Coupling a branch enclosure with differential mobility spectrometry to isolate and measure plant volatiles in contained greenhouse settings

Abstract

Volatile organic compounds (VOCs) are off-gassed from all living organisms and represent end products of metabolic pathways within the system. In agricultural systems, these VOCs can provide important information on plant health and can ordinarily be measured non-invasively without harvesting tissue from the plants. Previously we reported a portable gas chromatography/differential mobility spectrometry (GC/DMS) system that could distinguish VOC profiles of pathogen-infected citrus from healthy trees before visual symptoms of disease were present. These measurements were taken directly from canopies in the field, but the sampling and analysis protocol did not readily transfer to a controlled greenhouse study where the ambient background air was saturated with volatiles contained in the facility. In this study, we describe for the first time a branch enclosure uniquely coupled with GC/DMS to isolate and measure plant volatiles. To test our system, we sought to replicate our field experiment within a contained greenhouse and distinguish the VOC profiles of healthy versus citrus infected with Candidatus Liberibacter asiaticus. We indeed confirm the ability to track infection-related trace biogenic VOCs using our sampling system and method and we now show this difference in Lisbon lemons (Citrus×limon L. Burm. f.), a varietal not previously reported. Furthermore, the system differentiates the volatile profiles of Lisbon lemons from Washington navels [Citrus sinensis (L.) Osbeck] and also from Tango mandarins (Citrus reticulata Blanco). Based on this evidence, we believe this enclosure-GC/DMS system is adaptable to other volatile-based investigations of plant diseases in greenhouses or other contained settings, and this system may be helpful for basic science research studies of infection mechanisms.

Analytical strategies for in-vivo evaluation of plant volatile emissions - A review

“…In particular, the enclosure may change the natural plant micro-ambient, meaning that several parameters must continuously be monitored during the analysis. An equilibrium time (12-48 h) before the beginning of sampling is usually necessary to minimise and stabilise the stresses generated by confinement [25,32,[35][36][37][38][39]. The incoming air is usually purified with charcoal filters to eliminate external BVOCs [22,25,[38][39][40][41][42][43][44] and the flow, which can reach 25 L min -1 , is controlled by a flowmeter/pump to limit water condensation and temperature increases, at the same time, avoiding losses of analytes from the trap during sampling (the socalled breakthrough effect) [38].…”

Identification of Specific Substances in the FAIMS Spectra of Complex Mixtures Using Deep Learning

“…Principal component analysis (PCA) is a commonly used method for data dimensionality reduction. It generates low-dimensional new variables by linear combination of high-dimensional original variables, and the new variables reflect the signal information of the original variables to the maximum extent [40][41][42]. Support vector machine (SVM) is an effective traditional classification model.…”

A Review of the Citrus Greening Research and Development Efforts Supported by the Citrus Research and Development Foundation

“…Biomarkers specific to CLas have been found and could be analyzed using gas chromatography/mass spectrometry and gas chromatography/differential mobility spectrometry (Aksenov et al, 2014). Greenhouse tests showed that a mobile differential mobility spectrometry system was able to distinguish volatile differences between closely-related citrus cultivars and show volatile-profile differences between healthy and infected citrus (McCartney et al, 2016). The use of canines to sniff out these volatiles specific to infected trees has been an effective strategy for citrus canker detection and preliminary observations show promise for dogs to be able to detect HLB infections before symptom expression (Berger, 2014).…”