Proximal Remote Sensing to Non-destructively Detect and Diagnose Physiological Responses by Host Insect Larvae to Parasitism

Abstract

As part of identifying and characterizing physiological responses and adaptations by insects, it is paramount to develop non-destructive techniques to monitor individual insects over time. Such techniques can be used to optimize the timing of when in-depth (i.e., destructive sampling of insect tissue) physiological or molecular analyses should be deployed. In this article, we present evidence that hyperspectral proximal remote sensing can be used effectively in studies of host responses to parasitism. We present time series body reflectance data acquired from individual soybean loopers (Chrysodeixis includens) without parasitism (control) or parasitized by one of two species of parasitic wasps with markedly different life histories: Microplitis demolitor, a solitary larval koinobiont endoparasitoid and Copidosoma floridanum, a polyembryonic (gregarious) egg-larval koinobiont endoparasitoid. Despite considerable temporal variation in reflectance data 1–9 days post-parasitism, the two parasitoids caused uniquely different host body reflectance responses. Based on reflectance data acquired 3–5 days post-parasitism, all three treatments (control larvae, and those parasitized by either M. demolitor or C. floridanum) could be classified with >85 accuracy. We suggest that hyperspectral proximal imaging technologies represent an important frontier in insect physiology, as they are non-invasive and can be used to account for important time scale factors, such as: minutes of exposure or acclimation to abiotic factors, circadian rhythms, and seasonal effects. Although this study is based on data from a host-parasitoid system, results may be of broad relevance to insect physiologists. Described approaches provide a non-invasive and rapid method that can provide insights into when to destructively sample tissue for more detailed mechanistic studies of physiological responses to stressors and environmental conditions.

Categorization and authentication of Beijing‐you chicken from four breeds of chickens using near‐infrared hyperspectral imaging combined with chemometrics

“…This technique combines regular digital imaging technique with spectral technique to provide spectral data as well as spatial information (Kamruzzaman, ElMasry, Sun, & Allen, 2011). HSI has been employed in diverse fields, such as medicine and pharmaceuticals (Abdel Gawad, El‐Sharkawy, Ayoub, El‐Sherif, & Hassan, 2019; Parasca, Calin, Manea, Miclos, & Savastru, 2018), biology (Nansen & Strand, 2018), agriculture (Ravikanth, Singh, Jayas, & White, 2015) and bromatology (J. Sun et al, 2017; Velásquez, Cruz‐Tirado, Siche, & Quevedo, 2017).…”