AIMC Topic: Food Safety

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An ultra-sensitive, intelligent platform for food safety monitoring: Label-free detection of illegal additives using self-assembled SERS substrates and machine learning.

Food chemistry
To overcome the limitations of SERS in food safety monitoring, particularly significant interference from citrate ions, this study introduces an intelligent SERS-based platform for food safety monitoring. The platform utilizes sodium borohydride to a...

Initializing a Public Repository for Hosting Benchmark Datasets to Facilitate Machine Learning Model Development in Food Safety.

Journal of food protection
While there is clear potential for artificial intelligence (AI) and machine learning (ML) models to help improve food safety, the development and deployment of these models in the food safety domain are by and large lacking. The absence of publicly a...

Research on Risk Prediction of Condiments Based on Gray Correlation Analysis - Deep Neural Networks.

Journal of food protection
Food safety is directly related to the health of the public, and the safety of condiments is also of great significance. In this study, a risk assessment model for condiments based on gray correlation analysis was established by using publicly availa...

Recommendations for the Development of Artificial Intelligence Applications for the Retail Level.

Journal of food protection
Some of the early applications of artificial intelligence (AI) for food safety appear to be intended for use at the level of manufacturing and distribution. Artificial intelligence applications to facilitate foodborne illness outbreak investigations,...

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...

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-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 ...

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...

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...