Researcher Profile and Settings

Affiliation

• Research Faculty of Agriculture　Fundamental AgriScience Research　Bioresource and Environmental Engineering

Job Title

• Associate Professor

Degree

• Ph.D (Agriculture)（2019/03 Hokkaido University）

Contact information

• kkoyamaagr.hokudai.ac.jp

URL

https://www.afpe-hu.jp/
https://www.afpe-hu.jp/en.html

ORCID ID

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

J-Global ID

• 201901016277670444

Profile

• The research focuses on studying ways to maintain the deliciousness and safety of food from harvest to consumption. The specialty involves evaluating food using data, sensors, and mathematics.

  
  

  As a researcher, the aim is to develop "a probabilistic method for evaluating the quality and safety of food" and to implement it in various social issues, from the quality assessment of agricultural products using machine learning and image analysis to the quantitative evaluation of food microorganisms. The academic paper with peer review will be published in international academic journals.

  
  

  The research summary is available to view from the following link
  https://scrapbox.io/kentokoyama-Eng/
  https://www.afpe-hu.jp/en.html
  

Research Interests

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

Research Areas

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

Educational Organization

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

Academic & Professional Experience

• 2023/07 - Today Research Faculty of Agriculture, Hokkaido University Graduate School of Agriculture Research Faculty of Agriculture Associate professor
• 2019/04 - 2023/06 Hokkaido University Research Faculty of Agriculture Assistant professor
• 2017/04 - 2019/03 JSPS Research Fellowships for Young Scientist (DC1)

Education

• 2017/04 - 2019/03  Hokkaido University  Graduate School of Agriculture  Doctoral course
• 2017/09 - 2019/02  Aristotle University of Thessaloniki  Visiting student

Association Memberships

• Editorial board member, Journal of microbiological methods   

Research Activities

Published Papers

• 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 0950-3293 2024/03 [Refereed]
  
• 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 2193-4126 2023/12/20
• 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 0308-8146 2023/12 [Refereed]
  
• 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 0925-5214 2023/09 

  Internal rot of avocado fruit (Persea americana), attributable to fungal infection, occurs at the end of the ripening process and causes only minor changes in the appearance and texture of the fruit surface. Manual inspection of rot by sight and touch commonly conducted in countries importing avocado fruit is time-consuming, labor-intensive, and subjective. In this context, X-ray line scanning has been proven as an advantageous method of fruit rot detection because of its speed of data acquisition and the indication of internal rot by bright regions in associated images. However, some fruit internal disorders exhibit only poor changes in contrast, resulting in low detectability by traditional image processing. This study aimed to test the effectiveness of a detection model using deep learning-based semantic segmentation in identifying two types of fruit rot, stem-end and body rot, in Hass avocados. Therefore, U-net+ + was trained and validated via 5-fold cross-validation to classify every pixel in an X-ray image as either infected or not. Then, each X-ray image was binarily classified based on either the presence or absence of internal fruit rots, achieving an accuracy of 0.98. Furthermore, the percentage of infected area was quantified with a root mean squared error (RMSE) of 3.15 %. Lastly, the proposed model detected both stem-end and body rot as well as rot along low-contrast fruit edges. The results of this study indicate that the proposed automatic inspection system using deep learning-based X-ray image analysis can effectively detect internal rot in Hass avocado fruit. This non-destructive, objective detection model can therefore increase efficiency and reduce misclassification in post-harvest avocado inspection. Furthermore, deep learning-based X-ray imaging has potential for applications in fruit inspection for internal cavities attributable to diseases or wounds.
• 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 0362-028X 2023/08 [Refereed]
  
• 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 0023-6438 2023/01 [Refereed]
  
• 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 2022/12/15 [Refereed]
   

  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 2214-2894 2022/12 [Refereed]
  
• 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 0925-5214 2022/10 [Refereed]
  
• 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 0266-8254 2022/08 [Refereed]
   

  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 1935-5130 2022/06 [Refereed]
  
• 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 0740-0020 2022/04 [Refereed]
  
• 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 0168-1699 2022/02 [Refereed]
  
• 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 2193-4126 2022/02 [Refereed]
  
• 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 1935-5149 2022
• 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 0167-7012 2022/01
• 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) 2165-0497 2021/12/22 [Refereed]
   

  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) 2165-0497 2021/12/22 [Refereed]
   

  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) 2045-2322 2021/12 [Refereed]
   

  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 0956-7135 2021/12 [Refereed]
  
• 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 0167-7012 2021/11 [Refereed]
  
• 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  0099-2240 2021/09/28 [Refereed]
   

  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 1364-5072 2021/09/23 [Refereed]
  
• 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 0022-5193 2021/09 [Refereed]
   

  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  0099-2240 2021/07/13 [Refereed]
   

  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 0167-7012 2021/07 [Refereed]
   

  The concept of dielectrophoresis (DEP), which involves the movement of neutral particles by induced polarization in nonuniform electric fields, has been exploited in various biological applications. However, only a few studies have investigated the use of DEP for detecting and enumerating microorganisms in foodstuffs. Therefore, we aimed to evaluate the accuracy and efficiency of a DEP-based method for enumerating viable bacteria in three raw foods: freshly cut lettuce, chicken breast, and minced pork. The DEP separation of bacterial cells was conducted at 20 V of output voltage and 6000 to 9000 kHZ of frequency with sample conductivity of 30-70 mu S/cm. The accuracy and validity of the DEP method for enumerating viable bacteria were compared with those of the conventional culture method; no significant variation was observed. We found a high correlation between the data obtained using DEP and the conventional aerobic plate count culture method, with a high coefficient of determination (R-2 > 0.90) regardless of the food product; the difference in cell count data between both methods was within 1.0 log CFU/mL. Moreover, we evaluated the efficiency of the DEP method for enumerating bacterial cells in chicken breasts subjected to either freezing or heat treatment. After thermal treatment at 55 degrees C and 60 degrees C, the viable cell counts determined via the DEP method were found to be lower than those obtained using the conventional culture method, which implies that the DEP method may not be suitable for the direct detection of injured cells. In addition to its high accuracy and efficiency, the DEP method enables the determination of viable cell counts within 30 min, compared to 48 h required for the conventional culture method. In conclusion, the DEP method may be a potential alternative tool for rapid determination of viable bacteria in a variety of foodstuffs.
• 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 2021/06/24 [Refereed]
   

  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 2214-7993 2021/06 [Refereed]
  
• 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 0956-7135 2021/04 [Refereed]
  
• 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 2193-4126 2021/04 [Refereed]
  
• 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 1932-6203 2021/03/19 [Refereed]
   

  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 1935-5130 2020/12 [Refereed]
  
• 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) 0099-2240 2020/10/16 [Refereed]
   

  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 0740-0020 2020/10 [Refereed][Not invited]
   

  © 2020 Elsevier Ltd Kinetic models performing point estimation are effective in predicting the bacterial behavior. However, the large variation of bacterial behavior appearing in a small number of cells, i.e. equal or less than 102 cells, cannot be expressed by point estimation. We aimed to predict the variation of Escherichia coli O157:H7 behavior during inactivation in acidified tryptone soy broth (pH3.0) through Monte Carlo simulation and evaluated the accuracy of the developed model. Weibullian fitted parameters were estimated from the kinetic survival data of E. coli O157:H7 with an initial cell number of 105. A Monte Carlo simulation (100 replication) based on the obtained Weibullian parameters and the Poisson distribution of initial cell numbers successfully predicted the results of 50 replications of bacterial inactivation with initial cell numbers of 101, 102, and 103 cells. The accuracy of the simulation revealed that more than 83% of the observed survivors were within predicted range in all condition. 90% of the distribution in survivors with initial cells less than 100 is equivalent to a Poisson distribution. This calculation transforms the traditional microbial kinetic model into probabilistic model, which can handle bacteria number as discrete probability distribution. The probabilistic approach would utilize traditional kinetic model towards exposure assessment.
• 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 1935-5130 2020/09 [Refereed]
  
• 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 2020/05/19 [Refereed][Not invited]
   

  © Copyright © 2020 Abe, Koyama, Takeoka, Doto and Koseki. The objective of this study was to separately describe the fitting uncertainty and the variability of individual cell in bacterial survival kinetics during isothermal and non-isothermal thermal processing. The model describing bacterial survival behavior and its uncertainties and variabilities during non-isothermal inactivation was developed from survival kinetic data for Bacillus simplex spores under fifteen isothermal conditions. The fitting uncertainties in the parameters used in the primary Weibull model was described by using the bootstrap method. The variability of individual cells in thermotolerance and the true randomness in the number of dead cells were described by using the Markov chain Monte Carlo (MCMC) method. A second-order Monte Carlo (2DMC) model was developed by combining both the uncertainties and variabilities. The 2DMC model was compared with reduction behavior under three non-isothermal profiles for model validation. The bacterial death estimations were validated using experimentally observed surviving bacterial count data. The fitting uncertainties in the primary Weibull model parameters, the individual thermotolerance heterogeneity, and the true randomness of inactivated spore counts were successfully described under all the iso-thermal conditions. Furthermore, the 2DMC model successfully described the variances in the surviving bacterial counts during thermal inactivation for all three non-isothermal profiles. As a template for risk-based process designs, the proposed 2DMC simulation approach, which considers both uncertainty and variability, can facilitate the selection of appropriate thermal processing conditions ensuring both food safety and quality.
• 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 0740-0020 2019/09 [Refereed][Not invited]
   

  © 2019 Elsevier Ltd The control of bacterial reduction is important to maintain food safety during thermal processing. The goal of this study was to illustrate and describe variability in bacterial population behavior during thermal processing as a probability distribution based on individual cell heterogeneity regarding heat resistance. Toward this end, we performed a Monte Carlo simulation via computer, and compared and validated the simulated estimations with observed values. Weibullian fitted parameters were estimated from the kinetic survival data of Bacillus simplex during thermal treatment at 94 °C. The variability in reductions of bacterial sporular populations was illustrated using Monte Carlo simulation based on the Weibull distribution of the parameters. In particular, variabilities in viable spore counts and survival probability of the B. simplex spore population were simulated in various replicates. We also experimentally determined the changes in survival probability and distributions of survival spore counts; notably, these were successfully predicted by the Monte Carlo simulation based on the kinetic parameters. The kinetic parameter-based Monte Carlo simulation could thus successfully illustrate bacterial population behavior variability during thermal processing as a probability distribution. The simulation approach may contribute to improving food quality through risk-based processing designs and enhance risk assessment model accuracy.
• Describing uncertainty in salmonella thermal inactivation using bayesian statistical modeling

  Kento Koyama, Zafiro Aspridou, Shige Koseki, Konstantinos Koutsoumanis

  Frontiers in Microbiology 10 (SEP) 1664-302X 2019/09/01 [Refereed][Not invited]
   

  © 2019 Koyama, Aspridou, Koseki and Koutsoumanis. Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. In the context of microbial risk assessment, the uncertainty in the predicted microbial behavior can be an important component of the overall uncertainty. Conventional deterministic modeling approaches which provide point estimates of the pathogen’s levels cannot quantify the uncertainty around the predictions. The objective of this study was to use Bayesian statistical modeling for describing uncertainty in predicted microbial thermal inactivation of Salmonella enterica Typhimurium DT104. A set of thermal inactivation data in broth with water activity adjusted to 0.75 at 9 different temperature conditions obtained from the ComBase database (www.combase.cc) was used. A log-linear microbial inactivation was used as a primary model while for secondary modeling, a linear relation between the logarithm of inactivation rate and temperature was assumed. For comparison, data were fitted with a two-step and a global Bayesian regression. Posterior distributions of model’s parameters were used to predict Salmonella thermal inactivation. The combination of the joint posterior distributions of model’s parameters allowed the prediction of cell density over time, total reduction time and inactivation rate as probability distributions at different time and temperature conditions. For example, for the time required to eliminate a Salmonella population of about 107 CFU/ml at 65◦ C, the model predicted a time distribution with a median of 0.40 min and 5th and 95th percentiles of 0.24 and 0.60 min, respectively. The validation of the model showed that it can describe successfully uncertainty in predicted thermal inactivation with most observed data being within the 95% prediction intervals of the model. The global regression approach resulted in less uncertain predictions compared to the two-step regression. The developed model could be used to quantify uncertainty in thermal inactivation in risk-based processing design as well as in risk assessment studies.
• 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 0022-5193 2019/05/21 [Refereed][Not invited]
   

  © 2019 Elsevier Ltd The traditional log-linear inactivation kinetics model considers microbial inactivation as a process that follows first-order kinetics. A basic concept of log reduction is decimal reduction time (D-value), which means time/dose required to kill 90% of the relevant microorganisms. D-value based on the first-order survival kinetics model is insufficient for reliable estimations of bacterial survivors following inactivation treatment. This is because the model does not consider the inactivation curvature and variability in bacterial inactivation. However, although the D-value has some limitations, it is widely used for risk assessment and sterilization time estimation. In this study, stochastic inactivation models are used in place of the conventional D-value to describe the probability of a population containing survivors. As representative bacterial inactivation normally follows a log-linear or log-Weibull model, we calculate the time required for a specific decrease in the number of cells and the number of survival cells as a probability distribution using the stochastic inactivation of individual cells in a population. We compare the probability of a population containing survivors calculated via the D-value, an inactivation kinetics model, and the stochastic formula. The stochastic calculation can be approximately estimated via a kinetic curvature model with less than 5% difference below the probability of a population containing survivors 0.1. This stochastic formula indicates that the D-value model would over- or under-estimate the probability of a population containing survivors when applied to inactivation kinetics with curvature. The results presented in this study show that stochastic analysis using mathematical models that account for variability in the individual cell inactivation time and initial cell number would lead to a realistic and probabilistic estimation of bacterial inactivation.
• 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 0168-1605 2019/02/02 [Refereed][Not invited]
   

  © 2018 Elsevier B.V. Decimal reduction time (D-value) based on the first-order survival kinetics model is not sufficient for reliable estimation of the bacterial survivors of inactivation treatment because the model does not consider inactivation curvature. However, even though doubt exists in the calculation of D-value, it is still widely used for risk assessment and sterilisation time estimation. This paper proposes an approach for estimating the time-to-inactivation and death probability of bacterial population that considers individual cell heterogeneity and initial number of cells via computer simulation. In the proposed approach, Weibull and Poisson distributions are respectively used to provide individual cell inactivation time variability and initial number of cells variability. Our simulation results show that the time-to-inactivation significantly depends on kinetics curvature and initial number of cells. For example, with increases in the initial number of cells, the respective variance of the time-to-inactivation of log-linear, concave downward curve, and concave upward curve remains constant, decreases, and increases, respectively. The death probability contour plot was successfully generated via our computer simulation approach without using D-value estimation. Further, the death probability calculated using our stochastic approach was virtually the same as that obtained using inactivation kinetics. We validated the simulation by using literature data for acid inactivation of Salmonella population. The results of this study indicate that inactivation curvature can replace D-value extrapolation to estimate the death probability of bacterial population. Further, our computer simulation facilitates realistic estimation of the time-to-inactivation of bacterial population. The R code used for the above stochastic calculation is outlined.
• 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 0168-1605 2018/11/20 [Refereed][Not invited]
   

  Stochastic models take into account the uncertainty and variability of predictions in quantitative microbial risk assessment. However, a model that considers thermal inactivation conditions can better predict whether or not bacteria in food are alive. To this end, we describe a novel probabilistic modelling procedure for accurately predicting thermal end point, in contrast to conventional kinetic models that are based on extrapolation of the D value. We used this new model to investigate changes in the survival probability of Salmonella enterica serotype Oranienburg during thermal processing. These changes were accurately described by a cumulative gamma distribution. The predicted total bacterial reduction time with a survival probability of 10-6-the commercial standard for sterility-was significantly shorter than that predicted by the conventional deterministic kinetic model. Thus, the survival probability distribution can explain the heterogeneity in total reduction time for a bacterial population. Furthermore, whereas kinetic methodologies may overestimate the time required for inactivation, our method for determining survival probability distribution can provide an accurate estimate of thermal inactivation and is therefore an important tool for quantitative microbial risk assessment of foods.
• 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 0740-0020 2017/12 [Refereed][Not invited]
   

  Despite the development of numerous predictive microbial inactivation models, a model focusing on the variability in time to inactivation for a bacterial population has not been developed. Additionally, an appropriate estimation of the risk of there being any remaining bacterial survivors in foods after the application of an inactivation treatment has not yet been established. Here, Gamma distribution, as a representative probability distribution, was used to estimate the variability in time to inactivation for a bacterial population. Salmonella enterica serotype Typhimurium was evaluated for survival in a low relative humidity environment. We prepared bacterial cells with an initial concentration that was adjusted to 2 x 10n colony-forming units/2 mu l (n = 1, 2, 3, 4, 5) by performing a serial 10-fold dilution, and then we placed 2 ml of the inocula into each well of 96-well microplates. The microplates were stored in a desiccated environment at 10-20% relative humidity at 5, 15, or 25 degrees C. The survival or death of bacterial cells for each well in the 96-well microplate was confirmed by adding tryptic soy broth as an enrichment culture. The changes in the death probability of the 96 replicated bacterial populations were described as a cumulative Gamma distribution. The variability in time to inactivation was described by transforming the cumulative Gamma distribution into a Gamma distribution. We further examined the bacterial inactivation on almond kernels and radish sprout seeds. Additionally, we described certainty levels of bacterial inactivation that ensure the death probability of a bacterial population at six decimal reduction levels, ranging from 90 to 99.9999%. Consequently, the probability model developed in the present study enables us to estimate the death probability of bacterial populations in a desiccated environment over time. This probability model may be useful for risk assessment to estimate the amount of remaining bacteria in a given sample. (C) 2017 Elsevier Ltd. All rights reserved.
• 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) 0099-2240 2017/02 [Refereed][Not invited]
   

  Despite effective inactivation procedures, small numbers of bacterial cells may still remain in food samples. The risk that bacteria will survive these procedures has not been estimated precisely because deterministic models cannot be used to describe the uncertain behavior of bacterial populations. We used the Poisson distribution as a representative probability distribution to estimate the variability in bacterial numbers during the inactivation process. Strains of four serotypes of Salmonella enterica, three serotypes of enterohemorrhagic Escherichia coli, and one serotype of Listeria monocytogenes were evaluated for survival. We prepared bacterial cell numbers following a Poisson distribution (indicated by the parameter lambda, which was equal to 2) and plated the cells in 96-well microplates, which were stored in a desiccated environment at 10% to 20% relative humidity and at 5, 15, and 25 degrees C. The survival or death of the bacterial cells in each well was confirmed by adding tryptic soy broth as an enrichment culture. Changes in the Poisson distribution parameter during the inactivation process, which represent the variability in the numbers of surviving bacteria, were described by nonlinear regression with an exponential function based on a Weibull distribution. We also examined random changes in the number of surviving bacteria using a random number generator and computer simulations to determine whether the number of surviving bacteria followed a Poisson distribution during the bacterial death process by use of the Poisson process. For small initial cell numbers, more than 80% of the simulated distributions (lambda =2 or 10) followed a Poisson distribution. The results demonstrate that variability in the number of surviving bacteria can be described as a Poisson distribution by use of the model developed by use of the Poisson process. IMPORTANCE We developed a model to enable the quantitative assessment of bacterial survivors of inactivation procedures because the presence of even one bacterium can cause foodborne disease. The results demonstrate that the variability in the numbers of surviving bacteria was described as a Poisson distribution by use of the model developed by use of the Poisson process. Description of the number of surviving bacteria as a probability distribution rather than as the point estimates used in a deterministic approach can provide a more realistic estimation of risk. The probability model should be useful for estimating the quantitative risk of bacterial survival during inactivation.
• 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 0740-0020 2016/12 [Refereed][Not invited]
   

  We investigated a bacterial sample preparation procedure for single-cell studies. In the present study, we examined whether single bacterial cells obtained via 10-fold dilution followed a theoretical Poisson distribution. Four serotypes of Salmonella enterica, three serotypes of enterohaemorrhagic Escherichia coli and one serotype of Listeria monocytogenes were used as sample bacteria. An inoculum of each serotype was prepared via a 10-fold dilution series to obtain bacterial cell counts with mean values of one or two. To determine whether the experimentally obtained bacterial cell counts follow a theoretical Poisson distribution, a likelihood ratio test between the experimentally obtained cell counts and Poisson distribution which parameter estimated by maximum likelihood estimation (MLE) was conducted. The bacterial cell counts of each serotype sufficiently followed a Poisson distribution. Furthermore, to examine the validity of the parameters of Poisson distribution from experimentally obtained bacterial cell counts, we compared these with the parameters of a Poisson distribution that were estimated using random number generation via computer simulation. The Poisson distribution parameters experimentally obtained from bacterial cell counts were within the range of the parameters estimated using a computer simulation. These results demonstrate that the bacterial cell counts of each serotype obtained via 10-fold dilution followed a Poisson distribution. The fact that the frequency of bacterial cell counts follows a Poisson distribution at low number would be applied to some single-cell studies with a few bacterial cells. In particular, the procedure presented in this study enables us to develop an inactivation model at the single-cell level that can estimate the variability of survival bacterial numbers during the bacterial death process. (C) 2016 Elsevier Ltd. All rights reserved.
• 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 0362-028X 2016/10 [Refereed][Not invited]
   

  We investigated the survival kinetics of Salmonella enterica and enterohemorrhagic Escherichia coli under various water activity (a(w)) conditions to elucidate the net effect of a(w) on pathogen survival kinetics and to pursue the development of a predictive model of pathogen survival as a function of a(w). Four serotypes of S. enterica (Stanley, Typhimurium, Chester, and Oranienburg) and three serotypes of enterohemorrhagic E. coli (E. coli 026, E. coli 0111, and E. coli 0157:H7) were examined. These bacterial strains were inoculated on a plastic plate surface at a constant relative humidity (RH) (22, 43, 58, 68; or 93% RH, corresponding to the a) or on a surface of almond kernels (a(w) 0.58), chocolate (a(w) 0.43), radish sprout seeds. (a(w) 0.58), or Cheddar cheese (a(w) 0.93) at 5, 15, or 25 C for up to 11 months. Under most conditions, the survival kinetics were nonlinear with tailing regardless of the storage a(w), temperature, and bacterial strain. For all bacterial serotypes, there were no apparent differences in pathogen survival kinetics on the plastic surface at a given storage temperature among the tested RH conditions, except for the 93% RH condition. Most bacterial serotypes were rapidly inactivated on Cheddar cheese when stored at 5 degrees C compared with their inactivation on chocolate, almonds, and radish sprout seeds. Distinct trends in bacterial survival kinetics were also observed between almond kernels and radish sprout seeds, even though the a(w)s of these two foods were not significantly different. The survival kinetics of bacteria inoculated on the plastic plate surface showed little correspondence to those of bacteria inoculated on food matrices at an identical a(w). Thus, these results demonstrated that, for low-a(w) foods and/or environments, a alone is insufficient to account for the survival kinetics of S. enterica and enterohemorrhagic E. coli.

MISC

• 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
• 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  2017/09/04  [Not refereed][Not invited]
  

Awards & Honors

• 2024/03 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 

  受賞者: Hiroya Hoshino, Tomohiro Murakami, Kento Koyama, Shige Koseki
• 2023/10 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 

  受賞者: 松井 貴大;杉森 博行;小関成樹;小山健斗
• 2023/10 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 

  受賞者: 細江 隼平;砂川 純也;中岡 慎治;小関成樹;小山 健斗
• 2023/06 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? 

  受賞者: Hidemoto Yabe;Hiroki Abe;Kento Koyama;Shigenobu Koseki
• 2022/04 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 

  受賞者: Kento Koyama
• 2019/09 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 

  受賞者: Satoko Hiura, Hiroki Abe, Kento Koyama, Shige Koseki
• 2019/07 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 

  受賞者: 酒井健太郎;岡庭理央;小山健斗;小関成樹
• 2018/07 International Association for Food Protection Student Travel Scholarship Award
   

  受賞者: Kento Koyama
• 2017/09 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 

  受賞者: Hiroki Abe;Kento Koyama;Shuso Kawamura;Shige Koseki

Research Grants & Projects

• Growth probability of bacterial populations predicted from single cells

  Japan Society for the Promotion of Science：

  Date (from‐to) : 2024/04 -2027/03
• 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

  公益財団法人 飯島藤十郎記念食品科学振興財団：

  Date (from‐to) : 2024/04 -2025/03
• 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

  ノーステック財団：イノベーション創出研究支援事業 (産学連携創出補助金)

  Date (from‐to) : 2023/08 -2025/03
• Digitization of risk assessment: automation of information collection and analysis to save labor and improve accuracy

  Cabinet Office：Food Health Effects Assessment Technology Research

  Date (from‐to) : 2023 -2025/03
• 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

  ノーステック財団：若手研究人材育成事業

  Date (from‐to) : 2023/08 -2024/03
• Nondestructive evaluation of internal decay of avocado by X-ray and search for inhibition of fungal growth

  Japan Society for the Promotion of Science：Grants-in-Aid for Scientific Research Grant-in-Aid for Early-Career Scientists

  Date (from‐to) : 2021/04 -2024/03 

  Author : 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
• 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

  公益財団法人 戸部眞紀財団：研究助成金

  Date (from‐to) : 2022/10 -2023/09
• 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

  公益財団法人 秋山記念生命科学振興財団：研究助成＜奨励＞

  Date (from‐to) : 2022/06 -2023/03
• Developing automatic detection model for fungal contamination of fruit using X-ray imaging and investigating fungal control

  Leave a Nest：第56回リバネス研究費 Global Challenge Award

  Date (from‐to) : 2022/04 -2023/03
• 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

  一般財団法人 旗影会：研究助成

  Date (from‐to) : 2022/04 -2023/03
• Bayesian estimation for bacterial behavior from farm to fork

  Japan Society for the Promotion of Science：Grants-in-Aid for Scientific Research Grant-in-Aid for Research Activity Start-up

  Date (from‐to) : 2019/08 -2021/03 

  Author : 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.
• More Reliable Taste and Safety! Development of a new probabilistic evaluation method for sterilization efficacy and food quality

  Japan Society for the Promotion of Science：Grants-in-Aid for Scientific Research Grant-in-Aid for JSPS Fellows

  Date (from‐to) : 2017/04 -2019/03 

  Author : 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。
• Stochastic bacterial inactivation modeling using time-lapse microscopy for time to inactivation

  JSPS：JSPS Overseas Challenge Program for Young Researchers

  Date (from‐to) : 2017/04 -2018/03

Educational Activities

Teaching Experience

Committee Membership

• 2019/04 - Today   Hokkaido Agricultural Facilities Council