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The rise of antimicrobial resistance in foodborne pathogens poses a significant risk to public health.
New and emerging pathogens, such as norovirus and hepatitis A, can contaminate food and cause illness.
Foodborne illness outbreaks can occur due to contamination of food products, poor handling, and inadequate cooking.
Chemical contaminants, such as pesticides, heavy metals, and industrial pollutants, can contaminate food and pose health risks.
Food allergens, such as peanuts, tree nuts, and shellfish, can cause severe reactions in some individuals.
Some food additives, such as artificial colors and preservatives, can pose health risks.
Foreign objects, such as glass, metal, and plastic, can contaminate food and cause injury.
Contamination can occur during processing, packaging, and storage.
Globalization and trade can increase the risk of food safety hazards, as food products are often sourced from multiple countries.
Climate change can impact food safety by altering the prevalence and distribution of foodborne pathogens.
New food technologies, such as gene editing and nanotechnology, can pose unknown risks to food safety.
Predictive analytics can be used to identify trends and patterns in food safety data, enabling proactive risk assessment and mitigation.
Risk assessment models can be used to predict the likelihood and impact of food safety hazards, enabling targeted interventions.
Machine learning algorithms can be used to analyze large datasets and identify potential food safety risks.
By understanding these key food safety hazards and risks predictions, food manufacturers, regulators, and consumers can take proactive steps to mitigate risks and ensure a safer food supply.
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