ABSTRACT
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.
Subject(s)
Larva , Moths , Oviposition , Plant Leaves , Plant Roots , Volatile Organic Compounds , Zea mays , Animals , Oviposition/drug effects , Zea mays/physiology , Zea mays/parasitology , Volatile Organic Compounds/metabolism , Volatile Organic Compounds/pharmacology , Moths/physiology , Female , Larva/physiology , Plant Roots/parasitology , Plant Roots/physiology , Plant Leaves/physiology , HerbivoryABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
The box tree moth (Cydalima perspectalis Walker) is an invasive species in Europe causing severe damage both in natural and ornamental boxwood (Buxus spp.) vegetation. Pest management tactics are often based on the use of chemical insecticides, whereas environmentally-friendly control solutions are not available against this insect. The application of essential oils may provide effective protection against oviposition and subsequent larval damage. Oviposition deterrence of cinnamon, eucalyptus and lavender essential oils was tested on female C. perspectalis in behavioural bioassays. Our results indicate that all the studied essential oils may be adequate deterrents; however, cinnamon oil exhibited the strongest effect. To determine the physiologically active compounds in the headspace of the essential oils, gas chromatography coupled with electroantennography recordings were performed in parallel with gas chromatography-mass spectrometry to identify the volatile constituents. In addition, the release rates of various components from vial-wick dispensers were measured during the oviposition bioassay. These results may serve as a basis for the development of a practical and insecticide-free plant protection method against this invasive moth species.
ABSTRACT
Headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) was used to analyse microbial volatile organic compounds (MVOCs) of mushroom disease-related microorganisms. Mycogone perniciosa, Lecanicillum fungicola var. fungicola, and Trichoderma aggressivum f. europaeum species, which are typically harmful in mushroom cultivation, were examined, and Agaricus bisporus (bisporic button mushroom) was also examined as a control. For internal standard, a mixture of alkanes was used; these were introduced as the memory effect of primed septa in the vial seal. Several different marker compounds were found in each sample, which enabled us to distinguish the different moulds and the mushroom mycelium from each other. Monitoring of marker compounds enabled us to investigate the behaviour of moulds. The records of the temporal pattern changes were used to produce partial least squares regression (PLS-R) models that enabled determination of the exact time of contamination (the infection time of the media). Using these evaluation techniques, the presence of mushroom disease-related fungi can be easily detected and monitored via their emitted MVOCs.