Effect of water activity on the mechanical glass transition and dynamical transition of bacteria.

The purpose of this study was to clarify the glass-transition behavior of bacteria (Cronobacter sakazakii) as a function of water activity (aw). From the water sorption isotherm (298 K) for C. sakazakii, monolayer water content and monolayer aw were determined to be 0.0724 g/g-dry matter and 0.252, respectively. Mechanical relaxation was investigated at 298 K. In a higher aw range of over 0.529, the degree of mechanical relaxation increased with an increase in aw. From the effect of aw on the degree of mechanical relaxation, the mechanical awc (aw at which mechanical glass transition occurs at 298 K) was determined to be 0.667. Mean-square displacement of atoms in the bacteria was investigated by incoherent elastic neutron scattering. The mean-square displacement increased gradually with an increase in temperature depending on the aw of samples. From the linear fitting, two or three dynamical transition temperatures (low, middle, and high Tds) were determined at each aw. The low-Td values (142-158 K) were almost independent from aw. There was a minor effect of aw on the middle Td (214-234 K) except for the anhydrous sample (261 K). The high Td (252-322 K) largely increased with the decrease in aw. From the aw dependence of the high Td, the dynamical awc was determined to be 0.675, which was almost equivalent to the mechanical awc. The high Td was assumed to be the glass-transition temperature (Tg), and anhydrous Tg was estimated to be 409 K. In addition, molecular relaxation time (τ) of the bacteria was calculated as a function of aw. From the result, it is suggested that the progress of metabolism in the bacterial system requires a lower τ than approximately 6 × 10-5 s.

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

AIMS: The biological events occurring during human digestion help to understand the mechanisms underlying the dose-response relationships of enteric bacterial pathogens. To better understand these events, we investigated the growth and reduction behaviour of bacterial pathogens in an in vitro model simulating the environment of the small intestine. METHODS AND RESULTS: The foodborne pathogens Campylobacter jejuni, Listeria monocytogenes and Escherichia coli O157:H7 were cultured with multiple competing enteric bacteria. Differences in the pathogen's growth kinetics due to the relative amount of competing enteric bacteria were investigated. These growth differences were described using a mathematical model based on Bayesian inference. When pathogenic and enteric bacteria were inoculated at 1 log CFU per ml and 9 log CFU per ml, respectively, L. monocytogenes was inactivated over time, while C. jejuni and E. coli O157:H7 survived without multiplying. However, as pathogen inocula were increased, its inhibition by enteric bacteria also decreased. CONCLUSIONS: Although the growth of pathogenic species was inhibited by enteric bacteria, the pathogens still survived. SIGNIFICANCE AND IMPACT OF THE STUDY: Competition experiments in a small-intestine model have enhanced understanding of the infection risk in the intestine and provide insights for evaluating dose-response relationships.

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

Campylobacter jejuni-related foodborne diseases are mainly attributed to the consumption of undercooked chicken meat and cross-contaminated produce. This study aimed to develop a survival kinetics model, based on the Weibull model, for predicting foodborne C. jejuni survival during gastric digestion in a model stomach. We previously confirmed that C. jejuni can survive temperatures up to 62 °C; therefore, certain types of grilled chicken skewers (yakitori) were examined for C. jejuni survival during simulated gastric digestion. C. jejuni survival on a chicken thigh following grilling was examined to confirm the foods for digestion experiments. Further, C. jejuni survival during model digestion was investigated through simultaneous digestion of raw chicken and cross-contaminated iceberg lettuce. The model stomach pH increased from 1.5 to 6.0 immediately after yakitori ingestion and did not decrease below 4.0 within 3 h of digestion. Gastric digestion did not significantly contribute to C. jejuni inactivation (<1.5 log reduction after 3 h digestion). Our model could predict C. jejuni survival kinetics in simulated gastric fluid under varying pH during model digestion. This approach can be used to predict C. jejuni survival rates following digestion to improve food safety and reduce Campylobacter-related disease outbreaks.

Inhibitory effect of d-Tryptophan on the spoilage potential of Shewanella baltica and Pseudomonas fluorescens and its potential application in salmon fillet preservation.

In this study, we found the inhibitory effect of d-Tryptophan (d-Trp) on the bacterial growth and spoilage potential of P. fluorescens and S. baltica. The effectiveness of d-Trp in bacterial growth inhibition was highly dependent on environmental NaCl concentrations. The growth/no growth models were developed to clarify the relationship between salt and d-Trp concentrations for optimal growth-inhibitory effect. It showed that higher levels of NaCl (>3.5%) with d-Trp (>15 mM) are required for growth inhibition. The d-Trp treatment combined with NaCl also retarded the psychrotrophic growth and reduced the respiratory active bacteria at low temperature, indicating that the inhibitory effect of d-Trp might be associated with the tolerance to osmotic and cold stress. Furthermore, the treatment d-Trp combined NaCl (artificial seawater) on salmon fillets was investigated. During refrigerated storage, the d-Trp treated salmon fillet either inoculated with S. baltica or P. fluorescen maintained remained a relatively low level of total volatile basic nitrogen (TVBN) (below 30 mg N/100 g during 8-days storage) and delayed increase of low total viable bacteria counts (TVC) with a longer lag phase of 2 days.

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

Understanding the dose-response relationship between ingested pathogenic bacteria and infection probability is a key factor for appropriate risk assessment of foodborne pathogens. The objectives of this study were to develop and validate a novel mechanistic dose-response model for Campylobacter jejuni and simulate the underlying mechanism of foodborne illness during digestion. Bacterial behavior in the human gastrointestinal environment, including survival at low pH in the gastric environment after meals, transition to intestines, and invasion to intestinal tissues, was described using a Bayesian statistical model based on the reported experimental results of each process while considering physical food types (liquid versus solid) and host age (young adult versus elderly). Combining the models in each process, the relationship between pathogen intake and the infection probability of C. jejuni was estimated and compared with reported epidemiological dose-response relationships. Taking food types and host age into account, the prediction range of the infection probability of C. jejuni successfully covered the reported dose-response relationships from actual C. jejuni outbreaks. According to sensitivity analysis of predicted infection probabilities, the host age factor and the food type factor have relatively higher relevance than other factors. Thus, the developed "key events dose-response framework" can derive novel information for quantitative microbiological risk assessment in addition to dose-response relationship. The framework is potentially applicable to other pathogens to quantify the dose-response relationship from experimental data obtained from digestion. IMPORTANCE 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. In addition, the developed prediction procedure revealed that the dose-response relationship strongly depends on food type as well as host age. The implementation of the key events dose-response framework will mechanistically and logically reveal the dose-response relationship and provide useful information with quantitative microbiological risk assessment of C. jejuni on foods.

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

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.

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.

Predictive Growth Model of Listeria monocytogenes Under Fluctuating Temperature Conditions in Pasteurized Milk by Using Real-Time Polymerase Chain Reaction.

The aim of this study was to evaluate the application of real-time polymerase chain reaction (PCR)-based quantification as a rapid and accurate tool for the monitoring and prediction of Listeria monocytogenes growth in pasteurized milk under constant and fluctuating temperature conditions. The growth of L. monocytogenes was monitored under constant temperature conditions at 4°C, 10°C, 15°C, 20°C, and 35°C. High correlation was obtained between the bacterial growth rate and incubation temperature, where the R2 of the slope of the square root model was calculated to be 0.993 and 0.996 for real-time PCR and the conventional culture method, respectively. Moreover, the obtained maximum specific growth rate (µmax) data plots were correlated with 188 L. monocytogenes µmax data points from the existing model according to ComBase database, with an R2 of 0.961 for real-time PCR and of 0.931 for the conventional culture method. The growth models were examined under three different patterns of fluctuating temperature conditions ranging from 2°C to 30°C. The prediction results fell within ±20% of the relative error zone, showing that real-time PCR quantification could be used for fast, sensitive, and specific bacterial growth monitoring with high-throughput results. Real-time PCR should be considered a promising option and powerful tool for the construction of a bacterial growth prediction model for safety risk analysis in the dairy industry.

Novel antibacterial modalities against methicillin resistant Staphylococcus aureus derived from plants.

Methicillin-resistant Staphylococcus aureus (MRSA) is a notorious bacterial pathogen that induces high mortality and morbidity. Due to the emergence of multiple resistance, antibiotic treatments are rapidly becoming ineffective for the related infections. Natural products, especially those derived from plants, have been proven to be effective agents with unique antibacterial properties through different mechanisms. This review interprets the resistance mechanisms of MRSA with the aim to conquer public health threat. Further, recent researches about plant antimicrobials that showed remarkable antibacterial activity against MRSA are recorded, including the crude plant extracts and purified plant-derived bioactive compounds. Novel anti-MRSA modalities of plant antimicrobials such as alteration in efflux pump, inhibition of pyruvate kinase, and disturbance of quorum sensing in MRSA are also summarized which may be promising alternatives to antibacterial drug development in future.

Modeling growth limits of Bacillus spp. spores by using deep-learning algorithm.

Growth/no growth boundary models for Bacillus spores that accounted for the effects of environmental pH, water activity (aw), acetic acid, lactic acid, bacterial strain, and storage period were developed using conventional logistic regression and machine learning algorithms. Growth in tryptic soy broth at 317 conditions comprising nine levels of pH (4.0-6.5), six levels of aw (0.85-1.00), six levels of acetic acid concentrations (0-0.8%), and five levels of lactic acid concentrations (0-0.8%) was examined to confirm growth limit conditions. All models developed using logistic regression, neural network, and deep learning on the basis of obtained datasets successfully described growth/no growth boundaries of three Bacillus species. Although the logistic regression model failed to describe growth limits under some conditions, neural network and deep learning approaches enabled to determine them in such cases. The developed models were evaluated by independent experimental data of growth in tryptic soy broth and in clam soup. The deep learning model enabled better prediction of independent data with smaller probabilistic variability values than those of the logistic regression and neural network models. The deep learning procedure can be utilized for growth boundary modeling to control bacterial growth safely and flexibly.

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.

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.

Predictive Modeling for the Growth of Salmonella Enteritidis in Chicken Juice by Real-Time Polymerase Chain Reaction.

The goals of this study were to monitor the growth kinetics of Salmonella Enteritidis in chicken juice using real-time polymerase chain reaction (PCR) and to evaluate its efficacy by comparing the results with an experimental database. Salmonella Enteritidis was inoculated in chicken juice samples at an initial inoculum of 104 CFU/mL with inoculated samples incubated at six different temperatures (10, 15, 20, 25, 30, and 35°C). Sampling was carried out for 36 h to observe the growth of Salmonella Enteritidis. The total DNA was extracted from the samples, and the copy number of the Salmonella invasion gene (invA) was quantified by real-time PCR and converted to Salmonella Enteritidis cell concentration. Growth kinetics data were analyzed by the Baranyi and Roberts model to obtain growth parameters, whereas the Ratkowsky's square-root model was used to describe the effect of the interactions between growth parameters and temperature on the growth of Salmonella Enteritidis. The growth parameters of Salmonella Enteritidis obtained from an experiment conducted at a constant temperature were validated with growth data from chicken juice samples that were incubated under fluctuating temperature conditions between 5°C and 30°C for 30-min periods. A high correlation was observed between maximum growth rate (µmax) and storage temperature, indicating that the real-time PCR-monitoring method provides a precise estimation of Salmonella Enteritidis growth in food material with a microbial flora. Moreover, the µmax data reflected data from microbial responses viewer database and ComBase. The results of this study suggested that real-time PCR monitoring provides a precise estimation of Salmonella Enteritidis growth in food materials with a background microbial flora.

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.

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 λ, 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°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 (λ = 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.

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

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 × 10n colony-forming units/2 µl (n = 1, 2, 3, 4, 5) by performing a serial 10-fold dilution, and then we placed 2 µl 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 °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.

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

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.

Isolation and Identification of the Antibacterial Compounds Produced by Maillard Reaction of Xylose with Phenylalanine or Proline.

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.

Antibacterial Effect of Melanoidins Derived From Xylose and Phenylalanine Against Bacillus cereus and Clostridium perfringens.

Melanoidins produced from the combination of D-xylose and L-phenylalanine have been reported to exhibit strong antibacterial effects. This study investigated the influence of environmental factors, such as temperatures (10, 15, 20, 25, 30, 35, 40, and 45°C), pH (5.5, 6.0, 6.5, 7.0, 7.5, and 8.0), and water activity (aw: 0.99, 0.96, and 0.93), on the antibacterial effect of the melanoidins produced from the combination of D-xylose with L-phenylalanine against Bacillus cereus and Clostridium perfringens in culture media. Furthermore, freeze-dried powdered melanoidin was used to determine the minimum concentration for growth inhibition, to compare the antibacterial effect of the melanoidin with conventional food preservatives. The liquid melanoidins significantly inhibited the growth of B. cereus (up to 4 log CFU/mL at the maximum) and C. perfringens (up to 6.5 log CFU/mL at the maximum) regardless of the incubation temperatures. However, the remarkable difference between the presence and absence of the melanoidins was demonstrated in the range of 20-35°C as 4 log-cycle lower in B. cereus and 2 log-cycle lower in C. perfringens than those without the melanoidins. The antibacterial effect of the melanoidin on B. cereus was not influenced by pH from 5.5 to 7.0, which exhibited 2-3 log-cycle lower viable counts than those without the melanoidin. Only one log-cycle difference between with and without the melanoidin was shown in C. perfringens growth under the pH range of 5.5-7.0. Although there was no significant difference in the growth of B. cereus between three aw conditions, the melanoidin exhibited a significant antibacterial effect at aw 0.99 demonstrating 4 log-cycle lower viable numbers than those without the melanoidin. Minimum inhibitory concentration of the melanoidin powder for B. cereus and C. perfringens was 7 mg/mL and 15 mg/mL, respectively, regardless of the kind of foods. Furthermore, the melanoidin exhibited comparable antibacterial effect on B. cereus and C. perfringens to potassium sorbate and sodium benzoate under the same concentration as the minimum inhibitory concentration of the melanoidin, demonstrating 2 log-cycle reduction during 3 days of incubation period at 25°C. The results presented here suggest that the xylose- and phenylalanine-based melanoidin demonstrates the possibility to be an alternative food preservative.

Non-destructive quality classification of rice taste properties based on near-infrared spectroscopy and machine learning algorithms.

The taste quality of rice is determined by protein and amylose percentages, with low levels indicating high-quality taste in Japan. However, accurate non-destructive screening remains a challenge for the industry. We explored the use of machine learning models and near-infrared spectra to classify rice taste quality. Three models were optimized using 796 brown rice samples from Hokkaido, Japan, produced between 2008 and 2016, and tested on 278 distinct samples from the same region produced between 2017 and 2019. Logistic regression and support vector machine models outperformed the partial least-squares discriminant analysis model, achieving high accuracy (94%), f1-score (90%), average precision (0.94), and low classification error (4%) and allowing accurate non-destructive classification of rice quality. These results not only improve rice quality, post-harvest technology, and producer output in Japan but also could enhance quality control processes and foster the production of high-quality products for other agricultural goods and food commodities worldwide.

Non-destructive assessment of amylose content in rice using a quality inspection system at grain elevators.

Amylose content is an important determinant of rice quality. Its accurate, non-destructive assessment is a challenge to the industry. We examined whether the automatic quality inspection system accurately measured the amylose content. Brown and milled rice models were calibrated with 902 samples produced between 2008 and 2017 in Hokkaido, Japan, and validated individually by samples collected in 2018 (n = 33) and 2019 (n = 71) from several grain elevators within the region. Models were developed by processing the automatic system data comprising of a near-infrared spectrometer, a visible light grain segregator, combined analysis of chemometrics, and by merging low and ordinary amylose variety validation results. The lower standard error of prediction (<0.52%) and a higher ratio of performance deviation (>4.0) in both models enabled accurate non-destructive assessment of amylose content. Hence, the automatic quality inspection system is a useful tool for meeting the demands of higher quality and palatability of rice.

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

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 µS/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 (R2 > 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 °C and 60 °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.