Endothelial progenitor cells as an emerging cardiovascular risk factor in the field of food and nutrition research: advances and challenges.

Dietary modifications can help prevent many cardiovascular disease (CVD) events. Endothelial progenitor cells (EPCs) actively contribute to cardiovascular system maintenance and could function as surrogate markers for evaluating improvement in cardiovascular health resulting from nutritional interventions. This review summarizes the latest research progress on the impact of food and nutrients on EPCs, drawing on evidence from human, animal, and in vitro studies. Additionally, current trends and challenges faced in the field are highlighted. Findings from studies examining cells as EPCs are generally consistent, demonstrating that a healthy diet, such as the Mediterranean diet or a supervised diet for overweight people, specific foods like olive oil, fruit, vegetables, red wine, tea, chia, and nutraceuticals, and certain nutrients such as polyphenols, unsaturated fats, inorganic nitrate, and vitamins, generally promote higher EPC numbers and enhanced EPC function. Conversely, an unhealthy diet, such as one high in sugar substitutes, salt, or fructose, impairs EPC function. Research on outgrowth EPCs has revealed that various pathways are involved in the modulation effects of food and nutrients. The potential of EPCs as a biomarker for assessing the effectiveness of nutritional interventions in preventing CVDs is immense, while further clarification on definition and characterization of EPCs is required.

Exposure of Salmonella enterica serovar 1,4,[5],12:i:- to benzalkonium chloride leads to acquired resistance to this disinfectant and antibiotics.

AIMS: Disinfectants such as benzalkonium chloride (BC), extensively used in animal farms and food-processing industries, contribute to the development of adaptive and cross-resistance in foodborne pathogens, posing a serious threat to food safety and human health. The purpose of this study is to explore whether continuous exposure of Salmonella enterica serovar 1,4,[5],12:i:- (S. 1,4,[5],12:i:-) to sublethal concentrations of BC could result in acquired resistance to this agent and other environmental stresses (e.g. antibiotics, heat, and acid). METHODS AND RESULTS: BC tolerance increased in all tested strains after exposure to gradually increasing concentrations of BC, with increases in minimum inhibitory concentrations between two and sixfold. The survival rate of BC-adapted strains was significantly (P < 0.05) higher than that of their wild-type (non-adapted) counterparts in lethal concentrations of BC. In addition, significant reductions (P < 0.05) in zeta potential were observed in BC-adapted strains compared to wild-type ones, indicating that a reduction in cell surface charge was a cause of adaptative resistance. More importantly, two BC-adapted strains exhibited increased antibiotic resistance to levofloxacin, ceftazidime, and tigecycline, while gene mutations (gyrA, parC) and antibiotic efflux-related genes (acrB, mdsA, mdsB) were detected by genomic sequencing analysis. Moreover, the tolerance of BC-adapted strains to heat (50, 55, and 60°C) and acid (pH 2.0, 2.5) was strain-dependent and condition-dependent. CONCLUSIONS: Repeated exposure to sublethal concentrations of BC could result in the emergence of BC- and antibiotic-resistant S. 1,4,[5],12:i:- strains.

Antimicrobial function of yeast against pathogenic and spoilage microorganisms via either antagonism or encapsulation: A review.

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.

Distribution-based maximum likelihood estimation methods are preferred for estimating Salmonella concentration in chicken when contamination data are highly left-censored.

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.

Effects of antibiotic-induced resistance on the growth, survival ability and virulence of Salmonella enterica.

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, ß-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.

Risk measurement and application of the international carbon market in the era of global conflict: A data-driven study using FCM.

The accurate measurement and effective estimation of carbon market risk are crucial for practitioners and policymakers to mobilize resources toward the transition to a climate-resilient economy, particularly in a new era of global conflict. However, existing studies that have explored factors contributing to carbon market risk primarily relied on experience or subjective judgment in selecting risk-related factors. Such approaches undermine the estimation accuracy while making it difficult to ascertain causal inferences related to the risk spillover. To fill the gap, we adopted a data-driven factor analysis strategy by introducing the Fuzzy Cognitive Maps (FCM) model to establish a carbon market network and identify risk-related factors. We then evaluate the carbon market's risk level and spillover effects using combined econometric methods and explore their application in portfolio management. We report three main findings. First, based on our sample of 3217 observations between 2008 and 2022, five factors influencing carbon market risk emerged from the FCM, including OIL, COAL, SP500ENERGY, SPCLEANENERGY, and GPR. Second, we find a notable rise in risk spillover from GPR to EUA during the Russia-Ukraine conflict and an escalation of total cross-market spillover during extreme events. Third, our study presents new evidence on the hedging effect for EUA of the SP500ENERGY before the Russia-Ukraine conflict and of the SPCLEANENERGY during the conflict. Finally, implications are discussed for policymakers and investors.

Heat shock in Cronobacter sakazakii induces direct protection and cross-protection against simulated gastric fluid stress.

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 t3d (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.

Microrisk Lab: An Online Freeware for Predictive Microbiology.

Microrisk Lab is an R-based online modeling freeware designed to realize parameter estimation and model simulation in predictive microbiology. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/inactivation under static and nonisothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/inactivation under static and nonisothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate data set and/or parameter setting. In this study, six case studies were reproduced in Microrisk Lab and compared in parallel with DMFit, GInaFiT, IPMP 2013/GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling systems. Microrisk Lab allows for a friendly user experience when modeling microbial behaviors owing to its interactive interfaces, high integration, and interconnectivity. Users can freely access this application at https://microrisklab.shinyapps.io/english/ or https://microrisklab.shinyapps.io/chinese/.

Antibiotic Resistance Patterns of Staphylococcus aureus Isolates from Retail Foods in Mainland China: A Meta-Analysis.

Foodborne Staphylococcus aureus, including methicillin-resistant S. aureus (MRSA), is increasingly threatening human health. Pooled prevalence rates of S. aureus contamination have been extensively studied in retail food products in mainland China, but data regarding antibiotic resistance rates of S. aureus remain scattered. This study was designed to collect researches published between 2007 and 2017 in mainland China and to evaluate the antibiotic resistance of S. aureus from retail foods using a meta-analytic approach. We systematically searched the China National Knowledge Infrastructure (CNKI) and Web of Science databases to identify peer-reviewed literature. A number of multilevel random-effects models were fitted to estimate mean occurrence rates of antibiotic-resistant S. aureus, and subgroup analyses were performed to compare antibiotic resistance rates of S. aureus throughout the years and among the methods to determine the antimicrobial susceptibility. Among the considered antibiotics, S. aureus showed the highest resistance rate to penicillin G (87%, 95% confidence interval [CI] 83-90%), followed by ampicillin (72%, 95% CI 62-81%) and erythromycin (41%, 95% CI 36-46%). MRSA showed the highest resistance rate to ampicillin (98%, 95% CI 89-100%), followed by oxacillin (97%, 95% CI 80-100%) and penicillin G (96%, 95% CI 89-99%). Multidrug resistance (MDR) of S. aureus was most frequently observed to three antibiotics (17%, 95% CI 12-22%), and MRSA showed the highest resistance rate to four antibiotics (24%, 95% CI 5-67%). Subgroup analyses results proved that sources of heterogeneity among studies were neither publication year nor detection method. In conclusion, the meta-analysis showed that ß-lactam antibiotics resistance of S. aureus and MRSA strains isolated from retail foods remained the most serious, and MDR of S. aureus and MRSA were also observed. Therefore, it is important to monitor the antibiotic resistance of S. aureus and MRSA in food chain, and food safety measures should be taken to reduce the transmission of this bacterium from foods to human beings.

A colorimetric immunoassay for determination of Escherichia coli O157:H7 based on oxidase-like activity of cobalt-based zeolitic imidazolate framework.

Cobalt-based zeolitic imidazolate framework nanosheets (ZIF-67) with oxidase-like catalytic activities as an immunoprobe were employed to enhance the sensitivity of an immunoassay. ZIF-67 was synthesized via the solvothermal method using 2-methylimidazole and cobalt dichloride as substrates. A colorimetric immunoassay for Escherichia coli (E. coli) O157:H7 was designed. Preparation of the immunoprobe involved self-polymerized dopamine being applied for the surface modification of ZIF-67 nanosheets in order to bind to the antibody, which was used to identify E. coli O157:H7. ZIF-67 catalyze the oxidation of 3,3',5,5'-tetramethylbiphenyl (TMB) and produced a color change from colorless to blue. Upon reaction termination, the absorbance was measured at 450 nm. By combining ZIF-67@PDA catalyzed chromogenic reaction with antibody recognition and magnetic separation, the limit of determination is 12 CFU mL-1 and the linear range is 30 to 3.0 × 108 CFU mL-1. The proposed colorimetric immunoassay was successfully utilized to detect E. coli O157:H7 of spiked food samples. Graphical abstract.

Attenuated Listeria monocytogenes protecting zebrafish (Danio rerio) against Vibrio species challenge.

Live attenuated bacteria is a promising candidate vector for the delivery of vaccines in clinic trials. In the field of aquaculture industry, live vector vaccine also could provide long-term and effective protection against fish bacterial diseases. In our previous work, we demonstrated attenuated Listeria monocytogenes (Lm) had the potential to be an aquaculture vaccine vector in cellular level and zebrafish model. To further investigate the potential application of attenuated Lm in aquaculture vaccines, the outer membrane protein K (OmpK) from Vibrio parahaemolyticus (V. parahaemolyticus), as a conservative protective antigen, was fused to a new antigen-delivery system, and introduced into double-gene attenuated Lm strain (EGDe-ΔactA/inlB, Lmdd) to get live-vector vaccine strain Lmdd-OmpK. The strain Lmdd-OmpK showed the stable secrete efficacy of OmpK and was tested the cross-protective immunity against Vibrio species. After intraperitoneal administration in zebrafish, Lmdd and Lmdd-OmpK strain both improved the survival rates of zebrafish infected by V. parahaemolyticus, Vibrio alginolyticus (V. alginolyticus) and Vibrio anguillarum (V. anguillarum), respectively. In summary, attenuated Lm is able to protect zebrafish against Vibrio species challenge, illustrating its potential value for further aquaculture vaccines development.

Reactive oxygen species inhibit biofilm formation of Listeria monocytogenes.

Although the level of reactive oxygen species (ROS) is altered upon the formation of bacterial biofilm, the relationship between ROS alteration and biofilm formation is still unclear. The aim of the present study is to use Listeria monocytogenes (L. monocytogenes) as a model organism to examine whether ROS have an effect on the biofilm formation. After eliminating ROS by treatment with NAD(P)H oxidase inhibitor Diphenyleneiodonium chloride (DPI) or scavenging reagents N-acetylcysteine (NAC), the biofilm formation of L. monocytogenes was examined. Our data demonstrate that DPI and NAC induced-reduction of ROS enhances the biofilm formation in L. monocytogenes without affecting bacterial growth and activity. These data provide the evidence that ROS produced by L. monocytogenes inhibit the biofilm formation.

Quantitative risk assessment of Listeria monocytogenes in bulk cooked meat from production to consumption in China: a Bayesian approach.

BACKGROUND: To estimate the public health risk related to cooked meat in bulk products contaminated with Listeria monocytogenes, a generic Bayesian network (BN) risk-assessment model was developed to simulate influencing factors and processes of products from the industry level to the consumer level. To quantify the model, parameter values of prior distributions were acquired from the literature, websites, and expert opinions. Using the Markov chain Monte Carlo (MCMC) simulation approach, posterior probability distributions were calculated according to the incorporated evidence, which allowed us to predict various risks affected by processing variability from production to consumption. RESULTS: The average risks of listeriosis from consuming cooked meat in bulk products are 8.40 × 10-7 , 2.58 × 10-8 , 8.24 × 10-7 , and 1.05 × 10-6 per meal for children, young people, elderly people, and pregnant women, respectively. The estimated mean number of listeriosis cases is 5 per 100 000 people per year in China. CONCLUSION: Although only a conceptual BN model is given, it manifests the principles and characteristics of mathematical methods. The BN model can also provide significant benefits for quantitative risk assessment by incorporating all available data and by updating beliefs. © 2018 Society of Chemical Industry.

Exploration of the bacterial invasion capacity of Listeria monocytogenes in ZF4 cells.

Despite the results from zebrafish challenged model have demonstrated that Listeria monocytogenes (Lm) has strong adjuvant effects when this attenuated pathogenic bacteria is viewed as aquaculture vaccine vector, the underlying mechanism is not clear and extensive investigations are required. To further explore the potential of Lm in the field of aquaculture vaccine, zebrafish embryonic fibroblast cell line (ZF4) was used to evaluate the invasion ability of Lm. The data from cellular level showed that Lm had the lower invasion tendentiousness in ZF4 cells while bacterial invasion capacity was compared between zebrafish embryos cell line and human intestinal epithelial cell line. In ZF4 cells, there is no significant difference in bacterial invasion capacity between wild strain EGD-e and double-deleted strain ΔactA/inlB, which suggested that this attenuated effect was not showed in zebrafish cells. In addition, translation analysis indicated that the expressions of CD4 and CD8a in ZF4 cells increased after 2-h infection of the two Lm strains. These results further demonstrated that Lm presented multiple advantages including lower pathogenicity and antigen presentation when attenuated stain was viewed as aquaculture vaccine vector.

Predictive modeling of microbial single cells: A review.

In practice, food products tend to be contaminated with food-borne pathogens at a low inoculum level. However, the huge potential risk cannot be ignored because microbes may initiate high-speed growth suitable conditions during the food chain, such as transportation or storage. Thus, it is important to perform predictive modeling of microbial single cells. Several key aspects of microbial single-cell modeling are covered in this review. First, based on previous studies, the techniques of microbial single-cell data acquisition and growth data collection are presented in detail. In addition, the sources of microbial single-cell variability are also summarized. Due to model microbial growth, traditional deterministic mathematical models have been developed. However, most models fail to make accurate predictions at low cell numbers or at the single-cell level due to high cell-to-cell heterogeneity. Stochastic models have been a subject of great interest; and these models take into consideration the variability in microbial single-cell behavior.

Ultrathin ZIF-67 nanosheets as a colorimetric biosensing platform for peroxidase-like catalysis.

In this work, we report a zeolitic imidazolate framework (ZIF-67) which could catalyze 3,3',5,5'-tetramethylbenzidine (TMB) to produce a yellow chromogenic reaction. ZIF-67 showed high peroxidase-like activity compared with copper-based metal-organic framework nanoparticles (Cu-MOF), zinc-based metal-organic framework nanoparticles (ZIF-8), and horseradish peroxidase (HPR). We discovered for the first time that the cobalt-based metal-organic framework nanoparticles possess intrinsic peroxidase-like activity without H2O2, which can be employed to quantitatively monitor the H2O2. Graphical abstract ᅟ.

A novel hybrid strategy for PM2.5 concentration analysis and prediction.

The analysis and prediction of air pollutants are of great significance in environmental research today since airborne pollution is a substantial threat, especially in urban agglomerations of China. To develop more effective warning systems and management advice, the authorities and city dwellers need more accurate forecasts of the air pollution. Most previous analysis systems were based on costly observation apparatus at fixed sites, forecasting models were usually built on observations within a certain range, and some observations contained biases. In this paper, a novel and effective framework, termed HML-AFNN, was successfully developed to analyse and forecast the concentration of particular matter (PM2.5) for a selected number of forward time steps. In a simulation of the trajectory of air pollutants, the high-dimension association rules (HDAR) approach considered the tempo-spatial relations, as well as the meteorological and geographical factors of the ambient regions, as parameters. In addition, the learning vector quantization (LVQ) network was adopted to select the appropriate inputs to improve the efficiency of the training process. Moreover, an adaptive fuzzy neural network (AFNN), a combination of neural and fuzzy logic, was utilized to analyse and predict the PM2.5 concentration. The experiment results of our study on two major urban agglomerations of China, the Jing-Jin-Ji area and Pearl River Delta, over a period of more than one year demonstrated that the developed hybrid HML-AFNN model outperforms a plain AFNN, an HM-AFNN model without LVQ and the least squares support vector machines (LS-SVM); this superior performance can be determined from the values of several error indexes, including MAE, MAPE and band errors. This hybrid model, which has robust and accurate results, shows the potential to be a political and administrative method to issue effective early warnings and to design suitable abatement strategies.

Modelling postharvest quality of blueberry affected by biological variability using image and spectral data.

BACKGROUND: Hyperspectral reflectance and transmittance sensing as well as near-infrared (NIR) spectroscopy were investigated as non-destructive tools for estimating blueberry firmness, elastic modulus and soluble solid content (SSC). Least squares-support vector machine models were established from these three spectra based on samples from three cultivars viz. Bluecrop, Duke and M2 and two harvest years viz. 2014 and 2015 for predicting blueberry postharvest quality. RESULTS: One-cultivar reflectance models (establishing model using one cultivar) derived better results than the corresponding transmittance and NIR models for predicting blueberry firmness with few cultivar effects. Two-cultivar NIR models (establishing model using two cultivars) proved to be suitable for estimating blueberry SSC with correlations over 0.83. Rp (RMSEp ) values of the three-cultivar reflectance models (establishing model using 75% of three cultivars) were 0.73 (0.094) and 0.73 (0.186), respectively , for predicting blueberry firmness and elastic modulus. For SSC prediction, the three-cultivar NIR model was found to achieve an Rp (RMSEp ) value of 0.85 (0.090). Adding Bluecrop samples harvested in 2014 could enhance the three-cultivar model robustness for firmness and elastic modulus. CONCLUSION: The above results indicated the potential for using spatial and spectral techniques to develop robust models for predicting blueberry postharvest quality containing biological variability. © 2015 Society of Chemical Industry.

[Comparison of Predicting Blueberry Firmness and Elastic Modulus with Hyperspectral Reflectance, Transmittance and Interactance Imaging Modes].

In this study, a imaging system with hyperspectral reflectance, transmittance and interactance was constructed for estimate the firmness and elastic modulus of blueberry. The comparisons of these three imaging modes were carried out. This hyperspectral system could also be applied for scattering modewhile this mode was not suitable for small fruit such as blueberry. The reflectance hypercubes were segmented with the algorithm based on the Otsu method, and the transmittance and interactance hypercubes were processed with the algorithms based on region growing approach. Subsequently, the extracted spectra were pretreated with the Standard Normal Variate (SNV) and Savitzky-Golay of the first derivative (Der), and least squares-support vector machine was applied for the establishment of the corresponding prediction models. The obtained results demonstrated that -reflectance-SNV model could predict blueberry firmness with correlation coefficient of prediction sample set (Rp) of 0.80 and the ratio of percent deviation (RPD) of 1.76 among the models using full spectra. The elastic modulus of blueberry was better estimated by the full transmittance spectra subjected to SNV pretreatment with Rp (RPD) of 0.78 (1.74) than the other models. Furthermore, Random Frog selection approach could to some extent reduce the uninformative wavelengths while increasing the prediction accuracy of the established models. Random Frog-Interactance-Der model achieved Rp (RPD) of 0.80 (1.83) for blueberry firmness, but the number of wavelength was 140. In the case of blueberry elastic modulus, random frog-transmittance-SNV showed the relatively superior performance compared to the other models, with Rp (RPD) of 0.82 (1.83) and fewer wavelength number of 20.

Antibacterial Effect and Possible Mechanism of Sesamol against Foodborne Pathogens.

Food safety problems caused by foodborne pathogens have become a major public issue, and the search for efficient and safe bacteriostatic agents has gained attention. Sesamol (SE), a phenolic compound abundant in sesame oil, offers numerous health benefits and exhibits certain antibacterial properties. The purpose of this study was to evaluate the antibacterial effect and potential mechanisms of SE against representative foodborne pathogens, including Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus, Escherichia coli, and Salmonella serovar Enteritidis. The results showed that SE significantly inhibited the growth of the five pathogenic bacteria in sterile saline and pasteurized milk by 2.16-4.16 log10 CFU/g within 48 h. The results of the minimum bactericidal concentration and time-kill assay showed that SE had a greater inhibitory effect on L. monocytogenes compared with other bacteria. Additionally, SE was found to alter the cell membranes' permeability in these bacteria, resulting in the release of intercellular proteins and DNA. A scanning electron microscopy analysis showed that exposure to SE resulted in significant changes in bacterial morphology, producing cell shrinkage and deformation. These findings suggest that SE could inhibit both Gram-negative and Gram-positive bacteria by interfering with the function and morphology of bacterial cells.