Machine learning to predict the relationship between Vibrio spp. concentrations in seawater and oysters and prevalent environmental conditions.

Vibrio parahaemolyticus and Vibrio vulnificus are bacteria with a significant public health impact. Identifying factors impacting their presence and concentrations in food sources could enable the identification of significant risk factors and prevent incidences of foodborne illness. In recent years, machine learning has shown promise in modeling microbial presence based on prevalent external and internal variables, such as environmental variables and gene presence/absence, respectively, particularly with the generation and availability of large amounts and diverse sources of data. Such analyses can prove useful in predicting microbial behavior in food systems, particularly under the influence of the constant changes in environmental variables. In this study, we tested the efficacy of six machine learning regression models (random forest, support vector machine, elastic net, neural network, k-nearest neighbors, and extreme gradient boosting) in predicting the relationship between environmental variables and total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater and oysters. In general, environmental variables were found to be reliable predictors of total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater, and pathogenic V. parahaemolyticus in oysters (Acceptable Prediction Zone >70 %) when analyzed using our machine learning models. SHapley Additive exPlanations, which was used to identify variables influencing Vibrio concentrations, identified chlorophyll a content, seawater salinity, seawater temperature, and turbidity as influential variables. It is important to note that different strains were differentially impacted by the same environmental variable, indicating the need for further research to study the causes and potential mechanisms of these variations. In conclusion, environmental variables could be important predictors of Vibrio growth and behavior in seafood. Moreover, the models developed in this study could prove invaluable in assessing and managing the risks associated with V. parahaemolyticus and V. vulnificus, particularly in the face of a changing environment.

Research gaps and priorities for quantitative microbial risk assessment (QMRA).

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

A machine learning approach to identifying Salmonella stress response genes in isolates from poultry processing.

We explored the potential of machine learning to identify significant genes associated with Salmonella stress response during poultry processing using whole genome sequencing (WGS) data. The Salmonella isolates (n = 177) used in this study were obtained from various chicken sources (skin before chiller, chicken carcass before chiller, frozen chicken, and post-chill chicken carcass). Six machine learning algorithms (random forest, neural network, cost-sensitive learning, logit boost, and support vector machine linear and radial kernels) were trained on Salmonella WGS data, and model fit was assessed using standard evaluation metrics such as the area under the receiver operating characteristic (AUROC) curve and confusion matrix statistics. All models achieved high performances based on the AUROC metric, with logit boost showing the best performance with an AUROC score of 0.904, sensitivity of 0.889, and specificity of 0.920. The significant genes identified included ybtX, which encodes a Yersiniabactin-associated zinc transporter, and the transferase-encoding genes yccK and thiS. Additionally, genes coding for cold (cspA, cspD, and cspE) and heat shock (rpoH and rpoE) responses were identified. Other significant genes included those involved in lipopolysaccharide biosynthesis (irp1, waaD, rfc, and rfbX), DNA repair and replication (traI), biofilm formation (ccdA and fyuA), and cellular metabolism (irtA).

Linking microbial contamination to food spoilage and food waste: the role of smart packaging, spoilage risk assessments, and date labeling.

Ensuring a safe and adequate food supply is a cornerstone of human health and food security. However, a significant portion of the food produced for human consumption is wasted annually on a global scale. Reducing harvest and postharvest food waste, waste during food processing, as well as food waste at the consumer level, have been key objectives of improving and maintaining sustainability. These issues can range from damage during processing, handling, and transport, to the use of inappropriate or outdated systems, and storage and packaging-related issues. Microbial growth and (cross)contamination during harvest, processing, and packaging, which causes spoilage and safety issues in both fresh and packaged foods, is an overarching issue contributing to food waste. Microbial causes of food spoilage are typically bacterial or fungal in nature and can impact fresh, processed, and packaged foods. Moreover, spoilage can be influenced by the intrinsic factors of the food (water activity, pH), initial load of the microorganism and its interaction with the surrounding microflora, and external factors such as temperature abuse and food acidity, among others. Considering this multifaceted nature of the food system and the factors driving microbial spoilage, there is an immediate need for the use of novel approaches to predict and potentially prevent the occurrence of such spoilage to minimize food waste at the harvest, post-harvest, processing, and consumer levels. Quantitative microbial spoilage risk assessment (QMSRA) is a predictive framework that analyzes information on microbial behavior under the various conditions encountered within the food ecosystem, while employing a probabilistic approach to account for uncertainty and variability. Widespread adoption of the QMSRA approach could help in predicting and preventing the occurrence of spoilage along the food chain. Alternatively, the use of advanced packaging technologies would serve as a direct prevention strategy, potentially minimizing (cross)contamination and assuring the safe handling of foods, in order to reduce food waste at the post-harvest and retail stages. Finally, increasing transparency and consumer knowledge regarding food date labels, which typically are indicators of food quality rather than food safety, could also contribute to reduced food waste at the consumer level. The objective of this review is to highlight the impact of microbial spoilage and (cross)contamination events on food loss and waste. The review also discusses some novel methods to mitigate food spoilage and food loss and waste, and ensure the quality and safety of our food supply.

Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends.

Several studies have shown a correlation between outbreaks of Salmonella enterica and meteorological trends, especially related to temperature and precipitation. Additionally, current studies based on outbreaks are performed on data for the species Salmonella enterica, without considering its intra-species and genetic heterogeneity. In this study, we analyzed the effect of differential gene expression and a suite of meteorological factors on salmonellosis outbreak scale (typified by case numbers) using a combination of machine learning and count-based modeling methods. Elastic Net regularization model was used to identify significant genes from a Salmonella pan-genome, and a multi-variable Poisson regression developed to fit the individual and mixed effects data. The best-fit Elastic Net model (α = 0.50; λ = 2.18) identified 53 significant gene features. The final multi-variable Poisson regression model (χ2 = 5748.22; pseudo R2 = 0.669; probability > χ2 = 0) identified 127 significant predictor terms (p < 0.10), comprising 45 gene-only predictors, average temperature, average precipitation, and average snowfall, and 79 gene-meteorological interaction terms. The significant genes ranged in functionality from cellular signaling and transport, virulence, metabolism, and stress response, and included gene variables not considered as significant by the baseline model. This study presents a holistic approach towards evaluating multiple data sources (such as genomic and environmental data) to predict outbreak scale, which could help in revising the estimates for human health risk.

Development of a novel machine learning-based weighted modeling approach to incorporate Salmonella enterica heterogeneity on a genetic scale in a dose-response modeling framework.

Estimating microbial dose-response is an important aspect of a food safety risk assessment. In recent years, there has been considerable interest to advance these models with potential incorporation of gene expression data. The aim of this study was to develop a novel machine learning model that considers the weights of expression of Salmonella genes that could be associated with illness, given exposure, in hosts. Here, an elastic net-based weighted Poisson regression method was proposed to identify Salmonella enterica genes that could be significantly associated with the illness response, irrespective of serovar. The best-fit elastic net model was obtained by 10-fold cross-validation. The best-fit elastic net model identified 33 gene expression-dose interaction terms that added to the predictability of the model. Of these, nine genes associated with Salmonella metabolism and virulence were found to be significant by the best-fit Poisson regression model (p < 0.05). This method could improve or redefine dose-response relationships for illness from relative proportions of significant genes from a microbial genetic dataset, which would help in refining endpoint and risk estimations.

A Machine Learning Model for Food Source Attribution of Listeria monocytogenes.

Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.

Exploring the predictive capability of advanced machine learning in identifying severe disease phenotype in Salmonella enterica.

The past few years have seen a significant increase in availability of whole genome sequencing information, allowing for its incorporation in predictive modeling for foodborne pathogens to account for inter- and intra-species differences in their virulence. However, this is hindered by the inability of traditional statistical methods to analyze such large amounts of data compared to the number of observations/isolates. In this study, we have explored the applicability of machine learning (ML) models to predict the disease outcome, while identifying features that exert a significant effect on the prediction. This study was conducted on Salmonella enterica, a major foodborne pathogen with considerable inter- and intra-serovar variation. WGS of isolates obtained from various sources (i.e., human, chicken, and swine) were used as input in four machine learning models (logistic regression with ridge, random forest, support vector machine, and AdaBoost) to classify isolates based on disease severity (extraintestinal vs. gastrointestinal) in the host. The predictive performances of all models were tested with and without Elastic Net regularization to combat dimensionality issues. Elastic Net-regularized logistic regression model showed the best area under the receiver operating characteristic curve (AUC-ROC; 0.86) and outcome prediction accuracy (0.76). Additionally, genes coding for transcriptional regulation, acidic, oxidative, and anaerobic stress response, and antibiotic resistance were found to be significant predictors of disease severity. These genes, which were significantly associated with each outcome, could possibly be input in amended, gene-expression-specific predictive models to estimate virulence pattern-specific effect of Salmonella and other foodborne pathogens on human health.

Evaluating uncertainty and variability associated with Toxoplasma gondii survival during cooking and low temperature storage of fresh cut meats.

Toxoplasmosis is an infection caused by the protozoan parasite, Toxoplasma gondii. It has been reported as the fourth leading cause of hospitalization and second leading cause of death among 31 major foodborne pathogens in the United States. Humans are infected through consumption of raw or undercooked meat containing T. gondii tissue cysts or ingestion of food, soil, or water contaminated by T. gondii oocysts. People often lack knowledge about how to prevent T. gondii infection, especially the risks associated with eating or handling raw or undercooked meat. Current available data on cooking or low temperature storage for whole cuts of meat are not sufficient to validate inactivation of T. gondii. The objectives of the present study were to estimate the relationship of time and temperature with the survival rate of T. gondii during cooking and low temperature storage of fresh cut meats. We used different statistical sampling techniques such as bootstrap resampling and Gibbs sampling to establish those relationships. Monte Carlo simulation was used to estimate the safe temperature for cooking and storing meats. The results showed no detection of T. gondii in fresh meats when the internal temperature reached above 64 °C (147.2 °F) and below -18 °C (0 °F). The tissue cysts can remain viable at least up to 30 days at 4 °C (39 °F) and about 3.3% cysts survived at 62.8 °C (145 °F). This study can provide helpful information in improving the risk models to further mitigate the public health burden of toxoplasmosis.

Evaluation of public health risk for Escherichia coli O157:H7 in cilantro.

This study sought to model the growth and die-off of Escherichia coli (E. coli) O157:H7 along the cilantro supply chain from farm-to-fork to investigate its risk to public health. Contributing factors included in the model were on farm contamination from irrigation water and soil, solar radiation, harvesting, and transportation and storage times and temperatures. The developed risk model estimated the microbiological risks associated with E. coli O157:H7 in cilantro and determined the parameters that had the most effect on the estimated number of illnesses per year so future mitigation strategies could be applied. Results showed a similar decrease in the E. coli O157:H7 (median values) concentrations along the supply chain for cilantro grown in both winter and summer weather conditions. With an estimated 0.1% prevalence of E. coli O157:H7 contamination for cilantro post-harvest used for illustration, the model predicted the probability of illness from consuming fresh cilantro as very low with fewer than two illnesses per every one billion servings of cilantro (1.6 × 10-9; 95th percentile). Although rare, 3.7% and 1.6% of scenarios run in this model for summer and winter grown cilantro, respectively, result in over 10 cases per year in the United States. This is reflected in real life where illnesses from cilantro are seen rarely but outbreaks have occurred. Sensitivity analysis and scenario testing demonstrated that ensuring clean and high quality irrigation water and preventing temperature abuse during transportation from farm to retail, are key to reducing overall risk of illness.

Distribution of Toxoplasma gondii Tissue Cysts in Shoulder Muscles of Naturally Infected Goats and Lambs.

ABSTRACT: Toxoplasmosis has been recognized as a major public health problem worldwide. The consumption of uncooked or undercooked meat infected with Toxoplasma gondii tissue cysts is one of the main means of transmission of this parasite. Although sheep, goats, and pigs are commonly infected with T. gondii, little information is available on the distribution of T. gondii tissue cysts in naturally infected meat. In this study, we investigated the distribution of viable T. gondii tissue cysts in shoulder muscles of naturally infected lambs and goats. Hearts and shoulders of 46 lambs and 39 goats from a local grocery store were tested for T. gondii infection. Animals were evaluated for the presence of anti-T. gondii antibodies in heart blood and clots by the modified agglutination test. Fourteen of the 85 animals (seven lambs and seven goats) were seropositive. Six to 12 samples weighing 5, 10, and 50 g were obtained from shoulder muscles of each seropositive animal and used for bioassay in mice. The distribution of viable T. gondii differed according to the size of the sample analyzed, but in general larger sample sizes resulted in higher isolation rates (P < 0.05). Results of the study revealed an uneven distribution of T. gondii in muscle samples of lambs and goats and that T. gondii can be transmitted by consumption of very small servings (5 and 10 g) of meat when it is consumed raw or is undercooked.

Low prevalence of viable Toxoplasma gondii in fresh, unfrozen American pasture-raised pork and lamb from retail meat stores in the United States.

In a national survey of fresh, unfrozen, American pasture-raised lamb and pork, the prevalence of viable Toxoplasma gondii was determined in 1500 samples selected by random multistage sampling (750 pork, 750 lamb) obtained from 250 retail meat stores from 10 major geographic areas in the USA. Each sample consisted of a minimum of 500g of meat purchased from the retail meat case. To detect viable T. gondii, 50g meat samples of each of 1500 samples were bioassayed in mice. Viable T. gondii was isolated from 2 of 750 lamb samples (unweighted: 0.19%, 0.00-0.46%; weighted: 0.04%, 0.00-0.11%) and 1 of 750 pork samples (unweighted: 0.12%, 0.00-0.37%; weighted: 0.18%, 0.00-0.53%) samples. Overall, the prevalence of viable T. gondii in these retail meats was very low. Nevertheless, consumers, especially pregnant women, should be aware that they can acquire T. gondii infection from ingestion of undercooked meat. Cooking meat to an internal temperature of 66°C kills T. gondii.

Elucidating Transmission Patterns of Endemic Mycobacterium avium subsp. paratuberculosis Using Molecular Epidemiology.

Mycobacterial diseases are persistent and characterized by lengthy latent periods. Thus, epidemiological models require careful delineation of transmission routes. Understanding transmission routes will improve the quality and success of control programs. We aimed to study the infection dynamics of Mycobacterium avium subsp. paratuberculosis (MAP), the causal agent of ruminant Johne's disease, and to distinguish within-host mutation from individual transmission events in a longitudinally MAP-defined dairy herd in upstate New York. To this end, semi-annual fecal samples were obtained from a single dairy herd over the course of seven years, in addition to tissue samples from a selection of culled animals. All samples were cultured for MAP, and multi-locus short-sequence repeat (MLSSR) typing was used to determine MAP SSR types. We concluded from these precise MAP infection data that, when the tissue burden remains low, the majority of MAP infections are not detectable by routine fecal culture but will be identified when tissue culture is performed after slaughter. Additionally, we determined that in this herd vertical infection played only a minor role in MAP transmission. By means of extensive and precise longitudinal data from a single dairy herd, we have come to new insights regarding MAP co-infections and within-host evolution.

Age-Associated Distribution of Antimicrobial-Resistant Salmonella enterica and Escherichia coli Isolated from Dairy Herds in Pennsylvania, 2013-2015.

Antimicrobial resistance has become a major global public health concern, and agricultural operations are often implicated as a source of resistant bacteria. This study characterized the prevalence of antimicrobial-resistant Salmonella enterica and Escherichia coli from a total of 443 manure composite samples from preweaned calves, postweaned calves, dry cows, and lactating cows from 80 dairy operations in Pennsylvania. A total of 1095 S. enterica and 2370 E. coli isolates were screened and tested for resistance to 14 antimicrobials on the National Antimicrobial Resistance Monitoring System Gram-negative (NARMS GN) panel. Salmonellae were isolated from 67% of dairy operations, and 99% of the isolates were pan-susceptible. Salmonella were isolated more frequently from lactating and dry cow samples than from pre- and postweaned calf samples. Overall, the most prevalent serotypes were Cerro, Montevideo, Kentucky, and Newport. E. coli were isolated from all the manure composite samples, and isolates were commonly resistant to tetracyclines, sulfonamides, and aminoglycosides. Resistance was detected more frequently in the E. coli isolates from pre- and postweaned calf samples than in isolates from dry and lactating cow samples (p < 0.05). Multidrug-resistant E. coli (i.e., resistant to >3 antimicrobial classes) were isolated from 66 farms (83%) with significantly greater prevalence in preweaned calves (p < 0.05) than in the older age groups. The blaCTX-M and blaCMY genes were detected in the cephalosporin-resistant E. coli from 4% and 35% of the farms, respectively. These findings indicate that dairy animals, especially the calf population, serve as significant reservoirs for antimicrobial-resistant bacteria. Additional research on the colonization and persistence of resistant E. coli in calves is warranted to identify potential avenues for mitigation.

Dynamic Predictive Model for Growth of Bacillus cereus from Spores in Cooked Beans.

Kinetic growth data for Bacillus cereus grown from spores were collected in cooked beans under several isothermal conditions (10 to 49°C). Samples were inoculated with approximately 2 log CFU/g heat-shocked (80°C for 10 min) spores and stored at isothermal temperatures. B. cereus populations were determined at appropriate intervals by plating on mannitol-egg yolk-polymyxin agar and incubating at 30°C for 24 h. Data were fitted into Baranyi, Huang, modified Gompertz, and three-phase linear primary growth models. All four models were fitted to the experimental growth data collected at 13 to 46°C. Performances of these models were evaluated based on accuracy and bias factors, the coefficient of determination ( R2), and the root mean square error. Based on these criteria, the Baranyi model best described the growth data, followed by the Huang, modified Gompertz, and three-phase linear models. The maximum growth rates of each primary model were fitted as a function of temperature using the modified Ratkowsky model. The high R2 values (0.95 to 0.98) indicate that the modified Ratkowsky model can be used to describe the effect of temperature on the growth rates for all four primary models. The acceptable prediction zone (APZ) approach also was used for validation of the model with observed data collected during single and two-step dynamic cooling temperature protocols. When the predictions using the Baranyi model were compared with the observed data using the APZ analysis, all 24 observations for the exponential single rate cooling were within the APZ, which was set between -0.5 and 1 log CFU/g; 26 of 28 predictions for the two-step cooling profiles also were within the APZ limits. The developed dynamic model can be used to predict potential B. cereus growth from spores in beans under various temperature conditions or during extended chilling of cooked beans.

Identifying and modeling meteorological risk factors associated with pre-harvest contamination of Listeria species in a mixed produce and dairy farm.

This study sought to investigate the prevalence of Listeria species (including L. monocytogenes) in a mixed produce and dairy farm and to identify specific meteorological factors affecting Listeria spp. presence. Environmental samples were collected monthly from locations within the mixed farm over 14months and were analyzed for Listeria spp. Meteorological factors were evaluated for their association with the presence of Listeria spp. by using logistic regression (LR) and random forest (RF). The developed LR model identified wind speed and precipitation as significant risk factors (P<0.05), indicating higher wind speed at day 2 prior to sampling and higher average precipitation over the previous 25days before sampling increased the probability of isolation of Listeria spp. from the mixed farm. Results from RF revealed that average wind speed at day 2 prior to sampling and average precipitation in the previous 25days before sampling were the most important factors influencing the presence of Listeria spp., which supported the findings from LR. These findings indicate that the occurrence of Listeria spp. was influenced by wind speed and precipitation, suggesting run-off and wind-driven dust might be possible routes of pathogen transmission in mixed farms. The developed LR and RF models, with robust predictive performances as measured by the area under the receiver operating characteristic curves, can be used to predict Listeria spp. contamination risk in a mixed farm under different weather conditions and can help with the evaluation of farm management practices and the development of control strategies aimed at reducing pre-harvest microbial contamination in a mixed farming system.

Prevalence and genetic characterization of Toxoplasma gondii in free-range chickens from grocery stores and farms in Maryland, Ohio and Massachusetts, USA.

Chickens are considered important in the epidemiology of Toxoplasma gondii. Chicken hearts (n = 1185) obtained from grocery stores were tested for T. gondii infection. Antibodies to T. gondii were assayed in fluid removed from the heart cavity using the modified agglutination test (MAT) at 1:5, 1:25, and 1:100 dilutions. MAT antibodies were detected in 222 hearts at 1:5 dilution and 8 hearts at 1:25 dilution, but none were positive at 1:100 dilution. Seropositive (n = 230, 19.4%) chicken hearts were bioassayed in mice and seronegative (n = 157) chickens were bioassayed in cats. Viable T. gondii was not isolated from any hearts by bioassays in mice. The 2 cats fed 60 and 97 hearts did not excrete T. gondii oocysts. The results indicate a low prevalence of viable T. gondii in chickens from grocery stores. Molecular typing of 23 archived T. gondii strains isolated from free-range chickens from Ohio and Massachusetts using the 10 PCR-RFLP markers including SAG1, SAG2 (5'-3'SAG2 and altSAG2), SAG3, BTUB, GRA6, c22-8, c29-2, L358, PK1, and Apico revealed that seven were ToxoDB PCR-RFLP genotype #1, 11 were genotype #2, one was genotype #3, three were genotype #170, and one was mixed genotype. These results indicate that the clonal genotypes #1 (type II), #2 (type III), and #3 (type II variant) are common in free-range chickens.

Prediction of Escherichia coli O157:H7, Salmonella , and Listeria monocytogenes Growth in Leafy Greens without Temperature Control.

A recent study by the Centers for Disease Control and Prevention reported that between 1998 and 2008, leafy greens outbreaks accounted for 22.3% of foodborne outbreaks in the United States. Several studies on the growth of bacteria at different temperatures have been conducted; however, there is a need for the prediction of bacterial growth when leafy greens are transported without temperature control. Food products, when taken out of refrigeration, undergo a temperature change, with the rate of temperature change being proportional to the difference in the temperature of food and its surroundings. The objective of this study was to estimate the growth of Escherichia coli O157:H7, Salmonella enterica , and L. monocytogenes in leafy greens during transportation from retail to home at ambient temperatures ranging from 10 to 40°C for up to 10 h. Experiments were conducted to monitor the temperature increase in fresh spinach taken from refrigeration temperature to ambient temperature. The growth of pathogens was predicted using these changing temperature profiles with the three-phase linear model as a primary model and the square root model as the secondary model. The levels of E. coli O157:H7, S. enterica , and L. monocytogenes increased by 3.12, 2.43, and 3.42 log CFU at 40°C for the 10-h period, respectively, when no lag phase was assumed. If leafy greens are not kept out of refrigeration for more than 3 h, when the air temperature is 40°C or more, pathogen growth should be less than 1 log CFU. These results would assist in developing recommendations for food transportation without refrigeration.