Items Tagged with 'machine learning'

The researchers positioned the machine learning model as a low-cost complement to traditional testing workflows, helping dairy processors enhance food safety while targeting laboratory resources.

A study used machine learning to analyze WGS data for Salmonella isolates from ten European nations. The findings reveal that poultry and pigs remain the dominant sources of human salmonellosis. Notable cross-border transmission underscores the need for internationally harmonized control strategies.

A new study led by the University of South Australia offers a promising real-time mycotoxin detection method for the food industry that is based in artificial intelligence (AI) technology, and overcomes some of the limitations of traditional detection methods.

Using an artificial intelligence (AI) model to standardize and analyze a massive, global set of whole genome sequencing (WGS) data for Cronobacter sakazakii, University of Maryland researchers have discovered genetic traits that may explain the pathogen’s persistence and virulence in low-moisture foods like powdered infant formula. 

In the study, researchers analyzed the entire genome of over 1,600 Listeria strains. These DNA profiles were used to train a machine learning model that learned to identify genetic patterns associated with resistance to disinfectants commonly used in the food industry. 

Using EU Rapid Alert System for Food and Feed (RASFF) data, researchers have developed an integrated artificial intelligence (AI) framework for conducting food safety risk assessments, and demonstrated its usefulness in decreasing the ambiguity of risk management decisions.

Researchers from CDC, FDA, and USDA trained an artificial intelligence (AI) machine learning model to conduct food source attribution for human cases of salmonellosis by analyzing whole genome sequencing (WGS) data for Salmonella isolates. The model showed promise, estimating that the majority of salmonellosis cases are caused by chicken and vegetables

A recent publication from the Food and Agriculture Organization of the United Nations (FAO) has provided an in-depth review of early warning systems for food safety risks, an explanation of available open access tools, and the potential applications of Big Data and artificial intelligence (AI) in the field.

The advent of artificial technologies like electronic noses, electronic eyes, and electronic tongues has fundamentally reshaped our approach to evaluating food samples, as underscored by their ability to capture intricate sensory attributes. The symbiotic relationship between artificial instruments and machine learning underscores their potential to reshape the food industry, ensuring that the food we relish is not only delectable but also safe and of the highest quality.

The use of artificial intelligence (AI) tools like big data and machine learning for antimicrobial resistance (AMR) surveillance in livestock production shows promise for informing AMR mitigation efforts, according to a recent study led by University of Nottingham researchers.