Articles Tagged with ''artificial intelligence''

AI-enhanced biosurveillance, integrated sensor networks, and intelligent analytics have been framed as critical enablers of safer, more efficient food systems. At the same time, the field has begun to acknowledge that the same tools that drive efficiency and predictive power can also be turned against the food system itself. 

SnackPro, which helps users identify potential allergens in foods through image analysis, was developed by the father of a child with severe food allergies.

Peak Automation Intelligence by Peak Technologies uses machine vision, smart optics, mobile computing, and unified analytics to provide an automated view of pallets and packages, material movement, and dock workflow.

This article examines the need to always engage subject matter experts in the analysis of AI results for food safety in the context of biosurveillance and cognitive security.

Garlic carries a distinct bacterial signature reflective of the soil in which it was grown, enabling geographic identification based on microbial composition. A novel method using microbiome data and AI analysis potentially offers a low-cost authentication technique.

The low-cost approach enables simultaneous detection of multiple foodborne pathogens and spoilage microorganisms in a shorter timeframe than traditional detection methods, without requiring advanced technical training. 

TraceMap supports national authorities in identifying food safety threats and improving EU-wide coordinated response. A pilot version of TraceMap was recently used to support the investigation of globally distributed cereulide-contaminated infant formula.

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.

The extended agreement between FDA’s Human Foods Program and Simulations Plus allows scientists to continue research involving computational models to support chemical safety assessments for food and food-contact substances.

The detection system was able to detect the presence of foodborne pathogens in complex food matrices as little as three hours with 0 percent false positives, 94 percent recall, and 100 percent precision.