Methods

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LGC Standards has expanded its portfolio of Dr. Ehrenstorfer PFAS testing solutions with new 13C-labeled reference materials developed for EPA Method 1633.

Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic compounds formed during cooking. A new Seoul Tech study has demonstrated the effectiveness of a streamlined analytical method to detect PAHs, which holds distinct advantages over conventional techniques.

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.

This article discusses the development of low-cost, user-friendly biosensors to improve food safety in produce.

Researchers in China have developed a new onsite rapid test, based in fluorescence RNA-targeted isothermal amplification assay (SAT) technology, that can quickly detect and identify Cronobacter species in powdered infant formula. It offers significantly greater sensitivity and much more rapid results than polymerase chain reaction (PCR), without producing false positives.

USDA-FSIS and AOAC International have signed a Memorandum of Understanding (MOU) regarding the development, validation, and recognition of methods used by FSIS labs and regulated establishments for the verification of Hazard Analysis and Critical Control Points (HACCP) -based food safety systems.

USDA Food Safety and Inspection Service (USDA-FSIS) laboratories now use an improved enrichment method for Campylobacter in poultry meat samples, which reduced enrichment incubation time by half, and shaved a day off of reporting times for results. 

A Center for Produce Safety-funded proof-of-concept study is exploring a novel, high-throughput capture and concentration method for hepatitis A virus in fruit wash water, which uses magnets and hydrogel nanoparticles. It could be added to existing FDA and ISO digital PCR assay workflows, potentially reducing false positives.

Protecting Italian honey authenticity and combatting food fraud, the Italian Standards Body’s (UNI’s) new UNI 11972 standard introduces a new analytical method for detecting honey adulteration based in Nuclear Magnetic Resonance technology.

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