Neural network models for growth of <i>Salmonella</i> serotypes in ground chicken subjected to temperature abuse during cold storage for application in <scp>HACCP</scp> and risk assessment

Oscar, Thomas P.

doi:10.1111/ijfs.13242

Cited by 14 publications

(12 citation statements)

References 11 publications

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“…Previous research showed that variation in the growth of pathogens such as Salmonella was affected by serovars (Oscar, 2017). SL-p effects on L. monocytogenes strains differed at 30 min at both temperatures, and for 24-h treatment time at 5°C as strains 008 and 2625 were more sensitive to SL-p than strain 2634, particularly at 0.08% and 0.12% concentrations of SL-p (Tables 1 and 2).…”

Section: Temperature Effects On Inactivation Of Pathogenic Bacteria Bmentioning

confidence: 86%

Reductionin Listeria monocytogenes, Salmonella enterica and Escherichia coli O157:H7 in vitro and on tomato by sophorolipid and sanitiser as affected by temperature and storage time

Olanya

Ukuku

Solaiman

et al. 2017

Int J of Food Sci Tech

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Summary Increased consumption of produce by consumers has been attributed to perceived health benefits of postharvest produce. Pathogen control is crucial because periodic occurrences and contamination of tomato and leafy greens have exacerbated food safety risks for consumers. We investigated the effects of temperatures (5 and 25 °C), storage time (30 min and 24 h) for inactivation of Listeria monocytogenes, Salmonella enterica and Escherichia coli O157:H7 by sophorolipid (SL‐p) produced fermentatively using palmitic acid as a co‐substrate at different concentrations in vitro. Reduction in pathogenic bacteria on grape tomato by SL‐p, sanitiser (Lovit) and combinations of SL‐p and sanitiser was determined. Temperature and storage time significantly (P < 0.05) affected pathogen inactivations by SL‐p as pathogen reductions were greater at 25 °C and 24 h than at 5 °C and 30 min of storage. L. monocytogenes was the most sensitive to SL‐p treatment as reductions of 5 log relative to untreated controls were attained at 0.12% of SL‐p. Significant reductions in S. enterica (1.91–3.85 logs) and E. coli O157:H7 (0.87–4.09 logs) were recorded at 2–5% of SL‐p. Lower populations of Salmonella and E. coli O157:H7 were inactivated than L. monocytogenes. On grape tomato, pathogen populations inactivated increased at higher SL‐p levels at 25 °C. Sanitiser and sanitiser + SL‐p reduced bacterial populations on tomato by 5.29–5.76 logs and 0.71–3.3.66 logs, respectively. These results imply the interactions of temperature, storage time and SL‐p significantly (P < 0.05) affected pathogen strain reductions. The combination of SL‐p with sanitiser led to synergistic effect on E. coli O157:H7, but not L. monocytogenes and S. enterica.

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Section: Temperature Effects On Inactivation Of Pathogenic Bacteria Bmentioning

confidence: 86%

Reductionin Listeria monocytogenes, Salmonella enterica and Escherichia coli O157:H7 in vitro and on tomato by sophorolipid and sanitiser as affected by temperature and storage time

Olanya

Ukuku

Solaiman

et al. 2017

Int J of Food Sci Tech

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“…For proprietary reasons, NeuralTools does not provide weights and bias values for the neural networks it produces and the current model was too complex to easily develop a stand-alone version of the model like we have done in previous studies [11,13]. Thus, users of the current model will need have access to NeuralTools to run it.…”

Section: Modelingmentioning

confidence: 99%

Modeling the Effect of Inoculum Size on the Thermal Inactivation of <i>Salmonella</i> Typhimurium to Elimination in Ground Chicken Thigh Meat

Oscar¹

2017

AJFST

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A study was undertaken to investigate and model the effect of inoculum size on the thermal inactivation of Salmonella to elimination in ground chicken by conduction heating. To develop the model, ground chicken thigh meat portions (0.76 cm 3 ) in microcentrifuge tubes were inoculated with 2.0, 3.6, or 5.2 log of a single strain of Salmonella Typhimurium followed by cooking for 0 to 10 min at 52 to 100°C in a heating block. To validate the model, the ground chicken portions were inoculated with 2.8 or 4.4 log of S. Typhimurium followed by cooking for 0 to 9 min at 55 to 97°C. An automated, whole sample enrichment, miniature most probable number (MPN) method with a lower limit of detection of one Salmonella cell per portion was used for enumeration. The MPN data were used to develop (n = 851) and validate (n = 256) a multiple layer feedforward neural network model with two hidden layers of two nodes each. Model performance was evaluated using the acceptable prediction zone (APZ) method. The proportion of residuals in an APZ (pAPZ) from -1 log (fail-safe) to 0.5 log (fail-dangerous) was 0.945 (804/851) for dependent data and 0.945 (242/256) for independent data for interpolation. A pAPZ ≥ 0.7 indicated that model predictions had acceptable bias and accuracy. Thus, the model was successfully validated. The time for elimination of Salmonella at 58°C was 5.6, 7.1, and 8.7 min for inoculum sizes of 2.0, 3.6 and 5.2 log per portion, respectively. This relationship was observed for all cooking temperatures and among all inoculum sizes investigated indicating that inoculum size was an important independent variable to include in the model.

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“…The MPN method, as shown in this and previous studies (Oscar, ,c) with Salmonella , is one such method as it can enumerate all examined strains of this HBP in chicken without using antibiotics or genetic markers. In addition, because it uses a non‐selective media in the first step, it can enumerate injured and stressed cells of Salmonella in the food sample, which, as mentioned above, is not true for chromogenic media, which are specific but may contain selective agents that prevent growth of injured and stressed cells.…”

Section: Discussionmentioning

confidence: 99%

“…Novobiocin (25 μg mL −1 ) made the RVB more selective for Salmonella . In some of the early storage trials of the current study (results not shown) and in contrast with previous studies (Oscar, , ) with ground chicken with native microflora, growth of competitors that interfered with enumeration on selective agar (i.e. XLT4) was observed.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a neural network model for predicting growth of Salmonella Newport on diced Roma tomatoes during simulated salad preparation and serving: extrapolation to other serotypes

Oscar

2018

Int J of Food Sci Tech

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Summary A study was undertaken to model growth of Salmonella on tomatoes for developing and validating a predictive model for use in risk assessment. Cylindrical portions (0.14 g) of Roma tomato pulp were inoculated with a low dose (0.89 log MPN) of Salmonella Newport. The inoculated tomato portions were incubated for 0–8 h at 16–40 °C in 2 °C increments to obtain most probable number (MPN) data for model development and validation. A multiple‐layer feedforward neural network model with two hidden layers of two nodes each was developed. The proportion of residuals in an acceptable prediction zone (pAPZ) from −1 (fail‐safe) to 0.5 log (fail‐dangerous) was 0.93 (194/209) for dependent data and 0.96 (86/90) for independent data for interpolation. A pAPZ ≥0.7 indicated that the model provided acceptable predictions. Thus, the model was successfully validated. It was also validated for extrapolation to seven other Salmonella serotypes.

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Neural network models for growth of Salmonella serotypes in ground chicken subjected to temperature abuse during cold storage for application in HACCP and risk assessment

Cited by 14 publications

References 11 publications

Reductionin Listeria monocytogenes, Salmonella enterica and Escherichia coli O157:H7 in vitro and on tomato by sophorolipid and sanitiser as affected by temperature and storage time

Reductionin Listeria monocytogenes, Salmonella enterica and Escherichia coli O157:H7 in vitro and on tomato by sophorolipid and sanitiser as affected by temperature and storage time

Modeling the Effect of Inoculum Size on the Thermal Inactivation of <i>Salmonella</i> Typhimurium to Elimination in Ground Chicken Thigh Meat

Development and validation of a neural network model for predicting growth of Salmonella Newport on diced Roma tomatoes during simulated salad preparation and serving: extrapolation to other serotypes

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