Predictive model for growth of <i>Salmonella</i> Newport on Romaine lettuce

Oscar, Thomas P.

doi:10.1111/jfs.12786

Cited by 8 publications

(4 citation statements)

References 34 publications

(61 reference statements)

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“…Data for natural cross‐contamination of RTE food with Salmonella from ground turkey and ground chicken were not collected and are not available in the scientific literature but could be collected in the future using WSE, qPCR, cultural isolation, and serotyping as was done in a previous study (Oscar, 2017b) with whole chicken parts (i.e., wings, breasts, thighs, and drumsticks) harvested from whole chickens sold in flow‐pack wrappers. In addition, existing models for growth of chicken isolates of Salmonella on cooked chicken (Oscar, 1999a, 1999b, 2002) or on Roma tomatoes (Oscar, 2018a) or on Romaine lettuce (Oscar, 2020) as a function of times and temperatures encountered during meal preparation can be used to forecast how Salmonella number on RTE food may change between cross‐contamination and consumption.…”

Section: Discussionmentioning

confidence: 99%

Salmonella Prevalence Alone Is Not a Good Indicator of Poultry Food Safety

Oscar

2020

Risk Analysis

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Salmonella is a leading cause of foodborne illness (i.e., salmonellosis) outbreaks, which on occasion are attributed to ground turkey. The poultry industry uses Salmonella prevalence as an indicator of food safety. However, Salmonella prevalence is only one of several factors that determine risk of salmonellosis. Consequently, a model for predicting risk of salmonellosis from individual lots of ground turkey as a function of Salmonella prevalence and other risk factors was developed. Data for Salmonella contamination (prevalence, number, and serotype) of ground turkey were collected at meal preparation. Scenario analysis was used to evaluate effects of model variables on risk of salmonellosis. Epidemiological data were used to simulate Salmonella serotype virulence in a dose‐response model that was based on human outbreak and feeding trial data. Salmonella prevalence was 26% (n = 100) per 25 g of ground turkey, whereas Salmonella number ranged from 0 to 1.603 with a median of 0.185 log per 25 g. Risk of salmonellosis (total arbitrary units (AU) per lot) was affected (p ≤ 0.05) by Salmonella prevalence, number, and virulence, by incidence and extent of undercooking, and by food consumption behavior and host resistance but was not (p > 0.05) affected by serving size, serving size distribution, or total bacterial load of ground turkey when all other risk factors were held constant. When other risk factors were not held constant, Salmonella prevalence was not correlated (r = −0.39; p = 0.21) with risk of salmonellosis. Thus, Salmonella prevalence alone was not a good indicator of poultry food safety because other factors were found to alter risk of salmonellosis. In conclusion, a more holistic approach to poultry food safety, such as the process risk model developed in the present study, is needed to better protect public health from foodborne pathogens like Salmonella.

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Section: Discussionmentioning

confidence: 99%

Salmonella Prevalence Alone Is Not a Good Indicator of Poultry Food Safety

Oscar

2020

Risk Analysis

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“…Finally, over-training can be avoided by following the criteria of the APZ method when developing and validating ANN (Oscar, 2017b(Oscar, , 2018a(Oscar, , 2018b(Oscar, , 2020a(Oscar, , 2021. Thus, there is no reason not to use ANN in predictive microbiology applications.…”

Section: Introductionmentioning

confidence: 99%

Use of ComBase data to develop an artificial neural network model for nonthermal inactivation of Campylobacter jejuni in milk and beef and evaluation of model performance and data completeness using the acceptable prediction zones method

Boleratz

Oscar

2022

Journal of Food Safety

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ComBase is a widely used microbial modeling database. ComBase data can be used to develop and validate models and to test novel modeling methods like artificial neural networks (ANN) and acceptable prediction zones (APZ), which have been shown to outperform traditional methods. Here, ComBase data were used to evaluate the ANN and APZ methods for modeling nonthermal inactivation of Campylobacter jejuni in milk and beef as a function of time, temperature (À20, 1, 10, 20, 30, and 40 C), and strain (18177, ATCC 29428). Four ANN were developed using Excel and Neu-ralTools, and the best-performing was a general regression neural network (GRNN) whose performance and data completeness were evaluated using the APZ method.Relative variable impacts in the GRNN model were 42.5%, 31.5%, 20.1%, and 5.9% for time, temperature, food, and strain, respectively. Nonthermal inactivation of C. jejuni was faster and greater at ambient than at cold temperatures and in milk than in beef except at 1 C where it was similar. The proportion of residuals in the APZ (pAPZ) ranged from 0.77 to 1 for individual nonthermal inactivation curves. Although the model had acceptable performance (pAPZ ≥0.7), it failed validation because of data gaps like one instead of four replicates per combination of independent variables and no data at À10 C. Thus, these and other data gaps identified need to be filled before the model can be used with confidence to predict behavior of C. jejuni in milk and beef. Nonetheless, results indicated that ANN and APZ methods can be used to model data for nonthermal inactivation of C. jejuni in food.

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“…Street food is also an important attraction for tourists (Henderson et al, 2012). Unfortunately, in developing countries, street food is mainly prepared and sold on streets without taking proper hygienic measures (Teffo, 2017), and consumers are at risk of bacterial contamination which can lead to gastroenteritis and other foodborne illnesses (Ahmed et al, 2017;Alimi, 2016;Atter et al, 2015;De Lima et al, 2019;Oscar, 2020). The main sources of bacterial contamination are dirty utensils, impure and substandard cooking oil, and unhygienic practices by vendors (Asiegbu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Contamination of ready-to-eat street food in Pakistan with Salmonella spp.: Implications for consumers and food safety

Raza

Asmat

Mustafa

et al. 2021

International Journal of Infectious Diseases

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Ready-to-eat (RTE) food sold in Quetta, Pakistan was assessed for microbial contamination. Methods: Equal numbers of samples were collected from four categories of RTE foodburgers, shawarma, pizza and sandwichesfrom January 2018 to December 2018. Microbial contamination of individual food samples was assessed by quantifying the total aerobic count obtained from plating samples on bacterial growth medium. Salmonella spp. serovars were identified using polymerase chain reaction. Results: Approximately 38% (121/320) of RTE food samples were not fit for human consumption. The most contaminated type of RTE food was shawarma (49%). Microbial contamination of food samples was higher in summer compared with the other seasons. Approximately 40% (49/121) of food samples that were not fit for human consumption were contamined with Salmonella spp. Salmonella enteritidis (69%) and Salmonella typhimurium (31%) were the only serovars among the samples testing positive for Salmonella spp. Of the 49 samples with high microbial counts, S. enteritidis was present in 34 samples and S. typhimurium was present in 15 samples. The antibiotic sensitivity results demonstrated that both S. enteritidis and S. typhimurium were resistant to amoxicillin. In addition, S. enteritidis was resistant to chloramphenicol and erythromycin, and S. typhimurium presented high resistance to erythromycin. Both S. typhimurium and S. enteritidis were highly sensitive to kanamycin. Conclusion: RTE food sold by street vendors in Quetta was found to be contaminated with Salmonella spp. and poses a great health risk to consumers. As such, consumption should be avoided, and the health authorities should take stringent action to ensure the quality of street food in order to reduce the healthcare burden.

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Predictive model for growth of Salmonella Newport on Romaine lettuce

Cited by 8 publications

References 34 publications

Salmonella Prevalence Alone Is Not a Good Indicator of Poultry Food Safety

Salmonella Prevalence Alone Is Not a Good Indicator of Poultry Food Safety

Use of ComBase data to develop an artificial neural network model for nonthermal inactivation of Campylobacter jejuni in milk and beef and evaluation of model performance and data completeness using the acceptable prediction zones method

Contamination of ready-to-eat street food in Pakistan with Salmonella spp.: Implications for consumers and food safety

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