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Enregistrement W7018225966

Development of a Novel Egg Surface Decontamination Method via Electro-nano-spray

2023· dissertation· en· W7018225966 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Langueen
DomaineEngineering
ThématiqueElectrohydrodynamics and Fluid Dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHuman decontaminationHydrogen peroxideWater disinfectionMicroorganismEnvironmentally friendlyContaminationSurface water
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Chicken eggs and their products are a widely consumed and important source of nutrients for people worldwide. However, they can also be vehicles for pathogens like Salmonella and Escherichia coli (E. coli) that can cause foodborne illnesses. Commercially processed eggs in North America are typically washed with hot water and a chemical solution to decontaminate the surface of eggshells. Although the washing process is effective, this approach also removes the egg cuticle, which acts as a natural barrier to bacterial intrusion. In addition, the use of large amounts of water and washing chemicals produces significant amounts of chemically contaminated wastewater, making this approach environmentally unsustainable. Therefore, exploring alternative methods and innovative technologies that are both effective in preserving food and environmentally friendly would be important to the egg industry. Recently, a new and innovative technique based on nanotechnology called Engineered Water Nanostructures (EWNS) has been developed as a chemical-free solution for disinfection processes. EWNS are formed by electrospraying and ionizing water to create highly charged nanoscale water droplets that possess unique physicochemical properties. It means EWNS are electron-rich water shells that contain a variety of reactive oxygen species (ROS) including hydroxyl radicals, superoxide, and hydrogen peroxide generated during the electrospray process which has been proven to effectively deactivate bacteria. Researchers have explored the effectiveness of EWNS against food-related microorganisms on the surface of various fruits and vegetables. The consumption of eggs is common in Canada, with an average person consuming about 242 eggs per year. However, it has not yet been tested whether EWNS could effectively decontaminate egg surfaces, which could potentially serve as an alternative disinfection method in the egg industry. To evaluate the effectiveness of EWNS on eggshell decontamination, this research project was conducted in three phases. In Phase 1, an electro-nano-spray system was developed to generate EWNS, and lab-based experiments were conducted to assess the effectiveness of the process against E. coli inoculated on the eggshell surface. The parameters investigated included exposure time, water flow rate, and electric field strength to identify the most optimal operating conditions for the EWNS system. In Phase 2, the efficacy of the EWNS method to inactivate Salmonella on the egg surface was investigated under the optimal operating conditions established in Phase 1. In Phase 3, the impact of the EWNS technique on the quality attributes of treated eggs was evaluated and compared to washed and fresh eggs. Egg quality was measured based on physical properties such as eggshell specific gravity, eggshell thickness, albumen and yolk pH, yolk index, Haugh unit, and moisture content of albumen and yolk, as well as chemical components such as the main proteins of albumen. The results of the study showed that in 5 minutes of exposure time, the optimal EWNS operating conditions that produced the highest inactivation efficiency for E. coli inoculated on the egg surface included a water flow rate of 1 μL/min/needle (total flow rate of 16 μL/min), and an electric field strength of 9.0 kV/cm (-4.5 kV at 0.5 cm distance). At these conditions, the system achieved the inactivation efficiency of 97.6% for Escherichia coli W3110 with a 1.64 log reduction and 80.4% for Salmonella enterica serovar Enteritidis with a 0.71 log reduction. Statistical analyses of the physical characteristics of treated eggs showed that there was no significant difference in the properties compared to unwashed and washed eggs one week after treatment (20 eggs per group). Moreover, the physical characteristics of different egg groups (3 eggs per group), including unwashed, washed, and treated eggs, were analyzed over a 21-day storage period, and it was found that the quality of all groups decreased over time. However, there was no significant difference in physical properties between the EWNS-treated eggs and the control (unwashed and washed eggs). The intensity of protein bands of SDS-PAGE gel images were analyzed statistically, and the results indicated that there was no significant variation in protein features between the three sets of eggs (3 eggs per group). The research has demonstrated that the EWNS system can be a promising and environmentally friendly method for decontaminating eggshell surfaces, and may be a suitable substitute for traditional egg sanitation methods. However, the study was limited in scale, and further investigations are required to how the EWNS system can be applied for larger-scale commercial applications.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,005
Tête enseignante GPT0,178
Écart entre enseignants0,173 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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