Volatile-mediated oviposition preference for healthy over root-infested plants by the European corn borer

The selection of oviposition sites by female moths is crucial in shaping their progeny performance and survival, and consequently in determining insect fitness. Selecting suitable plants that promote the performance of the progeny is referred to as the Preference-Performance hypothesis (or 'mother-knows-best'). While root infestation generally reduces the performance of leaf herbivores, little is known about its impact on female oviposition. We investigated whether maize root infestation by the Western corn rootworm (WCR) affects the oviposition preference and larval performance of the European corn borer (ECB). ECB females used leaf volatiles to select healthy plants over WCR-infested plants. Undecane, a compound absent from the volatile bouquet of healthy plants, was the sole compound to be upregulated upon root infestation and acted as a repellent for first oviposition. ECB larvae yet performed better on plants infested below-ground than on healthy plants, suggesting an example of 'bad motherhood'. The increased ECB performance on WCR-infested plants was mirrored by an increased leaf consumption, and no changes in the plant primary or secondary metabolism were detected. Understanding plant-mediated interactions between above- and below-ground herbivores may help to predict oviposition decisions, and ultimately, to manage pest outbreaks in the field.

Unveiling aromas: Virtual reality and scent identification for sensory analysis

Sensory analysis is crucial for optimizing experiences in various fields, including food, cosmetics, and product design. Traditional methods can be inefficient and imprecise. This study introduces a novel approach by blending Virtual Reality (VR) technology with scent identification techniques. The aim is to investigate whether the visual representation of food products affects scent perception. Limited research has explored the use of VR in scent identification, which is especially relevant when altering the food environment setting. A virtual sensory laboratory was developed to mimic MATE's sensory booth. Sixty participants, all MATE students, were involved in this study. This method offers a potential means to streamline scent identification and reduce human bias in sensory analysis. In summary, the combination of VR technology and scent identification presents a fresh methodological approach to sensory analysis, where both scent and exposure are influenced by the environment or imagery. This concept delves into cross-modal correspondences and the role of sensory cues in shaping our perception of food odours within the VR setting.

Smelling the difference: separation of healthy and infected button mushrooms via microbial volatile organic compounds

In the literature there is a lack of consensus regarding mushroom volatiles; most of the studies identify only a few volatiles. This study deals with button mushrooms, their emitted volatiles, and the main changes during infections (green mould and cobweb disease) in a time series experiment. Emitted volatile profiles were determined using HS-SPME-GC-MS coupled analytical technique. The separation of healthy and infected mushroom samples was done using different multivariate statistical methods (PCA, PLS-DA, HeatMap). The main volatile compounds were also determined. As a result, several compounds were found to successfully distinguish healthy (bisabolene, cymene, myrtenol, d-limonene, etc.) and infected (thujopsene, cedr-8-ene, chamigrene, patchulane, longifolene, etc.), mushroom samples, and an early disease detection was achieved. Results can be used for further investigation of infected mushroom identification in an early stage in packaged mushroom products. Furthermore, these results could help to identify infections in commercially available mushrooms, thus increasing shelf-life in super/hypermarkets.

From Sampling to Analysis: How to Achieve the Best Sample Throughput via Sampling Optimization and Relevant Compound Analysis Using Sum of Ranking Differences Method?

The determination of an optimal volatile sampling procedure is always a key question in analytical chemistry. In this paper, we introduce the application of a novel non-parametric statistical method, the sum of ranking differences (SRD), for the quick and efficient determination of optimal sampling procedures. Different types of adsorbents (Porapak Q, HayeSep Q, and Carbotrap) and sampling times (1, 2, 4, and 6 h) were used for volatile collections of lettuce (Lactuca sativa) samples. SRD identified 6 h samplings as the optimal procedure. However, 1 or 4 h sampling with HayeSep Q and 2 h sampling with Carbotrap are still efficient enough if the aim is to reduce sampling time. Based on our results, SRD provides a novel way to not only highlight an optimal sampling procedure but also decrease evaluation time.