E-Nose Technology for Mycotoxin Detection in Feed: Ready for a Real Context in Field Application or Still an Emerging Technology?

Mycotoxin risk in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. Mycotoxin contamination in feed and food is unavoidable and unpredictable. Therefore, monitoring and control are the critical points. Effective and rapid methods for mycotoxin detection, at the levels set by the regulations, are needed for an efficient mycotoxin management. This review provides an overview of the use of the electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of the mycotoxin risk at feed business level. E-nose has a high discrimination accuracy between non-contaminated and single-mycotoxin-contaminated grain. However, the predictive accuracy of e-nose is still limited and unsuitable for in-field application, where mycotoxin co-contamination occurs. Further research needs to be focused on the sensor materials, data analysis, pattern recognition systems, and a better understanding of the needs of the feed industry for a safety and quality management of the feed supply chain. A universal e-nose for mycotoxin detection is not realistic; a unique e-nose must be designed for each specific application. Robust and suitable e-nose method and advancements in signal processing algorithms must be validated for specific needs.

Automated summary

The risk of mycotoxin contamination in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. This review discusses the use of electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of mycotoxin risk at the feed business level. While e-nose has high discrimination accuracy for single-mycotoxin-contaminated grain, its predictive accuracy is still limited for in-field application where mycotoxin co-contamination occurs. Further research is needed to improve sensor materials, data analysis, pattern recognition systems, and understand the needs of the feed industry for safety and quality management.