2025, [Last author, Corresponding author]
  Scientific journal
• Modeling and Validation of the Effects of Amino Group Concentrations in Food on the Growth of Escherichia coli
  Masaki Kato; Kento Koyama; Shige Koseki
  Journal of Food Protection, 88, 6, 100512, 100512, Elsevier BV, May 2025
  Scientific journal
• Formation of Antibacterial Maillard Reaction Products From D-xylose and L-phenylalanine During Stewing Cooking
  Hitomi Hirakawa; Hiroshi Ono; Junko Shinozaki; Kento Koyama; Shigenobu Koseki
  Journal of Food Protection, 88, 6, 100503, 100503, Elsevier BV, May 2025
  Scientific journal
• The role of glass transition temperature (T) and storage temperature (T) in explaining the survival behavior of dried Bacillus cereus
  Tatsuya Inomata; Kiyoshi Kawai; Kento Koyama; Shigenobu Koseki
  International Journal of Food Microbiology, 435, 111162, 111162, Elsevier BV, May 2025
  Scientific journal
• Ranking Prediction of Fresh Produce Quality: A Case Study of Strawberry Ripeness
  Yukihisa Nagaki; Sei Abe; Shige Koseki; Kento Koyama
  Food and Bioprocess Technology, Springer Science and Business Media LLC, 23 Apr. 2025, [Last author, Corresponding author]
  Scientific journal
• Reconsidering Stochasticity in Modeling of Bacterial Population Growth and Inactivation With Technical and Biological Variability
  Kento Koyama; Zafiro Aspridou; Hiroki Abe; Konstantinos Koutsoumanis; Shige Koseki
  Journal of Food Protection, 88, 5, 100482, 100482, Elsevier BV, Apr. 2025, [Peer-reviewed], [Lead author, Corresponding author]
  Scientific journal
• When should postharvest avocado fruit rots be inspected via X-rays? Relationship between internal fruit rot development and skin color alteration in “Hass” avocados
  Takahiro Matsui; Shige Koseki; Kento Koyama
  Postharvest Biology and Technology, 222, 113369, 113369, Elsevier BV, Apr. 2025, [Last author, Corresponding author]
  Scientific journal
• Impact of Low Humidity on Suppressing Stem-End Rot in Avocado During Ripening
  Yoan Felanny Lembono; Takahiro Matsui; Shige Koseki; Kento Koyama
  Food and Bioprocess Technology, Springer Science and Business Media LLC, 18 Feb. 2025, [Last author, Corresponding author]
  Scientific journal
• Machine Learning Prediction of Leuconostoc spp. Growth Inducing Spoilage in Cooked Deli Foods Considering the Effect of Glycine and Sodium Acetate
  Mayumi Kataoka; Hiroshi Ono; Junko Shinozaki; Kento Koyama; Shigenobu Koseki
  Journal of Food Protection, 87, 12, 100380, 100380, Elsevier BV, Dec. 2024
  Scientific journal
• Effects of at-home-preparation on the risk of foodborne illness in Japan: Analysis of quantitative microbial risk assessment of Escherichia coli O157:H7, Salmonella Typhimurium, Listeria monocytogenes, and Campylobacter jejuni in lettuce salad made at home
  Hiroki Abe; Akihiro Ando; Kento Koyama; Shigenobu Koseki
  Microbial Risk Analysis, 27-28, 100316, 100316, Elsevier BV, Dec. 2024, [Peer-reviewed]
  Scientific journal
• Isolation and Identification of the Antibacterial Compounds Produced by Maillard Reaction of Xylose with Phenylalanine or Proline.
  Kazuho Aonishi; Shungo Miyao; Lisa Yokoi; Naoki Kitaoka; Kento Koyama; Hideyuki Matsuura; Shigenobu Koseki
  Journal of agricultural and food chemistry, 04 Jul. 2024, [International Magazine]
  English, Scientific journal, Maillard reaction products (MRPs) of xylose with phenylalanine and xylose with proline exhibit high antibacterial activity. However, the active antibacterial compounds in MRPs have not yet been identified or isolated. This study aimed to isolate the active compounds in the two antibacterial MRPs. The organic layer of the MRP solution was separated and purified using silica gel chromatography and high-performance liquid chromatography. The chemical structures of the isolated compounds were determined by mass spectrometry and nuclear magnetic resonance spectroscopy. The compounds inhibited the growth of Bacillus cereus and Salmonella Typhimurium at 25 °C for 7 days at a concentration of 0.25 mM. Furthermore, the isolated compounds inhibited the growth of naturally occurring microflora of lettuce and chicken thighs at 25 °C for 2 days at a concentration of 0.5-1.0 mM. The antibacterial compounds found in MRPs demonstrated a wide range of effectiveness and indicated their potential as alternative preservatives.
• 3-D stochastic modeling approach in thermal inactivation: estimation of thermal survival kinetics of Escherichia coli O157:H7 in a hamburger after exposure to desiccation stress
  Hidemoto Yabe; Hiroki Abe; Yoshiki Muramatsu; Kento Koyama; Shige Koseki
  Applied and Environmental Microbiology, 90, 6, American Society for Microbiology, 18 Jun. 2024, [Peer-reviewed]
  Scientific journal, ABSTRACT
  
  Desiccation tolerance of pathogenic bacteria is one strategy for survival in harsh environments, which has been studied extensively. However, the subsequent survival behavior of desiccation-stressed bacterial pathogens has not been clarified in detail. Herein, we demonstrated that the effect of desiccation stress on the thermotolerance of Escherichia coli O157:H7 in ground beef was limited, and its thermotolerance did not increase. E. coli O157:H7 was inoculated into a ground beef hamburger after exposure to desiccation stress. We combined a bacterial inactivation model with a heat transfer model to predict the survival kinetics of desiccation-stressed E. coli O157:H7 in a hamburger. The survival models were developed using the Weibull model for two-dimensional pouched thin beef patties (ca. 1 mm), ignoring the temperature gradient in the sample, and a three-dimensional thick beef patty (ca. 10 mm), considering the temperature gradient in the sample. The two-dimensional (2-D) and three-dimensional (3-D) models were subjected to stochastic variations of the estimated Weibull parameters obtained from 1,000 replicated bootstrapping based on isothermal experimental observations as uncertainties. Furthermore, the 3-D model incorporated temperature gradients in the sample calculated using the finite element method. The accuracies of both models were validated via experimental observations under non-isothermal conditions using 100 predictive simulations. The root mean squared errors in the log survival ratio of the 2-D and 3-D models for 100 simulations were 0.25–0.53 and 0.32–2.08, respectively, regardless of the desiccation stress duration (24 or 72 h). The developed approach will be useful for setting appropriate process control measures and quantitatively assessing food safety levels.
  
  IMPORTANCE
  
  Acquisition of desiccation stress tolerance in bacterial pathogens might increase thermotolerance as well and increase the risk of foodborne illnesses. If a desiccation-stressed pathogen enters a kneaded food product via cross-contamination from a food-contact surface and/or utensils, proper estimation of the internal temperature changes in the kneaded food during thermal processing is indispensable for predicting the survival kinetics of desiccation-stressed bacterial cells. Various survival kinetics prediction models that consider the uncertainty or variability of pathogenic bacteria during thermal processing have been developed. Furthermore, heat transfer processes in solid food can be estimated using finite element method software. The present study demonstrated that combining a heat transfer model with a bacterial inactivation model can predict the survival kinetics of desiccation-stressed bacteria in a ground meat sample, corresponding to the temperature gradient in a solid sample during thermal processing. Combining both modeling procedures would enable the estimation of appropriate bacterial survival kinetics in solid food.
• Hierarchal ranking sensory evaluation test of fresh produce quality: A case study of injured cherry tomato images
  Sei Abe; Takahiro Matsui; Shige Koseki; Kento Koyama
  Food Quality and Preference, 105167, 105167, Elsevier BV, Mar. 2024, [Peer-reviewed], [Last author, Corresponding author]
  Scientific journal
• Investigating the relationship between strawberry maturity and glossiness on the surface during storage through digital image analysis
  Raki Takemoto; Takashi Watanabe; Nobutaka Nakamura; Shige Koseki; Kento Koyama
  Journal of Food Measurement and Characterization, 18, 3, 1776, 1785, Springer Science and Business Media LLC, 20 Dec. 2023, [Last author, Corresponding author]
  Scientific journal
• Non-destructive quality classification of rice taste properties based on near-infrared spectroscopy and machine learning algorithms
  Edenio Olivares Díaz; Haruka Iino; Kento Koyama; Shuso Kawamura; Shigenobu Koseki; Suxing Lyu
  Food Chemistry, 429, 136907, 136907, Elsevier BV, Dec. 2023, [Peer-reviewed]
  Scientific journal
• Automated detection of internal fruit rot in Hass avocado via deep learning-based semantic segmentation of X-ray images
  Takahiro Matsui; Hiroyuki Sugimori; Shige Koseki; Kento Koyama
  Postharvest Biology and Technology, 203, Sep. 2023, [Last author, Corresponding author]
  Scientific journal
• Antibacterial effect of melanoidins derived from xylose and phenylalanine against Bacillus cereus and Clostridium perfringens
  Eisuke Maesaka; Satoshi Kukuminato; Kazuho Aonishi; Kento Koyama; Shigenobu Koseki
  Journal of Food Protection, 100140, 100140, Elsevier BV, Aug. 2023, [Peer-reviewed]
  Scientific journal
• Prediction of growth/no growth status of previously unseen bacterial strain using Raman spectroscopy and machine learning
  Takashi Yamamoto; J. Nicholas Taylor; Shige Koseki; Kento Koyama
  LWT, 114449, 114449, Elsevier BV, Jan. 2023, [Peer-reviewed], [Last author, Corresponding author]
  Scientific journal
• Data mining for prediction and interpretation of bacterial population behavior in food
  Junpei Hosoe; Junya Sunagawa; Shinji Nakaoka; Shige Koseki; Kento Koyama
  Frontiers in Food Science and Technology, Frontiers Media SA, 15 Dec. 2022, [Peer-reviewed], [Last author, Corresponding author]
  Scientific journal, Although bacterial population behavior has been investigated in a variety of foods in the past 40 years, it is difficult to obtain desired information from the mere juxtaposition of experimental data. We predicted the changes in the number of bacteria and visualize the effects of pH, a_w, and temperature using a data mining approach. Population growth and inactivation data on eight pathogenic and food spoilage bacteria under 5,025 environmental conditions were obtained from the ComBase database (www.combase.cc), including 15 food categories, and temperatures ranging from 0°C to 25°C. The eXtreme gradient boosting tree was used to predict population behavior. The root mean square error of the observed and predicted values was 1.23 log CFU/g. The data mining model extracted the growth inhibition for the investigated bacteria against a_w, temperature, and pH using the SHapley Additive eXplanations value. A data mining approach provides information concerning bacterial population behavior and how food ecosystems affect bacterial growth and inactivation.
• Mathematical model for analyzing the effect of storage conditions on the visually perceived freshness of strawberries via surface luminance distribution
  Takemoto, R.; Koyama, K.; Watanabe, T.; Koseki, S.; Nakamura, N.
  Food Packaging and Shelf Life, 34, 100965, 100965, Elsevier BV, Dec. 2022, [Peer-reviewed]
  Scientific journal
• Development of automatic detection model for stem-end rots of ‘Hass’ avocado fruit using X-ray imaging and image processing
  Matsui, T.; Kamata, T.; Koseki, S.; Koyama, K.
  Postharvest Biology and Technology, 192, 111996, 111996, Elsevier BV, Oct. 2022, [Peer-reviewed], [Last author, Corresponding author]
  Scientific journal
• Modelling the invasion of human small intestinal epithelial‐like cells by Salmonella enterica Typhimurium and Listeria monocytogenes using Bayesian inference
  Y. Takahashi; H. Abe; K. Koyama; S. Koseki
  Letters in Applied Microbiology, 75, 2, 388, 395, Wiley, Aug. 2022, [Peer-reviewed]
  Scientific journal, Abstract
  
  To develop a mechanistic bacterial dose–response model, based on the concept of Key Events Dose–Response Framework (KEDRF), this study aimed to investigate the invasion of intestinal model cells (Caco-2) by Salmonella Typhimurium and Listeria monocytogenes and described the behaviour of both pathogens as a mathematical model using Bayesian inference. Monolayer-cultured Caco-2 cells (approximately 105 cells) were co-cultured with various concentrations (103–107 colony forming unit [CFU] ml−1) of Salm. Typhimurium and L. monocytogenes for up to 9 h to investigate the invasion of the pathogens into the Caco-2 cells. While an exposure of ≥103 CFU ml−1 of Salm. Typhimurium initiated the invasion of Caco-2 cells within 3 h, much less exposure (102 CFU ml−1) of L. monocytogenes was sufficient for invasion within the same period. Furthermore, while the maximum number of invading Salm. Typhimurium cells reached by approximately 103 CFU cm−2 for 6-h exposure, the invading maximum numbers of L. monocytogenes cells increased by approximately 106 CFU cm−2 for the same exposure period. The invasion kinetics of both the pathogens was successfully described as an asymptotic exponential mathematical model using Bayesian inference. The developed pathogen invasion model allowed the estimation of probability of Salm. Typhimurium and L. monocytogenes infection, based on the physiological natures of digestion process, which was comparable to the published dose–response relationship. The invasion models developed in the present study will play a key role in the development of an alternative pathogen dose–response model based on KEDRF concept.
• Application of Time–Temperature Indicator/Integrator Based on the Maillard Reaction to Frozen Food Distribution
  Marin Tsujihashi; Saki Tanaka; Kento Koayama; Shigenobu Koseki
  Food and Bioprocess Technology, 15, 6, 1343, 1358, Springer Science and Business Media LLC, Jun. 2022, [Peer-reviewed]
  Scientific journal
• Experimentally observed Campylobacter jejuni survival kinetics in chicken meat products during model gastric digestion tended to be lower than model predictions
  Kohei Takeoka; Hiroki Abe; Kento Koyama; Shigenobu Koseki
  Food Microbiology, 102, 103932, 103932, Elsevier {BV}, Apr. 2022, [Peer-reviewed]
  English, Scientific journal
• Soft-labeling approach along with an ensemble of models for predicting subjective freshness of spinach leaves
  Kento Koyama; Suxing Lyu
  Computers and Electronics in Agriculture, 193, 106633, 106633, Elsevier {BV}, Feb. 2022, [Peer-reviewed], [Lead author, Corresponding author]
  English, Scientific journal
• Does the firmness vary within a single kiwifruit? Estimation of firmness distribution in individual fruit by compressed air deformation measurement
  Rio Okaniwa; Kento Koyama; Shigenobu Koseki
  Journal of Food Measurement and Characterization, 16, 1, 12, 18, Springer Science and Business Media {LLC}, Feb. 2022, [Peer-reviewed]
  English, Scientific journal
• Correction to: Application of Time–temperature Indicator/Integrator Based on the Maillard Reaction to Frozen Food Distribution (Food and Bioprocess Technology, (2022), 15, 6, (1343-1358), 10.1007/s11947-022-02821-4)
  Tsujihashi, M.; Tanaka, S.; Koyama, K.; Koseki, S.
  Food and Bioprocess Technology, 15, 9, 2134, 2134, 2022
  Scientific journal
• Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning
  Kento Koyama; Kyosuke Kubo; Satoko Hiura; Shige Koseki
  Journal of Microbiological Methods, 192, 106366, 106366, Elsevier {BV}, Jan. 2022, [Lead author, Corresponding author]
  English, Scientific journal
• Why Does Cronobacter sakazakii Survive for a Long Time in Dry Environments? Contribution of the Glass Transition of Dried Bacterial Cells
  Kyeongmin Lee; Kento Koyama; Kiyoshi Kawai; Shigenobu Koseki
  Microbiology Spectrum, 9, 3, American Society for Microbiology, 22 Dec. 2021, [Peer-reviewed]
  English, Scientific journal, The mechanical glass transition temperature ( T _g ) of dried Cronobacter sakazakii cells varied depending on differences in drying methods and water activity (a _w ) levels. Because the T _g of the dried bacterial cells varied depending on the drying method and a _w , the T _g will play an important role as an operational factor in the optimization of dry food processing for controlling microbial contamination in the future.
• Antibacterial Properties of Melanoidins Produced from Various Combinations of Maillard Reaction against Pathogenic Bacteria
  Satoshi Kukuminato; Kento Koyama; Shigenobu Koseki
  Microbiology Spectrum, 9, 3, American Society for Microbiology, 22 Dec. 2021, [Peer-reviewed]
  English, Scientific journal, Although the antimicrobial effect of melanoidins has been reported in some foods, there have been few comprehensive investigations on the antimicrobial activity of combinations of reaction substrates of the Maillard reaction. The present study comprehensively investigated the potential of various combinations of reducing sugars and amino acids. Because the melanoidins examined in this study were produced simply by heating in an autoclave at 121°C for 60 min, the of the targeted melanoidins can be easily produced.
• Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database
  Satoko Hiura; Shige Koseki; Kento Koyama
  Scientific Reports, 11, 1, Springer Science and Business Media {LLC}, Dec. 2021, [Peer-reviewed], [Corresponding author], [International Magazine]
  English, Scientific journal, In predictive microbiology, statistical models are employed to predict bacterial population behavior in food using environmental factors such as temperature, pH, and water activity. As the amount and complexity of data increase, handling all data with high-dimensional variables becomes a difficult task. We propose a data mining approach to predict bacterial behavior using a database of microbial responses to food environments. Listeria monocytogenes, which is one of pathogens, population growth and inactivation data under 1,007 environmental conditions, including five food categories (beef, culture medium, pork, seafood, and vegetables) and temperatures ranging from 0 to 25 °C, were obtained from the ComBase database (www.combase.cc). We used eXtreme gradient boosting tree, a machine learning algorithm, to predict bacterial population behavior from eight explanatory variables: ‘time’, ‘temperature’, ‘pH’, ‘water activity’, ‘initial cell counts’, ‘whether the viable count is initial cell number’, and two types of categories regarding food. The root mean square error of the observed and predicted values was approximately 1.0 log CFU regardless of food category, and this suggests the possibility of predicting viable bacterial counts in various foods. The data mining approach examined here will enable the prediction of bacterial population behavior in food by identifying hidden patterns within a large amount of data.
• Bayesian statistical modeling to describe uncertainty of thermal inactivation behaviour of bacterial spores
  Shinya Doto; Hiroki Abe; Kento Koyama; Shigenobu Koseki
  Food Control, 130, 108288, 108288, Elsevier {BV}, Dec. 2021, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• Classification of food spoilage bacterial species and their sodium chloride, sodium acetate and glycine tolerance using chemometrics analysis and Raman spectroscopy
  Takashi Yamamoto; J. Nicholas Taylor; Shige Koseki; Kento Koyama
  Journal of Microbiological Methods, 190, 106326, 106326, Elsevier {BV}, Nov. 2021, [Peer-reviewed], [Corresponding author], [International Magazine]
  English, Scientific journal
• A New Dose-Response Model for Estimating the Infection Probability of Campylobacter jejuni Based on the Key Events Dose-Response Framework
  Hiroki Abe; Kohei Takeoka; Yuto Fuchisawa; Kento Koyama; Shigenobu Koseki
  Applied and Environmental Microbiology, 87, 20, e0129921, American Society for Microbiology, 28 Sep. 2021, [Peer-reviewed], [International Magazine]
  English, Scientific journal, Based on the mechanistic approach called the key events dose-response framework (KEDRF), an alternative to previous nonmechanistic approaches, the dose-response models for infection probability of
  C. jejuni
  were developed considering with age of people who ingest pathogen and food type. The developed predictive framework illustrates highly accurate prediction of dose (minimum difference 0.21 log CFU) for a certain infection probability compared with the previously reported dose-response relationship.
• Competitive growth kinetics of Campylobacter jejuni, Escherichia coli O157:H7 and Listeria monocytogenes with enteric microflora in a small‐intestine model
  Yuto Fuchisawa; Hiroki Abe; Kento Koyama; Shigenobu Koseki
  Journal of Applied Microbiology, 132, 2, 1467, 1478, Wiley, 23 Sep. 2021, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• Application of growth rate from kinetic model to calculate stochastic growth of a bacteria population at low contamination level
  Kento Koyama; Satoko Hiura; Hiroki Abe; Shige Koseki
  Journal of Theoretical Biology, 525, 110758, 110758, Elsevier BV, Sep. 2021, [Peer-reviewed], [Lead author, Corresponding author], [International Magazine]
  English, Scientific journal, Traditional predictive microbiology is not suited for cell growth predictions for low-level contamination, where individual cell heterogeneity becomes apparent. Accordingly, we simulated a stochastic birth process of bacteria population using kinetic parameters. We predicted the variation in behavior of Salmonella enterica serovar Typhimurium cells at low inoculum density. The modeled cells were grown in tryptic soy broth at 25 °C. Kinetic growth parameters were first determined empirically for an initial cell number of 104 cells. Monte Carlo simulation based on the growth kinetics and Poisson distribution for different initial cell numbers predicted the results of 50 replicate growth experiments with the initial cell number of 1, 10, and 64 cells. Indeed, measured behavior of 85% cells fell within the 95% prediction area of the simulation. The calculations link the kinetic and stochastic birth process with Poisson distribution. The developed model can be used to calculate the probability distribution of population size for exposure assessment and for the evaluation of a probability that a pathogen would exceed critical contamination level during food storage.
• Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling
  Kento Koyama; Jukka Ranta; Kohei Takeoka; Hiroki Abe; Shige Koseki
  Applied and Environmental Microbiology, 87, 15, e0091821, American Society for Microbiology, 13 Jul. 2021, [Peer-reviewed], [Lead author, Corresponding author], ［Internationally co-authored］, [International Magazine]
  English, Scientific journal, Since microbial strains vary in their growth and activation patterns in food materials, it is important to accurately predict these patterns for quantitative microbial risk assessment. However, most previous studies in this area have used highly resistant strains, which could lead to inaccurate predictions.
• Rapid detection and enumeration of aerobic mesophiles in raw foods using dielectrophoresis
  Umi Ogawa; Kento Koyama; Shigenobu Koseki
  Journal of Microbiological Methods, 186, 106251, 106251, Jul. 2021, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty
  Satoko Hiura; Hiroki Abe; Kento Koyama; Shige Koseki
  Frontiers in Microbiology, 12, 674364, 674364, Frontiers Media SA, 24 Jun. 2021, [Peer-reviewed], [Corresponding author], [International Magazine]
  English, Scientific journal, Conventional regression analysis using the least-squares method has been applied to describe bacterial behavior logarithmically. However, only the normal distribution is used as the error distribution in the least-squares method, and the variability and uncertainty related to bacterial behavior are not considered. In this paper, we propose Bayesian statistical modeling based on a generalized linear model (GLM) that considers variability and uncertainty while fitting the model to colony count data. We investigated the inactivation kinetic data of Bacillus simplex with an initial cell count of 10⁵ and the growth kinetic data of Listeria monocytogenes with an initial cell count of 10⁴. The residual of the GLM was described using a Poisson distribution for the initial cell number and inactivation process and using a negative binomial distribution for the cell number variation during growth. The model parameters could be obtained considering the uncertainty by Bayesian inference. The Bayesian GLM successfully described the results of over 50 replications of bacterial inactivation with average of initial cell numbers of 10¹, 10², and 10³ and growth with average of initial cell numbers of 10^–1, 10⁰, and 10¹. The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 10¹ were within the 95% prediction interval. In addition, parameter uncertainty could be expressed as an arbitrary probability distribution. The analysis procedures can be consistently applied to the simulation process through fitting. The Bayesian inference method based on the GLM clearly explains the variability and uncertainty in bacterial population behavior, which can serve as useful information for risk assessment related to food borne pathogens.
• Recent advances in predictive microbiology: theory and application of conversion from population dynamics to individual cell heterogeneity during inactivation process
  Shige Koseki; Kento Koyama; Hiroki Abe
  Current Opinion in Food Science, 39, 60, 67, Elsevier {BV}, Jun. 2021, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• How many repetitions per condition are required for developing a stable growth/no growth boundary model for Bacillus simplex spores?
  Natsuki Tsuruma; Shinya Doto; Wataru Ishida; Kento Koyama; Shigenobu Koseki
  Food Control, 122, 107756, 107756, Elsevier {BV}, Apr. 2021, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• Determination of ‘Hass’ avocado ripeness during storage by a smartphone camera using artificial neural network and support vector regression
  Byeong-Hyo Cho; Kento Koyama; Shigenobu Koseki
  Journal of Food Measurement and Characterization, 15, 2, 2021, 2030, Springer Science and Business Media {LLC}, Apr. 2021, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• Predicting sensory evaluation of spinach freshness using machine learning model and digital images
  Kento Koyama; Marin Tanaka; Byeong-Hyo Cho; Yusaku Yoshikawa; Shige Koseki
  PLOS ONE, 16, 3, e0248769, e0248769, Public Library of Science ({PLoS}), 19 Mar. 2021, [Peer-reviewed], [Lead author, Corresponding author], [International Magazine]
  English, Scientific journal, The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels.
• Development of a Maillard Reaction–Based Time-Temperature Integrator/indicator (TTI) for Visual Monitoring of Chilled Beef During Long-term Storage and Distribution
  Kentaro Sakai; Jung Hyun Lee; Chawalit Kocharunchitt; Tom Ross; Ian Jenson; Kento Koyama; Shigenobu Koseki
  Food and Bioprocess Technology, 13, 12, 2094, 2103, Springer Science and Business Media {LLC}, Dec. 2020, [Peer-reviewed], ［Internationally co-authored］, [International Magazine]
  English, Scientific journal
• Modeling Invasion of Campylobacter jejuni into Human Small Intestinal Epithelial-Like Cells by Bayesian Inference
  Hiroki Abe; Kento Koyama; Shigenobu Koseki
  Applied and Environmental Microbiology, 87, 1, American Society for Microbiology, 16 Oct. 2020, [Peer-reviewed], [International Magazine]
  English, Scientific journal,
  Current approaches used for dose-response modeling of low-dose exposures of pathogens rely on assumptions and extrapolations. These models are important for quantitative microbial risk assessment of food. A mechanistic framework has been advocated as an alternative approach for evaluating dose-response relationships. The objectives of this study were to investigate the invasion behavior of Campylobacter jejuni, which could arise as a foodborne illness even if there are low counts of pathogens, into Caco-2 cells as a model of intestinal cells and to develop a mathematical model for invading cell counts to reveal a part of the infection dose-response mechanism. Monolayer-cultured Caco-2 cells and various concentrations of C. jejuni in culture were cocultured for up to 12 h. The numbers of C. jejuni bacteria invading Caco-2 cells were determined after coculture for different time periods. There appeared to be a maximum limit to the invading bacterial counts, which showed an asymptotic exponential increase. The invading bacterial counts were higher with higher exposure concentrations (maximum, 5.0 log CFU/cm²) than with lower exposure concentrations (minimum, 0.6 log CFU/cm²). In contrast, the ratio of invading bacteria (number of invading bacteria divided by the total number of bacteria exposed) showed a similar trend regardless of the exposure concentration. Invasion of C. jejuni into intestinal cells was successfully demonstrated and described by the developed differential equation model with Bayesian inference. The model accuracy showed that the 99% prediction band covered more than 97% of the observed values. These findings provide important information on mechanistic pathogen dose-response relationships and an alternative approach for dose-response modeling.
  
  
  IMPORTANCE One of the infection processes of C. jejuni, the invasion behavior of the bacteria in intestinal epithelial cells, was revealed, and a mathematical model for prediction of the cell-invading pathogen counts was developed for the purpose of providing part of a dose-response model for C. jejuni based on the infection mechanism. The developed predictive model showed a high accuracy of more than 97% and successfully described the C. jejuni invading counts. The bacterial invasion predictive model of this study will be essential for the development of a dose-response model for C. jejuni based on the infection mechanism.
• Transforming kinetic model into a stochastic inactivation model: Statistical evaluation of stochastic inactivation of individual cells in a bacterial population
  Satoko Hiura; Hiroki Abe; Kento Koyama; Shigenobu Koseki
  Food Microbiology, 91, Oct. 2020, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• Determination of “Hass” Avocado Ripeness During Storage Based on Smartphone Image and Machine Learning Model
  Byeong-Hyo Cho; Kento Koyama; Edenio Olivares D{\'{\i } }az; Shigenobu Koseki
  Food and Bioprocess Technology, 13, 9, 1579, 1587, Springer Science and Business Media {LLC}, Sep. 2020, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• Describing the Individual Spore Variability and the Parameter Uncertainty in Bacterial Survival Kinetics Model by Using Second-Order Monte Carlo Simulation
  Hiroki Abe; Kento Koyama; Kohei Takeoka; Shinya Doto; Shigenobu Koseki
  Frontiers in Microbiology, 11, 19 May 2020, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• 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
  Hiroki Abe; Kento Koyama; Shuso Kawamura; Shigenobu Koseki
  Food Microbiology, 82, 436, 444, Sep. 2019, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• Describing uncertainty in salmonella thermal inactivation using bayesian statistical modeling
  Kento Koyama; Zafiro Aspridou; Shige Koseki; Konstantinos Koutsoumanis
  Frontiers in Microbiology, 10, SEP, 01 Sep. 2019, [Peer-reviewed], [Lead author], ［Internationally co-authored］, [International Magazine]
  English, Scientific journal
• Calculating stochastic inactivation of individual cells in a bacterial population using variability in individual cell inactivation time and initial cell number
  Kento Koyama; Hiroki Abe; Shuso Kawamura; Shige Koseki
  Journal of Theoretical Biology, 469, 172, 179, 21 May 2019, [Peer-reviewed], [Lead author], [International Magazine]
  English, Scientific journal
• Stochastic simulation for death probability of bacterial population considering variability in individual cell inactivation time and initial number of cells
  Kento Koyama; Hiroki Abe; Shuso Kawamura; Shigenobu Koseki
  International Journal of Food Microbiology, 290, 125, 131, 02 Feb. 2019, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Stochastic evaluation of Salmonella enterica lethality during thermal inactivation
  Abe Hiroki; Koyama Kento; Kawamura Shuso; Koseki Shigenobu
  INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY, 285, 129, 135, 20 Nov. 2018, [Peer-reviewed], [International Magazine]
  English, Scientific journal
• Estimation of the probability of bacterial population survival: Development of a probability model to describe the variability in time to inactivation of Salmonella enterica
  Kento Koyama; Hidekazu Hokunan; Mayumi Hasegawa; Shuso Kawamura; Shigenobu Koseki
  FOOD MICROBIOLOGY, 68, 121, 128, Dec. 2017, [Peer-reviewed], [Lead author], [International Magazine]
  English, Scientific journal
• 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
  Kento Koyama; Hidekazu Hokunan; Mayumi Hasegawa; Shuso Kawamura; Shigenobu Koseki
  APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 83, 4, Feb. 2017, [Peer-reviewed], [Lead author], [International Magazine]
  English, Scientific journal
• Do bacterial cell numbers follow a theoretical Poisson distribution? Comparison of experimentally obtained numbers of single cells with random number generation via computer simulation
  Kento Koyama; Hidekazu Hokunan; Mayumi Hasegawa; Shuso Kawamura; Shigenobu Koseki
  FOOD MICROBIOLOGY, 60, 49, 53, Dec. 2016, [Peer-reviewed], [Lead author], [International Magazine]
  English, Scientific journal
• Survival Kinetics of Salmonella enterica and Enterohemorrhagic Escherichia coli on a Plastic Surface at Low Relative Humidity and on Low-Water Activity Foods
  Hidekazu Hokunan; Kento Koyama; Mayumi Hasegawa; Shuso Kawamura; Shigenobu Koseki
  JOURNAL OF FOOD PROTECTION, 79, 10, 1680, 1692, Oct. 2016, [Peer-reviewed], [International Magazine]
  English, Scientific journal

• ブロッコリー花蕾腐敗病の疾病マーカー分子網羅探索と匂い識別センシング
  深澤香奈; 風間勇汰; CHEN Le; 小山健斗; 岡紗雪; 蓬田陽平; 長島一樹, 化学系学協会北海道支部冬季研究発表会(Web), 2026, 2026
• 2000-2022年に検査された国内で流通している食品の食中毒菌汚染実態のデータベース化と統計解析
  細江隼平; 小関成樹; 小山健斗, 日本食品微生物学会学術総会講演要旨集, 46th, 2025
• 誘電泳動とマイクロ流路装置を用いたViable but non-culturable状態のCampylobacter jejuniの分離・定量
  中島大翔; 村上知広; 星野浩也; 小山健斗; 小関成樹, 日本食品微生物学会学術総会講演要旨集, 46th, 2025
• 非破壊測定による追熟果実の内部腐敗の定量評価とカビ抑制技術の探索—Impact of low humidity on suppressing stem-end rot in avocado during ripening
  LEMBONO Yoan Felanny; 小山 健斗, 年報 / 飯島藤十郎記念食品科学振興財団 編 = Annual report / The Tojuro Iijima Foundation for Food Science and Technology, 40, 442, 446, 2024
  市川 : 飯島藤十郎記念食品科学振興財団, Japanese
• カテゴリ内の順位を見る:多量の画像を並び替える官能評価手法の開発
  安部星; 小関成樹; 小山健斗, 日本官能評価学会誌, 28, 1, 2024
• Recent Advances in Predictive Models for Bacterial Inactivation
  小関成樹; 小山健斗; 安部大樹, Foods & Food Ingredients Journal of Japan, 229, 1, 2024
• Prediction of bacterial growth/inactivation in food using data mining
  細江隼平; 砂川純也; 中岡慎治; 小関成樹; 小山健斗, 日本数理生物学会年会(Web), 2024, 2024
• Mathematical modeling of the probability of adaptation of bacteria to stress
  加藤大志; 小関成樹; 小山健斗, 日本数理生物学会年会(Web), 2024, 2024
• 順序データによる新たな熟度の評価プロセス
  長岐幸尚; 小関成樹; 小山健斗, 農業食料工学会年次大会講演要旨(CD-ROM), 82nd, 2024
• ランキング手法で解き明かすミニトマトの傷に対する認識
  安部星; 松井貴大; 小関成樹; 小山健斗, 農業食料工学会年次大会講演要旨(CD-ROM), 82nd, 2024
• 細菌の死滅予測モデルにおける最近の進展—Recent Advances in Predictive Models for Bacterial Inactivation—特集 次世代型食品微生物制御論
  小関 成樹; 小山 健斗; 安部 大樹, Foods & food ingredients journal of Japan = 食品・食品添加物研究誌 : FFIジャーナル, 229, 1, 5, 12, 2024
  豊中 : FFIジャーナル編集委員会, Japanese
• Development of non-destructive inspection method for internal fruit rots of avocados via X-ray imaging and deep learning
  松井貴大; 杉森博行; 小関成樹; 小山健斗, 農業環境工学関連学会合同大会講演要旨集(CD-ROM), 2023, 2023
• 誘電泳動とマイクロ流路装置を用いた複合技術によるカンピロバクター損傷菌の定量・分離
  岩崎愛深; 村上知広; 小山健斗; 小関成樹, 日本食品微生物学会学術総会講演要旨集, 44th, 2023
• 交差汚染後の食品中における食中毒細菌の死滅挙動の解明および予測モデル開発
  矢辺秀茂; 小山健斗; 小関成樹, 日本食品微生物学会学術総会講演要旨集, 43rd, 2022
• 要冷蔵条件下における低温増殖性細菌芽胞の増殖確率予測モデルの開発
  鶴間菜月; 道東慎也; 石田亘; 小山健斗; 小関成樹, 日本食品微生物学会学術総会講演要旨集, 40th, 2019
• 確率分布を用いたサルモネラの死滅時間の予測モデル
  安部大樹; 小山健斗; 川村周三; 小関成樹, 農業食料工学会年次大会講演要旨, 76th, 181, 04 Sep. 2017
  Japanese
• 確率論に基づいた細菌集団死滅挙動の再解釈
  小山健斗; 安部大樹; 川村周三; 小関成樹, 農業食料工学会年次大会講演要旨, 76th, 2017
• 乾燥環境下におけるサルモネラの死滅時間のばらつきを予測する確率論的モデルの開発
  小山健斗; 北南秀和; 長谷川真弓; 川村周三; 小関成樹, 農業食料工学会年次大会講演要旨, 75th, 2016
• 乾燥環境下における個々の細菌の生残/死滅を予測する確率論的数理モデルの開発
  小山健斗; 北南秀和; 長谷川真弓; 川村周三; 小関成樹, 農業環境工学関連学会合同大会講演要旨集(CD-ROM), 2015, 2015
• 乾燥環境下における腸管出血性大腸菌およびサルモネラの死滅挙動予測モデルの開発
  北南秀和; 長谷川真弓; 小山健斗; 川村周三; 小関成樹, 農業環境工学関連学会合同大会講演要旨集(CD-ROM), 2015, 2015
• 低水分活性食品における食中毒細菌の死滅挙動の解明
  長谷川真弓; 北南秀和; 小山健斗; 川村周三; 小関成樹, 農業環境工学関連学会合同大会講演要旨集(CD-ROM), 2015, 2015

• 生物環境工学実験Ⅳ, 2024年, 学士課程, 農学部
• 機械設計製図, 2024年, 学士課程, 農学部
• 生物環境工学実習, 2024年, 学士課程, 農学部
• 生物環境工学基礎実験, 2024年, 学士課程, 農学部

• Editorial board member, Journal of Food Protection
• Editorial board member, Journal of Microbiological Methods

• 青果物の貯蔵特性はなぜばらつくのか？劣化程度の「未来」を左右する潜在的因子の解明
  科学研究費助成事業
  Apr. 2025 - Mar. 2029
  渡邊高志; 蔦瑞樹; 冨田理; 小山健斗
  日本学術振興会, 基盤研究(B), 国立研究開発法人農業・食品産業技術総合研究機構, 25K02134
• At-Harvest management for protecting stem-end to suppress fruit and vegetable decay
  研究助成
  Apr. 2026 - Mar. 2028
  小山健斗
  Matsushima Horticultural Development Foundation
• 言語化しにくい人間の暗黙知をひもとく新たな比較評価理論体系の構築
  奨励研究助成
  Apr. 2025 - Mar. 2028
  小山健斗
  ロッテ財団, Principal investigator
• 病原微生物のばく露評価に資する日本国内データベースの構築
  食品健康影響評価技術研究
  Apr. 2025 - Mar. 2027
  小山健斗
  内閣府 食品安全委員会, Principal investigator, JPCAFSC20252507
• Growth probability of bacterial populations predicted from single cells
  Apr. 2024 - Mar. 2027
  小山健斗
  Japan Society for the Promotion of Science, Grant-in-Aid for Early-Career Scientists, Principal investigator
• AIと短波長の自家蛍光による高精度な人参内部の木化判別機の開発
  Aug. 2024 - Mar. 2025
  小山 健斗; 松井 貴大; 川島 圭太; 井川 久; 宮島 沙織; 吉田 道拓
  ノーステック財団, イノベーション創出研究支援事業（研究成果展開補助金）, Principal investigator
• 非破壊測定による追熟果実の内部腐敗の定量評価とカビ抑制技術の探索
  Apr. 2024 - Mar. 2025
  小山 健斗
  公益財団法人 飯島藤十郎記念食品科学振興財団, 外国人留学生研究助成, Principal investigator
• Digitization of risk assessment: automation of information collection and analysis to save labor and improve accuracy
  Food Health Effects Assessment Technology Research
  2023 - Mar. 2025
  小山 健斗; 小関 成樹; 田﨑 創平
  Cabinet Office, JPCAFSC20232307
• 食品中の有害細菌の増える/減るを見える化:データ解析の高度化
  若手研究人材育成事業
  Aug. 2023 - Mar. 2024
  小山 健斗
  ノーステック財団
• AIと紫外光を組み合わせた高精度な人参内部の木質化判別手法の 開発
  イノベーション創出研究支援事業 (産学連携創出補助金)
  Aug. 2023 - Mar. 2024
  小山 健斗, 川島 圭太, 井川 久, 吉田 道拓
  ノーステック財団, Principal investigator
• Nondestructive evaluation of internal decay of avocado by X-ray and search for inhibition of fungal growth
  Grants-in-Aid for Scientific Research Grant-in-Aid for Early-Career Scientists
  Apr. 2021 - Mar. 2024
  小山 健斗
  近年，農産物の長時間・長距離輸送が増える傾向にある。それにともない，品質を保持したままでの保存日数の向上が求められている。世界的にも消費者は，加工食品，冷凍食品や冷凍野菜のみならず，食品素材そのものの食感，香りを保持した生鮮農産物を望んでいる。すなわち，農産物そのものの輸送が求められている。これまでに，輸送時の農産物の廃棄の削減を目的とし，生産・輸送・消費の過程で途切れることなく低温に保つコールドチェーンが確立されてきた。一方で，アボカド，マンゴーといった追熟が必要な農産物は，小売店に陳列される前に20°C前後で追熟処理される。追熟前より追熟後の農産物は腐敗変敗しやすいため，保存環境の制御が難しい。特に追熟や保存時のロスの原因は茎の付け根からカビが増える内部黒変である。しかし，カビが原因で発生する内部黒変の検出や制御が難しく，追熟後から消費にいたる過程に大量の廃棄が生み出される。これらの問題を解決するために，輸送時の農産物の低温流通のみならず，農産物の追熟や保存技術が重要となる。
  アボカド，マンゴーといった追熟が必要な農産物の内部の黒変を目視で早期に検出することは難しい。特に，茎の付け根から侵入したカビが原因となる内部黒変は追熟前に症状として現れないため検出が困難である。内部黒変の有無を評価するには果実の切断が必要となる。本研究では，X線画像を用いた非破壊でのアボカドの内部黒変の定量化とカビの増殖を抑制する保存条件を探索をおこなう。X線異物検査器は対象物の密度を測定できるため，カビが活動してできる空洞と内部黒変を検出できると考えられる。本研究の目的を以下の3課題に設定する。
  ◯X線画像を用いたアボカドの内部の黒変の自動判定
  ◯追熟過程における内部黒変レベルの定量化
  ◯文献データと機械学習によるアボカドの最適保存方法の探索と実証実験
  Japan Society for the Promotion of Science, Grant-in-Aid for Early-Career Scientists, Hokkaido University, Principal investigator, 21K14943
• 食品における有害細菌の制御の概念を変える：ビックデータから発見する新たな知識体系
  研究助成金
  Oct. 2022 - Sep. 2023
  小山 健斗
  公益財団法人 戸部眞紀財団, Principal investigator
• 官能評価を駆使した革新的な人間の認識の見える化アルゴリズムの開発
  研究助成＜奨励＞
  Jun. 2022 - Mar. 2023
  小山健斗
  公益財団法人 秋山記念生命科学振興財団, Principal investigator
• Developing automatic detection model for fungal contamination of fruit using X-ray imaging and investigating fungal control
  第56回リバネス研究費 Global Challenge Award
  Apr. 2022 - Mar. 2023
  小山健斗
  Leave a Nest, Principal investigator
• 未知の細菌のストレス耐性をラマン分光と過去研究事例から瞬時に予測する手法の開発
  研究助成
  Apr. 2022 - Mar. 2023
  小山健斗
  一般財団法人 旗影会, Principal investigator
• Bayesian estimation for bacterial behavior from farm to fork
  Grants-in-Aid for Scientific Research Grant-in-Aid for Research Activity Start-up
  Aug. 2019 - Mar. 2021
  KOYAMA KENTO
  Bayesian statistic for predicting bacterial population behavior was introduced instead of classical frequentist statistical method. Probabilistic approach based on Bayesian statistic enabled to deal individual cell growth or inactivation using probability distribution. Probabilistic approach would be useful for estimating food borne illness using probability distribution. Approach in this study tries to use probabilistic approach instead of kinetic model, which has been used so far.
  Especially, this study focused on growth of Listeria monocytogenes and Salmonella enterica and inactivation of Bacillus. Pure birth process and pure death process were applied for describing individual cell growth or inactivation. Experiment was performed for validation.
  Japan Society for the Promotion of Science, Grant-in-Aid for Research Activity Start-up, Hokkaido University, Principal investigator, 19K23655
• More Reliable Taste and Safety! Development of a new probabilistic evaluation method for sterilization efficacy and food quality
  Grants-in-Aid for Scientific Research Grant-in-Aid for JSPS Fellows
  Apr. 2017 - Mar. 2019
  小山 健斗
  本研究では食品の品質と殺菌効果とを両立させる安全性理論の構築を目的とする。少数の細菌で食中毒を引き起こす腸管出血性大腸菌とサルモネラを用いる。対象とする細菌集団が完全に死滅する時間を確率分布で表記し，細菌集団の死滅条件を確率に裏付けられた方法で設定する。例えば，「〇〇°C△△分以上加熱殺菌した場合，サルモネラ10万個は99.99%の確率で完全に死滅する。」といった殺菌条件を提示する。確率論に裏付けされた殺菌条件に加え，食品の品質と安全性のバランスを考える。確率論の知見に基づき品質劣化を最小限に抑えつつ，安全面も許容レベルにできるといった殺菌効果と品質保持の両面における最適化を可能とする殺菌条件を提示する。新たな安全性評価理論の構築により，実際の殺菌工程における殺菌条件の設定において，“どの程度の確率で対象とする細菌集団が完全に死滅したか？”といった製造業者やリスク評価者の関心事への回答を可能とする。今年度は，サルモネラの死滅・及び増殖をベイズ統計モデルによって解析した。滞在先のギリシャでのディスカッションの結果，不確実性を考慮したモデルづくりに着手することになった。国際共同論文の作成にいたり現在論文を2報投稿中である。今後も共同研究を引き続き行っていき，食品の品質と殺菌効果を両立させる安全性理論について研究を進めていく。
  Japan Society for the Promotion of Science, Grant-in-Aid for JSPS Fellows, Hokkaido University, Principal investigator, 17J04163
• Stochastic bacterial inactivation modeling using time-lapse microscopy for time to inactivation
  JSPS Overseas Challenge Program for Young Researchers
  Apr. 2017 - Mar. 2018
  小山健斗
  JSPS, 北海道大学大学院, Principal investigator

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  Patent right, 小山 健斗; 安部 星; 長岐 幸尚
  特開2025-29334, 06 Mar. 2025