Developing quantitative tools for predicting foodborne pathogen behaviour in paneer
Notice bibliographique
Résumé
[Graphical Abstract]<br>There are 100 million cases of foodborne disease (FBD) in India each year, with 150-170 million cases projected by 2030. Among all food sectors, dairy is the largest contributor to FBD. These findings prompted Indian authorities to implement large-scale public health measures, including increased surveillance, epidemiological investigations of outbreaks, and a shift towards risk-based food safety management and policy. Based on this context, this PhD project was designed to develop quantitative tools to aid in food processing and food safety management of a highly consumed Indian dairy product, paneer, a cheese of growing global significance.<br>Paneer is a fresh, soft, ready-to-eat cheese produced by curdling heat-treated milk with citric acid or vinegar, followed by pressing curds to form the final product. The global paneer market was estimated at US$ 8.7 billion in 2021. High moisture content (50-60%), mild pH (5.5 to 6.0), low organic acid levels, and lack of protective cultures make paneer highly susceptible to growth of foodborne pathogens, including <i>Escherichia coli, Salmonella spp.,</i> <i>Listeria monocytogenes, B. cereus,</i> and <i>Staphylococcus aureus. </i>This is evidenced by epidemiological reports of pathogen contamination in India, as well as product recalls in New Zealand, Australia, Canada, and the USA due to <i>L. monocytogenes</i> contamination. However, there is very limited information on the behaviour and growth kinetics of pathogens in paneer, which challenges development of quantitative risk management tools. Thus, this thesis addressed this knowledge gap by studying growth kinetics of selected foodborne pathogens in paneer, resulting in predictive models that can help support food safety management plans and risk assessment<br>The study organisms for this thesis, <i>L. monocytogenes</i> and <i>B. cereus</i>, are more commonly associated with contaminated cheese products, and have been isolated from paneer. <i>L. monocytogenes</i> is a biofilm-forming, psychotropic, ubiquitous pathogen; <i>B. cereus</i>, also ubiquitous in the environment, forms endospores that render it resistant to heat-treatment processes. The first two experimental chapters of the thesis investigate growth kinetics of <i>L. monocytogenes</i> and <i>B. cereus</i> in freshly prepared paneer. In these studies, freshly prepared paneer was inoculated with a cocktail of <i>L. monocytogenes</i> and <i>B. cereus </i>strains, separately, and incubated at a range of temperature (4–40 °C for <i>L. monocytogenes</i> and 10-50°C for <i>B. cereus</i>). Growth kinetics were then modelled as a function of temperature.<br>In the first study, <i>L. monocytogenes</i> growth was modelled using a standard two-step parameter estimation, including the Baranyi and Roberts model primary model and the extended Ratkowsky square root model for secondary models. Next, <i>B. cereus</i> group III and IV strains, isolated from dairy farm environments, were selected based on screening for enterotoxin production in paneer. In contrast to <i>L. monocytogenes</i> growth studies, a one-step model was fitted to data, combining bootstrap re-sampling to generate confidence intervals. The Baranyi and Roberts primary model was combined with a cardinal parameter secondary model to form a global model, to which the <i>B. cereus</i> growth in paneer data was fitted. Results indicated both <i>L. monocytogenes</i> and <i>B. cereus</i> grew to hazardous levels in paneer over the entire experimental temperature range. The developed models were compared to relevant publicly available models (i.e., ComBase and USDA Pathogen Modelling Program) and published cheese models (only available for<i> L. monocytogenes)</i>. Bias and accuracy factors were used as indices of model performance, demonstrating the need for paneer-specific models to accurately estimate pathogen growth in paneer.<br>A probabilistic model was developed to describe effects of different time-temperature profiles along the supply chain on risk of B. cereus exposure and listeriosis infection from paneer consumption. A lack of data on paneer processing conditions and pathogen surveillance during paneer processing prevented the development of a full farm-to-fork probabilistic quantitative microbial risk assessment (QMRA) model. However, a preliminary QMRA model was built for a processing-to-table continuum, providing an initial framework and risk estimates for B. cereus contamination and listeriosis in Australia attributable to paneer consumption. Currently available experimental data, survey data, and assumptions assessed exposure level, which was divided into three modules: 1) pre-consumption storage, 2) consumption and 3) risk characterization. Due to the lack of dose-exposure data for <i>B. cereus</i>, the model only extended to module 2), with a final output of CFU/serving. However, for <i>L.</i> <i>monocytogenes</i>, the final output was listeriosis cases/100,000 population. The model was built in MS-Excel spreadsheet, using @RISK software to conduct Monte Carlo simulations and examine selected scenarios that considered different levels of pathogen contamination at retail and during storage. Sensitivity analyses demonstrated that factors such as initial contamination, home storage time and temperature and retail storage temperature were the most important input variables that affected the output of the models significantly.<br>The last chapter of the thesis describes an Excel<sup>© </sup>tool that incorporates the developed growth models, along with inactivation data of <i>L. monocytogenes</i> and <i>B. cereus</i> in milk, as well as user inputs linked to a gamma generalised linear model that provides deterministic estimates of <i>L. monocytogenes</i> and <i>B. cereus</i> during different steps of paneer processing. The tool is based on the major processes during paneer preparation and storage, along with eight modules for initial pathogen load, heating and cooling regimes, cross-contamination incidents, and final product storage. The final output module provides estimates of pathogen concentration in paneer and the time required to reach a critical concentration of 2 log<sub>10</sub> CFU/g as defined by regulatory authorities.<br>This thesis provides a greater understanding of foodborne pathogen behaviour in paneer, as well as quantitative tools to aid in food safety management and an initial framework for paneer risk assessment.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».