Application of electronic nose for feed safety and animal nutrition

Cereals in general, and maize in particular, is one of the most important food and feed commodities among cereal crops. However, maize diffusion, popularity, and, most of all, safety for consumption are threatened by mycotoxin contamination. Maize is often infected by mycotoxigenic fungi. Furthermore, global surveys indicate that more than 70% of the samples of feed and raw feed materials are positive for at least more than one mycotoxin, emphasizing co-contamination as a big issue for both food safety and public health. The co-occurrence of mycotoxins in food and feed is explained by three reasons: (i) most fungi are able to simultaneously produce several mycotoxins; (ii) commodities can be contaminated by several fungi simultaneously or in quick succession; and (iii) the complete diet comprises different commodities. Multi-mycotoxin contamination, however, is a topic of great concern, as co-contaminated samples might still exert adverse effects on animals due to additive/synergistic interactions of the mycotoxins. This scenario underlines the importance of multi-mycotoxin analysis methods that can also be rapid and user-friendly. Rapid methods for the determination of mycotoxins in cereals are hence highly needed in order to prevent the entry of mycotoxins into food and feed chains. In this scenario, Electronic noses (E-nose) may represent a promising analytical tool for industry, also in combination with conventional rapid methods already widely used by the feed industry. An e-nose consists of an array of nonspecific chemical detectors that detect different volatile organic compounds (VOCs) and consequently provides a signal that can be used as a fingerprint of the specific volatile compounds in a sample. E-nose techniques for the detection of fungal infection are based on identifying specific VOCs associated with the growth of fungi on cereal grains. At the current state E-nose has shown good potential in discriminating 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.