Sniffing out Rhyzopertha dominica (Coleoptera: Bostrichidae) infestation in wheat by leveraging the latest electronic nose technology

Annually, about 1.3 billion tonnes of food is lost globally during postharvest operations. Stored product insects contribute to this loss. One of the most destructive cosmopolitian pests to stored wheat is the lesser grain borer, Rhyzopertha dominica (Coleoptera: Bostrichidae). Larvae develop and feed inside the kernels, thereby reducing nutritional content. Early detection for managing infestations is vital; however, it is often challenging to locate them in extensive food facilities and warehouses. Electronic noses may be able to identify odors associated with insects and could lead to earlier detection of infestations. These instruments contain multiple sensors that take advantage of machine learning and pattern recognition software to recognize volatile profiles. Here, we investigated the state-of-the-art electronic nose, MSEM-160 (Sensigent, Baldwin Park, U.S.A.), and its ability to detect R. dominica in wheat. Rhyzopertha dominica was reared on hard red winter wheat tempered to 13% for eight weeks. One-hundred-gram wheat samples at ratios of 1:1, 4:1, 19:1, and 99:1 of clean:infested grain were tested with supervised and unsupervised machine learning analyses, like principal component analysis and support vector machine analysis, to differentiate the infestation levels, and to identify the sensors responsible for discerning the R. dominica infestations in wheat. We found that only a handful of sensors were needed for the electronic nose to reliably recognize infestations by R. dominica. This work has clear implications in improving pest management throughout the supply chain by detecting cryptic infestations of R. dominica in stored wheat and additional stored product pests in other commodities.