AIMC Topic: Food Safety

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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...

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...

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...

Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting.

Analytical chemistry
Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise...

An ensemble of AHP-EW and AE-RNN for food safety risk early warning.

PloS one
Food safety problems are becoming increasingly severe in modern society, and establishing an accurate food safety risk warning and analysis model is of positive significance in avoiding food safety accidents. We propose an algorithmic framework that ...

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

Regulatory toxicology and pharmacology : RTP
In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new ...

How Can AI Help Improve Food Safety?

Annual review of food science and technology
With advances in artificial intelligence (AI) technologies, the development and implementation of digital food systems are becoming increasingly possible. There is tremendous interest in using different AI applications, such as machine learning model...

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging.

Applied and environmental microbiology
In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization,...

Neural network in food analytics.

Critical reviews in food science and nutrition
Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpo...

Nondestructive and multiplex differentiation of pathogenic microorganisms from spoilage microflora on seafood using paper chromogenic array and neural network.

Food research international (Ottawa, Ont.)
Non-destructive detection of human foodborne pathogens is critical to ensuring food safety and public health. Here, we report a new method using a paper chromogenic array coupled with a machine learning neural network (PCA-NN) to detect viable pathog...