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Food Safety

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Frontiers of machine learning in smart food safety.

Advances in food and nutrition research
Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the c...

Machine learning-enabled colorimetric sensors for foodborne pathogen detection.

Advances in food and nutrition research
In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for ...

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays.

Advances in food and nutrition research
Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple c...

Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning.

Science progress
Contaminated fresh produce remains a prominent catalyst for food-borne illnesses, prompting the need for swift and precise pathogen detection to mitigate health risks. This paper introduces an innovative strategy for identifying food-borne pathogens ...

Classification and Prediction of Food Safety Policy Tools in China Based on Machine Learning.

Journal of food protection
Governments use policy interventions to mitigate food safety risks. Despite its crucial role, empirical studies evaluating the effectiveness of China's food safety policy tools are scarce. Drawing on a dataset encompassing 11,236 food safety policy t...

Random forest models of food safety behavior during the COVID-19 pandemic.

International journal of environmental health research
Machine learning approaches are increasingly being adopted as data analysis tools in scientific behavioral predictions. This paper utilizes a machine learning approach, Random Forest Model, to determine the top prediction variables of food safety beh...

Preface to the special issue of Food and Chemical Toxicology on "New approach methodologies and machine learning in food safety and chemical risk assessment: Development of reproducible, open-source, and user-friendly tools for exposure, toxicokinetic, and toxicity assessments in the 21st century".

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
This Special Issue contains articles on applications of various new approach methodologies (NAMs) in the field of toxicology and risk assessment. These NAMs include in vitro high-throughput screening, quantitative structure-activity relationship (QSA...

Application of machine vision in food computing: A review.

Food chemistry
With global intelligence advancing and the awareness of sustainable development growing, artificial intelligence technology is increasingly being applied to the food industry. This paper, grounded in practical application cases, reviews the current r...

Progress of machine learning-based biosensors for the monitoring of food safety: A review.

Biosensors & bioelectronics
Rapid urbanization and growing food demand caused people to be concerned about food safety. Biosensors have gained considerable attention for assessing food safety due to selectivity, and sensitivity but poor stability inherently limits their applica...

Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain.

Scientific reports
The management of a food supply chain is difficult and complex because of the product's short shelf-life, time-sensitivity, and perishable nature which must be carefully considered to minimize food waste. Temperature-controlled perishable food supply...