研究者データベース
Last Updated :2024/10/24
研究者情報
マスター
アカウント（マスター）
氏名
小山 健斗(コヤマ ケント), コヤマ ケント
所属（マスター）
所属（マスター）
独自項目
syllabus
- 2021, 生物環境工学実習, Field Training on Bioresource and Environmental Engineering, 学士課程, 農学部, 作物，生産，土，水，大気，環境，農作業，機械，食品，廃棄物，循環
- 2021, 生物環境工学実験Ⅳ, Laboratory Work on Bioresource and Environmental Engineering Ⅳ, 学士課程, 農学部, 食料，米，青果物，発酵，好気性，嫌気性，リファイナリー，再生可能エネルギ
- 2021, 生物環境工学基礎実験, Laboratory Work on the Instrumentation of Bioresource and Environmental Engineering, 学士課程, 農学部, 計測，距離と位置，土壌，水質，流量，力と加速度，温度，湿度，空気質，放射，農産物，画像計測
researchmap
プロフィール情報
学位
プロフィール情報
プロフィール
研究対象は，収穫後から食べるまで，食べ物の美味しさと安全性を保つ研究を行う。
専門は，データ・センサー・数学を使った食べ物の評価研究者として「確率論を用いた食べ物の品質・安全性の評価手法」を開発し，機械学習と画像解析による農産物の品質評価から食品微生物の定量評価まで幅広い社会課題に実装，国際学術誌に査読つき学術論文を公開する
研究概要は下のリンクよりから閲覧可能
https://scrapbox.io/kentokoyama/
https://www.afpe-hu.jp/公開用メールアドレス
kkoyama
agr.hokudai.ac.jp姓
小山, コヤマ名
健斗, ケントID各種
201901016277670444
対象リソース
業績リスト
研究キーワード
研究分野
経歴
- 2023年07月 - 現在 北海道大学 大学院農学研究院 准教授
- 2019年04月 - 2023年06月 北海道大学 大学院農学研究院 助教
- 2017年04月 - 2019年03月 独立行政法人日本学術振興会 特別研究員（DC1）
学歴
委員歴
受賞
- 2024年03月 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: 星野浩也;村上知広;小山健斗;小関成樹 - 2023年10月 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年10月 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年06月 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年04月 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年09月 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年07月 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年07月 International Association for Food Protection Student Travel Scholarship Award
受賞者: 小山健斗 - 2017年09月 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
論文
- Hiroki Abe, Akihiro Ando, Kento Koyama, Shigenobu KosekiMicrobial Risk Analysis 27-28 100316 - 100316 2024年12月 [査読有り]
- Kazuho Aonishi, Shungo Miyao, Lisa Yokoi, Naoki Kitaoka, Kento Koyama, Hideyuki Matsuura, Shigenobu KosekiJournal of agricultural and food chemistry 2024年07月04日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.
- Hidemoto Yabe, Hiroki Abe, Yoshiki Muramatsu, Kento Koyama, Shige KosekiApplied and Environmental Microbiology 90 6 2024年06月18日 [査読有り]
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. - Sei Abe, Takahiro Matsui, Shige Koseki, Kento KoyamaFood Quality and Preference 105167 - 105167 2024年03月 [査読有り]
- Raki Takemoto, Takashi Watanabe, Nobutaka Nakamura, Shige Koseki, Kento KoyamaJournal of Food Measurement and Characterization 18 3 1776 - 1785 2023年12月20日
- Edenio Olivares Díaz, Haruka Iino, Kento Koyama, Shuso Kawamura, Shigenobu Koseki, Suxing LyuFood Chemistry 429 136907 - 136907 2023年12月 [査読有り]
- Takahiro Matsui, Hiroyuki Sugimori, Shige Koseki, Kento KoyamaPostharvest Biology and Technology 203 2023年09月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.
- Eisuke Maesaka, Satoshi Kukuminato, Kazuho Aonishi, Kento Koyama, Shigenobu KosekiJournal of Food Protection 100140 - 100140 2023年08月 [査読有り]
- Takashi Yamamoto, J. Nicholas Taylor, Shige Koseki, Kento KoyamaLWT 114449 - 114449 2023年01月 [査読有り]
- Junpei Hosoe, Junya Sunagawa, Shinji Nakaoka, Shige Koseki, Kento KoyamaFrontiers in Food Science and Technology 2022年12月15日 [査読有り]
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, aw, 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 aw, 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. - Takemoto, R., Koyama, K., Watanabe, T., Koseki, S., Nakamura, N.Food Packaging and Shelf Life 34 100965 - 100965 2022年12月 [査読有り]
- Matsui, T., Kamata, T., Koseki, S., Koyama, K.Postharvest Biology and Technology 192 111996 - 111996 2022年10月 [査読有り]
- Y. Takahashi, H. Abe, K. Koyama, S. KosekiLetters in Applied Microbiology 75 2 388 - 395 2022年08月 [査読有り]
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. - Marin Tsujihashi, Saki Tanaka, Kento Koayama, Shigenobu KosekiFood and Bioprocess Technology 15 6 1343 - 1358 2022年06月 [査読有り]
- Kohei Takeoka, Hiroki Abe, Kento Koyama, Shigenobu KosekiFood Microbiology 102 103932 - 103932 2022年04月 [査読有り]
- Kento Koyama, Suxing LyuComputers and Electronics in Agriculture 193 106633 - 106633 2022年02月 [査読有り]
- Rio Okaniwa, Kento Koyama, Shigenobu KosekiJournal of Food Measurement and Characterization 16 1 12 - 18 2022年02月 [査読有り]
- Tsujihashi, M., Tanaka, S., Koyama, K., Koseki, S.Food and Bioprocess Technology 15 9 2134 - 2134 2022年
- Kento Koyama, Kyosuke Kubo, Satoko Hiura, Shige KosekiJournal of Microbiological Methods 192 106366 - 106366 2022年01月
- Kyeongmin Lee, Kento Koyama, Kiyoshi Kawai, Shigenobu KosekiMicrobiology Spectrum 9 3 2021年12月22日 [査読有り]
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. - Satoshi Kukuminato, Kento Koyama, Shigenobu KosekiMicrobiology Spectrum 9 3 2021年12月22日 [査読有り]
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. - Satoko Hiura, Shige Koseki, Kento KoyamaScientific Reports 11 1 2021年12月 [査読有り]
Abstract 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. - Shinya Doto, Hiroki Abe, Kento Koyama, Shigenobu KosekiFood Control 130 108288 - 108288 2021年12月 [査読有り]
- Takashi Yamamoto, J. Nicholas Taylor, Shige Koseki, Kento KoyamaJournal of Microbiological Methods 190 106326 - 106326 2021年11月 [査読有り]
- Hiroki Abe, Kohei Takeoka, Yuto Fuchisawa, Kento Koyama, Shigenobu KosekiApplied and Environmental Microbiology 87 20 e0129921 2021年09月28日 [査読有り]
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 ofC. 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. - Yuto Fuchisawa, Hiroki Abe, Kento Koyama, Shigenobu KosekiJournal of Applied Microbiology 132 2 1467 - 1478 2021年09月23日 [査読有り]
- Kento Koyama, Satoko Hiura, Hiroki Abe, Shige KosekiJournal of Theoretical Biology 525 110758 - 110758 2021年09月 [査読有り]
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. - Kento Koyama, Jukka Ranta, Kohei Takeoka, Hiroki Abe, Shige KosekiApplied and Environmental Microbiology 87 15 e0091821 2021年07月13日 [査読有り]
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. - Umi Ogawa, Kento Koyama, Shigenobu KosekiJournal of Microbiological Methods 186 106251 - 106251 2021年07月 [査読有り]
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. - Satoko Hiura, Hiroki Abe, Kento Koyama, Shige KosekiFrontiers in Microbiology 12 674364 - 674364 2021年06月24日 [査読有り]
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 ofBacillus simplex with an initial cell count of 105 and the growth kinetic data ofListeria 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. - Shige Koseki, Kento Koyama, Hiroki AbeCurrent Opinion in Food Science 39 60 - 67 2021年06月 [査読有り]
- Natsuki Tsuruma, Shinya Doto, Wataru Ishida, Kento Koyama, Shigenobu KosekiFood Control 122 107756 - 107756 2021年04月 [査読有り]
- Byeong-Hyo Cho, Kento Koyama, Shigenobu KosekiJournal of Food Measurement and Characterization 15 2 2021 - 2030 2021年04月 [査読有り]
- Kento Koyama, Marin Tanaka, Byeong-Hyo Cho, Yusaku Yoshikawa, Shige KosekiPLOS ONE 16 3 e0248769 - e0248769 2021年03月19日 [査読有り]
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. - Kentaro Sakai, Jung Hyun Lee, Chawalit Kocharunchitt, Tom Ross, Ian Jenson, Kento Koyama, Shigenobu KosekiFood and Bioprocess Technology 13 12 2094 - 2103 2020年12月 [査読有り]
- Hiroki Abe, Kento Koyama, Shigenobu KosekiApplied and Environmental Microbiology 87 1 2020年10月16日 [査読有り]
ABSTRACT 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 ofCampylobacter 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 ofC. jejuni in culture were cocultured for up to 12 h. The numbers ofC. 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 ofC. 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 ofC. 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 forC. jejuni based on the infection mechanism. The developed predictive model showed a high accuracy of more than 97% and successfully described theC. jejuni invading counts. The bacterial invasion predictive model of this study will be essential for the development of a dose-response model forC. jejuni based on the infection mechanism. - Satoko Hiura, Hiroki Abe, Kento Koyama, Shigenobu KosekiFood Microbiology 91 2020年10月 [査読有り][通常論文]
© 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. - Byeong-Hyo Cho, Kento Koyama, Edenio Olivares Díaz, Shigenobu KosekiFood and Bioprocess Technology 13 9 1579 - 1587 2020年09月 [査読有り]
- Hiroki Abe, Kento Koyama, Kohei Takeoka, Shinya Doto, Shigenobu KosekiFrontiers in Microbiology 11 2020年05月19日 [査読有り][通常論文]
© 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. - Hiroki Abe, Kento Koyama, Shuso Kawamura, Shigenobu KosekiFood Microbiology 82 436 - 444 2019年09月 [査読有り][通常論文]
© 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. - Kento Koyama, Zafiro Aspridou, Shige Koseki, Konstantinos KoutsoumanisFrontiers in Microbiology 10 SEP 2019年09月01日 [査読有り][通常論文]
© 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. - Kento Koyama, Hiroki Abe, Shuso Kawamura, Shige KosekiJournal of Theoretical Biology 469 172 - 179 2019年05月21日 [査読有り][通常論文]
© 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. - Kento Koyama, Hiroki Abe, Shuso Kawamura, Shigenobu KosekiInternational Journal of Food Microbiology 290 125 - 131 2019年02月02日 [査読有り][通常論文]
© 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. - Abe Hiroki, Koyama Kento, Kawamura Shuso, Koseki ShigenobuINTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY 285 129 - 135 2018年11月20日 [査読有り][通常論文]
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. - Kento Koyama, Hidekazu Hokunan, Mayumi Hasegawa, Shuso Kawamura, Shigenobu KosekiFOOD MICROBIOLOGY 68 121 - 128 2017年12月 [査読有り][通常論文]
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. - Kento Koyama, Hidekazu Hokunan, Mayumi Hasegawa, Shuso Kawamura, Shigenobu KosekiAPPLIED AND ENVIRONMENTAL MICROBIOLOGY 83 4 2017年02月 [査読有り][通常論文]
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. - Kento Koyama, Hidekazu Hokunan, Mayumi Hasegawa, Shuso Kawamura, Shigenobu KosekiFOOD MICROBIOLOGY 60 49 - 53 2016年12月 [査読有り][通常論文]
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. - Hidekazu Hokunan, Kento Koyama, Mayumi Hasegawa, Shuso Kawamura, Shigenobu KosekiJOURNAL OF FOOD PROTECTION 79 10 1680 - 1692 2016年10月 [査読有り][通常論文]
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
- 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年
担当経験のある科目（授業）
所属学協会
共同研究・競争的資金等の研究課題
- 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ロッテ財団：奨励研究助成研究期間 : 2025年04月 -2028年03月
- シングルセルから予測する細菌集団の増殖確率日本学術振興会 科学研究費助成事業：研究期間 : 2024年04月 -2027年03月
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- 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：研究期間 : 2024年04月 -2025年03月
- リスク評価のデジタル化：情報収集と解析の自動化による省力化と精度向上内閣府：食品健康影響評価技術研究研究期間 : 2023年 -2025年03月
- 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研究期間 : 2023年08月 -2024年03月
- 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)研究期間 : 2023年08月 -2024年03月
- 日本学術振興会：科学研究費助成事業 若手研究研究期間 : 2021年04月 -2024年03月代表者 : 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
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- 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：研究助成研究期間 : 2022年04月 -2023年03月
- 日本学術振興会：科学研究費助成事業 研究活動スタート支援研究期間 : 2019年08月 -2021年03月代表者 : 小山 健斗100年前に計算された缶詰における芽胞菌の死滅時間の計算は細菌挙動の数理モデル化の第一歩目の報告である。現在の多くの数理モデルはこのBigelowのモデルを原則とし，ある時間における細菌数の平均値を計算する。従来，10^4個以上の細菌数を対象に研究が行われてきたが，食品の汚染のほとんどは低濃度での汚染が原因であり，100個未満の少ない細菌では個体差の影響が表れ平均値での評価が困難である。生物と統計由来のばらつきにより，細菌の増殖・死滅挙動は平均値から離れる。そのため，平均値では細菌挙動の全体を把握できず，リスクを考慮した微生物学的な安全性の評価が不十分である。平均値による点での評価から，確率による幅での評価への転換が求められている。確率論を用い，ばらつきを含めた細菌挙動全体の定量的な評価が必要である。 本研究の目的は，生物・統計由来のばらつきを含めた細菌集団の挙動の予測をする確率モデルの開発である。屠殺・収穫直後から喫食に至るまでの生鮮食品における細菌挙動のデータ収集し，平均値から離れた増殖・死滅挙動を予測する。ベイズ統計を用い，生物・統計由来のばらつきを包括的に扱う。食中毒細菌による健康被害のリスクを計算するために必要な喫食時の細菌数の確率分布，消費期限の設定に必要なある細菌数に至る時間の確率分布を明確にする。本研究は，100年前に提案された平均値に基づく頻度論統計からベイズ統計へと計算手法を根本的に変える挑戦的な研究である。ばらつきを包括的に扱う試みはカビやウイルスによる健康被害の計算にも応用が可能であるため関連する研究分野への波及研効究果が高い。
- 日本学術振興会：科学研究費助成事業 特別研究員奨励費研究期間 : 2017年04月 -2019年03月代表者 : 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。
- 細菌集団の死滅時間予測のためのタイムラプスを用いた死滅確率過程モデル日本学術振興会：若手研究者海外挑戦プログラム研究期間 : 2017年04月 -2018年03月