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Salmonella enterica is an important foodborne pathogen that constitutes a major health hazard. The emergence and aggravation of antibiotic-resistant Salmonella has drawn attention widely around the world. Conducting a risk assessment of antibiotic-resistant foodborne pathogens throughout the food chain is a pressing requirement for ensuring food safety. The growth, survival capability, and virulence of antibiotic-resistant Salmonella represent crucial biological characteristics that play an important role in microbial risk assessment. In this study, eight antibiotic-sensitive S. enterica strains were induced by Ampicillin (Amp) and Ciprofloxacin (CIP), respectively, and AMP-resistant and CIP-resistant mutants were obtained. The growth characteristics under different temperatures (25, 30, 35 °C), viability after exposure to heat (55, 57.5, 60 °C) and acid (HCl, pH = 3.0), the virulence potential (adhesion and invasion to Caco-2 cells, biofilm formation and motility) and the lethality in a model species (Galleria mellonella) were evaluated and compared for S. enterica strains before and after antibiotic exposure. The induction by AMP and CIP are likely to promote cross-antibiotic resistance to their antibiotic classes, β-lactams and quinolones, as well as some compound antibiotics. It was observed that generally the antibiotic-induction-resistant strains showed decreased growth ability and lower heat resistance, although the differences were not significant at all the conditions tested. The AMP-resistant strains were significantly less acid resistance than the sensitive and the CIP-resistant ones, while exhibiting increased biofilm formation ability. In general, the antibiotic-induced resistance did not significantly affect the motility, adherence, or invasion ability of Caco-2 cells. However, CIP-resistant strains displayed lower lethality in G. mellonella infection, whereas AMP-resistant strains did not, and even two strains improved lethality. The study of the biological characteristics of antibiotic-resistant S. enterica is essential in better understanding the microbial risks to both the food chain and human health, thereby facilitating a more accurate risk assessment., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Salmonella is a common chicken-borne pathogen that causes human infections. Data below the detection limit, referred to as left-censored data, are frequently encountered in the detection of pathogens. The approach of handling the censored data was regarded to affect the estimation accuracy of microbial concentration. In this study, a set of Salmonella contamination data was collected from chilled chicken samples using the most probable number (MPN) method, which consisted of 90.42% (217/240) non-detect values. Two simulated datasets with fixed censoring degrees of 73.60% and 90.00% were generated based on the real-sampling Salmonella dataset for comparison. Three methodologies were applied for handling left-censored data: (i) substitution with different alternatives, (ii) the distribution-based maximum likelihood estimation (MLE) method, and (iii) the multiple imputation (MI) method. For each dataset, the negative binomial (NB) distribution-based MLE and zero-modified NB distribution-based MLE were preferable for highly censored data and resulted in the least root mean square error (RMSE). Replacing the censored data with half the limit of quantification was the next best method. The mean concentration of Salmonella monitoring data estimated by the NB-MLE and zero-modified NB-MLE methods was 0.68 MPN/g. This study provided an available statistical method for handling bacterial highly left-censored data., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Contaminations of pathogenic and spoilage microbes on foods are threatening food safety and quality, highlighting the importance of developing antimicrobial agents. According to different working mechanisms, the antimicrobial activities of yeast-based agents were summarized from two aspects: antagonism and encapsulation. Antagonistic yeasts are usually applied as biocontrol agents for the preservation of fruits and vegetables via inactivating spoilage microbes, usually phytopathogens. This review systematically summarized various species of antagonistic yeasts, potential combinations to improve the antimicrobial efficiency, and the antagonistic mechanisms. The wide applications of the antagonistic yeasts are significantly limited by undesirable antimicrobial efficiency, poor environmental resistance, and a narrow antimicrobial spectrum. Another strategy for achieving effective antimicrobial activity is to encapsulate various chemical antimicrobial agents into a yeast-based carrier that has been previously inactivated. This is accomplished by immersing the dead yeast cells with porous structure in an antimicrobial suspension and applying high vacuum pressure to allow the agents to diffuse inside the yeast cells. Typical antimicrobial agents encapsulated in the yeast carriers have been reviewed, including chlorine-based biocides, antimicrobial essential oils, and photosensitizers. Benefiting from the existence of the inactive yeast carrier, the antimicrobial efficiencies and functional durability of the encapsulated antimicrobial agents, such as chlorine-based agents, essential oils, and photosensitizers, are significantly improved compared with the unencapsulated ones., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Hyperspectral imaging (HSI) is a promising technique that can replace the time-consuming and destructive plate count method. In this study, growth models of Pseudomonas fluorescens (P. fluorescens) in pork using the plate count method and HSI were established. Hyperspectral information extracted by three different methods was used for the growth simulation of P. fluorescens. The Baranyi model in combination with the Ratkowsky square-root model (BRSR model) and the Huang model in combination with the Ratkowsky square-root model (HRSR model) for P. fluorescens in pork were directly constructed using a one-step method. Results showed that the correlation coefficients between the growth models established using HSI and the plate count method were high, ranging from 0.952-0.999. The adjusted coefficient of determination (R a 2 ) of simulation models reached 0.974, and the growth parameters obtained were consistent with those obtained using the plate count method. This study demonstrated that HSI may be potentially used as a new predictive microbial modeling technique in the future., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The bacterial heat shock response in foodborne pathogens is caused by exposure to higher temperatures which poses a great threat to food safety because it can undermine food processing interventions and host defense. The study assessed the heat and acid resistance of Cronobacter sakazakii following heat shock (53 °C for 15min). Inactivation curves of the heat-shocked and non-shocked C. sakazakii cells at four temperatures (56, 58, 60, and 62 °C) and simulated gastric fluid (SGF, pH 3.0) were examined and fitted with the log-linear model and the Weibull model. The inactivation parameters obtained on the basis of the Weibull model showed that heat shock significantly (p < 0.05) increased the values of δ (time to reach 1 log reduction) and t 3d (time to reach 3 log reduction) under thermal and acid inactivation. The results proved that heat shock provided C. sakazakii direct protection from a more adverse heat challenge and cross-protection from SGF, i.e. there was a heat shock response. Results of sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) revealed that seven protein species showed enhanced expression, while four protein species showed decreased expression in the heat-shocked compared to the non-heat-shocked C. sakazakii cells. Quantitative real-time reverse transcriptase PCR (RT-qPCR) revealed upregulation of six stress related genes, ibpA, ibpB (both encoding molecular chaperons), Hsp15 (encoding heat shock protein), Hsp20 (encoding small heat-shock protein), HspQ (encoding proteases) and rpoS (encoding stationary phase sigma factor), following heat shock treatment. In addition, heat shock induced an increase proportion of saturated fatty acids (SFA), cyclic fatty acids (CFA) and the ratio of saturated fatty acids to unsaturated fatty acids (SFA/USFA), whereas reducing the proportion of unsaturated fatty acids (USFA). Consequently, establishment of inactivation models of C. sakazakii could provide data support for quantitative microbial risk assessment (QMRA). Exploration of enhanced resistance mechanisms might provide clues for prevention and control of contamination by heat-shocked C. sakazakii., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Abstract: Given the importance of strain variability to predictive microbiology and risk assessment, this study aimed to quantify the magnitude of strain variability in growth and thermal inactivation kinetics behaviors after acid adaptation. Thirty-three Listeria monocytogenes strains were exposed to acid-adapted tryptic soy broth supplemented with yeast extract (TSBYE; pH 5.5) and non-acid-adapted TSBYE (pH 7.0) for 20 h. Next, the growth parameters of these adapted and nonadapted strains that grew in nonbuffered TSBYE at 25°C were estimated. The tested strains were inactivated at 60°C in nonbuffered broth to obtain the heat resistance parameters. The results revealed that strain variability was present in the growth and thermal inactivation characteristics. The maximum specific growth rate ranged from 0.21 to 0.44 h-1 and from 0.20 to 0.45 h-1 after acid and nonacid adaptation, respectively. The lag times were from 0.69 to 2.56 h and from 0.24 to 3.36 h for acid-adapted and non-acid-adapted cells, respectively. The apparent D-values at 60°C of the pathogen ranged between 0.56 and 3.93 min and between 0.52 and 3.63 min for the presence and absence of acid adaptation condition, respectively. Acid adaptation significantly (P < 0.05) increased the magnitude of strain variability in the thermal inactivation characteristics of L. monocytogenes, with the coefficient of variation increasing to 0.17, whereas acid adaptation did not significantly (P ≥ 0.05) influence the variabilities in the growth parameters of the tested strains. Furthermore, the subsequent growth behaviors of all strains did not exhibit significant (P > 0.05) changes after exposure to acidic broth. However, the thermal resistance of most (25 of 33) of the tested strains increased (P < 0.05) after growing in acid-adapted broth. The relevant data generated in the present study can be used to describe the strain variability in predictive microbiology and to deeply understand the behavioral responses of different strains to acid adaptation., (Copyright ©, International Association for Food Protection.)

Pseudomonas have a faster growth rate over other bacteria in chilled meat under aerobic conditions. A non-destructive method for modelling the dynamic growth of Pseudomonas in pork stored at different temperatures using gas sensors was presented in our work. Based on selected gas sensor data, the first-order kinetic equations (Gompertz and Logistic Functions) combined with the secondary model (Square-root Function) effectively simulated Pseudomonas growth in pork at different temperatures with R 2 and RMSE values of 0.71-0.97 and 0.27-0.84, respectively. Additionally, these models showed high accuracy with correlation coefficients greater than 0.90, in addition to several individual accuracy values. Furthermore, HS-SPME/GC-MS results demonstrated the presence of identified key volatiles in samples inoculated with Pseudomonas, including three amine compounds (mercaptamine, 1-octanamine and 1-heptadecanamine), phenol and indole. Our work showed that gas sensors are a rapid, easy and non-destructive method with acceptable feasibility in modelling the dynamic growth of spoilage microorganisms in meat., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In predictive microbiology, the study of the microbial lag phase, i.e., the time needed for bacteria to adapt to a new environment before multiplying, has received a great deal of attention in the research literature. The microbial lag phase is more difficult to estimate than the specific growth rate because the lag phase is impacted by the previous and actual growth environments. In this study, the growth of Listeria monocytogenes preincubated at 0, 5, 10, and 15°C and subsequently grown at 25°C was investigated at the single-cell and population levels. The population lag phase of L. monocytogenes was obtained by fitting the Baranyi model, and the single-cell lag time was estimated by the time to detection method. The lag phase at the single-cell and population levels of L. monocytogenes presented a downward trend as the preculture temperature ranged from 0 to 15°C. The population lag phase of L. monocytogenes was lower than the single-cell lag time at the same preculture temperature. In addition, except for the zero-lag distribution at a preculture temperature of 15°C, all the single-cell lag time distributions of L. monocytogenes followed a Weibull distribution under all preculture temperatures. The preculture temperature had a significant impact on the rapid variation in the single-cell lag time distribution. Thus, the influence of preculture temperature on the lag phase needs to be quantitatively analyzed for better assessment of microbiological risk.

This study aimed to estimate the growth parameters of Listeria monocytogenes growth in ready-to-eat (RTE) braised beef by one-step dynamic and static kinetic analysis. The Baranyi model and cardinal parameters model were integrated into a dynamic and static model to estimate the kinetic parameters under one dynamic condition (-20 to 40.0°C) and eight static conditions (4, 8, 15, 20, 30, 35, 37, and 40°C). Based on the dynamic and static methods, the respective dynamic and static results for estimated growth boundaries of L. monocytogenes in RTE braised beef were from -2.5 and -2.7°C to 40.5 and 40.7°C with optimal specific growth rates of 1.078 and 0.913 per h at temperatures of 35.7 and 35.0°C. Temperature effects on the specific growth rate and lag period were developed and used to simulate the change of the physiological state of inocula during the bacterial growth. Subsequently, three additional dynamic temperature profiles were implemented for external validation. The root mean square error of the model developed by dynamic regression (0.19 log CFU/g) is slightly better than that of the model developed by static regression (0.23 log CFU/g). Comparing the validation results, one-step dynamic analysis might be a preferable method for prediction, especially when the growth approaches the stationary phase. Generally, both one-step dynamic and static analyses could be used to accurately predict L. monocytogenes growth in RTE braised beef under fluctuating temperatures.

Cities in China suffer from severe smog and haze, and a forecasting system with high accuracy is of great importance to foresee the concentrations of the airborne particles. Compared with chemical transport models, the growing artificial intelligence models can simulate nonlinearities and interactive relationships and getting more accurate results. In this paper, the Kolmogorov-Zurbenko (KZ) filter is modified and firstly applied to construct the model using an artificial intelligence method. The concentration of inhalable particles and fine particulate matter in Dalian are used to analyze the filtered components and test the forecasting accuracy. Besides, an extended experiment is made by implementing a comprehensive comparison and a stability test using data in three other cities in China. Results testify the excellent performance of the developed hybrid models, which can be utilized to better understand the temporal features of pollutants and to perform a better air pollution control and management., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Clostridium sporogenes is considered to be a non-toxingenic equivalent of proteolytic Clostridium botulinum, and it also causes food spoilage. The effects of temperature (16.6-33.4 degrees C), pH value (5.2-6.8) and concentration of sodium chloride (0.6-7.4%) on the growth parameters of C. sporogenes spores were investigated. The growth curves generated within different conditions were fitted using Baranyi function. Two growth parameters (growth rate, GR; lag-time, LT) of the growth curves under combined effects of temperature, pH and sodium chloride were modeled using a quadratic polynomial equation of response surface (RS) model. Mathematical evaluation demonstrated that the standard error of prediction (%SEP) obtained by RS model was 1.033% for GR and was 0.166% for LT for model establishing. The %SEP for model validation were 43.717% and 5.895% for GR and LT, respectively. The root-mean-squares error (RMSE) was in acceptable range which was less than 0.1 for GR and was less than 8.0 for LT. Both the bias factor (B(f)) and accuracy factor (A(f)) approached 1.0, which were within acceptable range. Therefore, RS model provides a useful and accurate method for predicting the growth parameters of C. sporogenes spores, and could be applied to ensure food safety with respect to proteolytic C. botulinum control.