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Chromatographia

, Volume 82, Issue 1, pp 297–305 | Cite as

Rapid Plant Volatiles Screening Using Headspace SPME and Person-Portable Gas Chromatography–Mass Spectrometry

  • Yong Foo Wong
  • DanDan Yan
  • Robert A. Shellie
  • Danilo Sciarrone
  • Philip J. MarriottEmail author
Original
Part of the following topical collections:
  1. 50th Anniversary Commemorative Issue

Abstract

Rapid on-site screening of biogenic volatile emissions from leaves of living plants is demonstrated, using headspace solid-phase microextraction (HS-SPME) with a portable gas chromatograph (PGC), fitted with a low-thermal mass (LTM) column equipped with a miniature toroidal ion trap mass spectrometer (ITMS). For field sampling, the study was conducted at the Royal Botanical Garden, Cranbourne, Australia, with the sampling site located in the Peppermint Garden. Twelve designated plants in the families of Asteraceae, Lamiaceae, Myrtaceae, Pittosporaceae, and Rutaceae were chosen for this field study. A customised SPME syringe was used for headspace sampling and sample introduction; leaves were collected into vials, equilibrated, sampled onto a PDMS/DVB-coated fibre, then desorbed in the GC inlet in split mode. A resistively heated LTM, narrow bore (0.1 mm ID) non-polar capillary column heated at 2 °C s−1 to 270 °C, provided fast GC elution with total run time of 3 min. The miniaturised ITMS was operated over a mass range of 40–500 Da. This provided approximation of near-real-time measurement of leaf volatiles released from the plant. For a second study, PGC–ITMS is employed to profile essential oils from experimental hybrid and commercial Humulus lupulus L. (hop) plant extracts in the laboratory, and contrasted with bench-top data. Results were processed by chromatographic fingerprinting using retention times, and MS fragmentation pattern similarity criteria. Unsupervised multivariate analysis was performed to improve specificity for classification of different plant volatiles, yielding loading variables corresponding to chemical differences of the analysed plants. The combination of HS-SPME and portable GC–ITMS proved effective for rapid chemical expression of the plant volatile genotype in the field.

Keywords

Low-thermal mass gas chromatography Portable mass spectrometry Leaf volatiles Hop essential oils On-site analysis 

Introduction

Plants are sessile organisms, capable of producing a remarkable diversity of low molecular weight organic compounds (i.e. secondary metabolites) with a range of lipophilicities. These compounds have specific ecological significance (such as signalling in response to biotic stress); they are emitted through leaves, flowers, fruits and the stem into the atmosphere, and from roots into the soil [1, 2]. Ecologically, one function of airborne biogenic volatile organic compounds (VOCs) is to defend against herbivores (e.g. in tritrophic interactions) and pathogens, attraction of pollinators and seed dispersers, and signalling in plant–plant communication [1, 2]. Studies indicate that plant VOCs may be reactive in the troposphere, with life-times in the min to h range [1, 2, 3]. Qualitative and quantitative analyses of VOCs emitted by plants can be efficiently studied by static or dynamic techniques, through headspace collection in combination with gas chromatography (GC) hyphenated with mass spectrometry (MS) analysis.

Direct field (or in vivo) measurements of plant VOCs is of interest to chemical ecologists and plant biologists, allowing measurement of fluxes of biogenic VOCs emitted under different environmental stresses (e.g. natural wounding, leaf senescence, diurnal effects, etc.) [3, 4, 5]. Numerous analytical tools (comprising both sampling and instrumental techniques) have been reported for this task [6]. Two major challenges of field analysis methods are collection/sampling and subsequent transfer of extracted solutes to the analytical system, which may compromise immediacy of analysis. Modification of the analytical instrument suitable for remote and/or at-site analysis is necessary. In the last decade, renewed interest in addressing direct/field analysis, combined with technical innovation have led to development of field- or person-portable analytical instrumentation to facilitate these measurements; MS is a particular focus. The aim may be to monitor fast changes in biogenic VOC emissions. A number of portable MS systems have been reported, including ion mobility MS, quadrupole MS, ion trap MS and time-of-flight MS [7, 8, 9, 10, 11, 12].

Coupling a GC separation dimension to portable MS provides the ability to separate complex mixture components in close-to-real-time, prior to mass spectrometric detection, to greatly improve the broad detection and identification of various chemical classes of VOCs in a given plant. Often specific monoterpenes and sesquiterpenes are of interest, so a separation dimension is crucial in providing sufficient speciation. However, there remain limitations and trade-offs between portability and performance of such a system [13, 14]. Sample preparation and suitable introduction approaches integrated with the portable analytical system is a prerequisite for successful on-site analysis. Solid-phase microextraction (SPME), needle trap sampling, and air sampling onto a small on-board or external sorbent trap may be employed for headspace (HS) sampling prior to thermal desorption in a heated injector [14]. Sampling via HS-SPME followed by laboratory analysis using a bench-top instrument is well documented. Field sampling devices with samples returned to the laboratory has also been reported [15].

Fewer studies report HS-SPME with immediate on-site fast analysis of plant biogenic VOCs, with a person-portable GC–MS system. The objectives of this work are to: (1) evaluate applicability of a person-portable low-thermal mass GC and toroidal ITMS (PPGC–ITMS) to achieve fast chemotyping of hybrid and commercial Humulus lupulus L. essential oils, which provides discrimination among genotypes; (2) examine and demonstrate the potential of HS-SPME with a PPGC–ITMS for rapid chemical expression of leaf VOCs in the field, at prevailing environmental conditions, to allow measurement of leaf volatiles released from the plant. The usefulness, practicability, and limitations of this fast analytical platform for measurement of plant VOCs are discussed.

Experimental

HS-SPME Sampling of Hybrid and Commercial Humulus lupulus L. Essential Oils

Hop essential oils (commercial hop: Cascade; experimental hybrid hop: H1 and H2) were obtained by hydro-distillation and stored at − 20 °C when not in use. Prior to sampling, samples were thawed at ambient temperature for 30 min. HS-SPME was carried out by transferring 500 µL of hop essential oil into a 20 mL glass HS vial fitted with a septum cap, equilibrated for 5 min at ambient temperature. A CUSTODION SPME holder (PerkinElmer Torion Technologies, American Fork, UT) was used for HS sampling, with a polydimethylsiloxane/divinylbenzene fibre coating (Supelco, Bellefonte, PA; DVB/PDMS, 65 µm coating thickness), capable of withstanding injector temperatures up to 270 °C. SPME fibres were conditioned prior to use following the manufacturer’s recommendations. Subsequently, the SPME fibre was manually inserted into the sealed vial through the septum and the fibre was exposed to the sample HS for 2 min at ambient temperature. Extraction time and temperature were chosen to simulate realistic field sampling protocols. Following the extraction process, the fibre was retracted prior to removing from the sample vial and immediately inserted into the PPGC injector for thermal desorption (ca. 20 s). Blank runs were performed prior to each sampling, to ensure no carryover of solutes from previous extractions.

HS-SPME Sampling of Leaf Oil Emissions in the Field

All plant samples (Boronia megastigma, B. heterophylla, Darwinia citriodora, D. collina, Eucalyptus risdonii, E. willisii, Mentha australis, Kunzea pauciflora, Prostanthera incisa, Pittosporum undulatum, Senecio odoratus, and Zieria cytisoides) were sampled directly in the Peppermint Garden, Royal Botanic Gardens (Cranbourne, Australia; 38.1286°S, 145.2695°E). Leaves were collected into new 20 mL vials fitted with septum caps, equilibrated for 10 min (at the prevailing environmental temperature), followed by HS-SPME sampling for 2 min (exposure time was chosen to simulate realistic field sampling protocols), using the HS-SPME approach detailed above. Extracted solutes were desorbed from the fibre for ca. 8 s after insertion into the injection port. Blank runs were performed on the fibres prior to each sampling, to ensure absence of carryover of solutes from previous extractions.

PPGC–ITMS System

Separations were conducted on a person-portable low-thermal mass GC system coupled with a miniature toroidal ion trap mass analyser (PPGC–ITMS; Torion® T-9 Portable GC–MS, PerkinElmer Torion Technologies), equipped with a low-thermal mass injector. The system is a stand-alone instrument that can be used in the field without need for an external computer system. An external D-size helium (He) cylinder (Air Liquide, Melbourne, Australia) supplied ultrahigh purity helium carrier gas (99.999%) to the PPGC–ITMS system at a constant pressure of 30 psig. Chromatographic separation was performed using a MXT-5 column (Siltek-treated stainless steel, Restek Corporation, Bellefonte, PA) of dimensions 5 m × 0.1 mm ID × 0.4 µm film thickness (df). The capillary column, which has an external metal coating, is bundled with a resistive heater and platinum sensor for T control. The oven T program began at 50 °C (hold 10 s), then was heated at 1 °C s−1 or 2 °C s−1 to 270 °C (hold 60 s). He at a constant flow rate of ca. 0.30 mL min−1 was used throughout the experiments. The ITMS conditions were: transfer line T = 270 °C; trap heater T = 200 °C; mass range 40–500 Da. A nominal radio frequency trapping of 2 MHz was used with trapping amplitude of about 200 Vp-p to 2400 Vp-p. A 385 kHz AC signal (ramped from 1 to 10 V in coordination with RF scan) was applied to resonantly eject ions from the trap. The electron gun produces a gated electron beam (approximate 70 eV) for molecule ionisation within the trap. Ions are detected with a continuous dynode electron multiplier detector with an approximate gain of 105 at 1300–1500 V. The GC injector was maintained at 270 °C, equipped with a 1.2 mm ID liner. Split injection was employed, with a 10:1 split for 10 s, followed by 50:1 split flow. A schematic of the system set-up used in the field is illustrated in Fig. 1. On-board battery lifetime was ca. 4 h.
Fig. 1

PPGC–ITMS system on-site in the Royal Botanic Gardens, Cranbourne. A Injection port with CUSTODION programming contact; B data acquisition and processing for operation without external computer control; C rechargeable Li ion battery slot; D compartment for miniaturised toroidal ITMS; the low-thermal mass GC is located on the other end of ITMS; and E external helium supply. On-board He supply is also available

GC–MS System

GC–MS analyses were conducted on an Agilent Technologies 7890A GC system (Agilent Technologies, Mulgrave, Australia) equipped with a 7000 GC–MS triple quadrupole mass spectrometer, and split/splitless injector. Chromatographic separation was performed using a SLB-5 ms capillary column of dimensions 30 m × 0.25 mm ID × 0.25 µm film thickness (Supelco). The chromatographic conditions were: oven temperature program, 50 °C (hold 2 min) at 10 °C min−1 to 270 °C (hold 1 min); helium at flow rate of 1.2 mL min−1; injector temperature, 230 °C and a split ratio of 10:1. The MS transfer line (0.5 m × 0.1 mm ID) temperature was 250 °C, and mass scan range of 40–500 Da was used throughout the analysis.

Data Handling

For the PPGC–ITMS system, data acquisition and processing were performed using CHROMION 1.1 software (PerkinElmer Torion Technologies). Total ion chromatograms were generated by exporting CHROMION data in CSV file format, and further processed using OriginPro 8 SR2 software Version 8.0891 (Origin, Northampton, MA, USA). Principal component analysis (PCA) was performed using The Unscrambler® X 10.3 (CAMO Software, Oslo, Norway), and Microsoft Excel Version 14.0.7140.5002 (Microsoft Corporation, Redmond, WA, USA). Data preprocessing was conducted using CHROMION 1.1 software for background correction, peak detection, and peak deconvolution. Compound retention and MS data (i.e. fragmentation patterns) were used as metrics for tentative clustering and listing of the detected VOCs. PCA was conducted using the three principal components that contain the greatest source of information in the data set as coordinates of points to generate 3D scores and loading plots. The adequacy of the PCA model was further evaluated using random cross validation (10 segments with 2 samples per segment) which is incorporated in The Unscrambler® X software. For the GC–MS system, Agilent MassHunter was used for instrument control and data acquisition.

Results and Discussion

Evaluation of PPGC–ITMS for Qualitative Screening of Experimental Hybrid and Commercial H. lupulus L. VOCs

In the field of hop chemistry, cultivar comparison for the purpose of assessment of suitability for beer production is usually based on the profiling of volatile oil from female hop cones, which allow differentiation through chemical composition and odour characteristics [16, 17]. As the phytochemical profile is a key factor in defining both quality and value of hop strobiles, appropriate measures to determine phytoconstituents of different hop varieties have been of enormous importance to phytologist and hop breeders [18, 19, 20]. In this instance, capability to rapidly perform qualitative screening for basic characterisation of hop chemical phenotypes may facilitate fast selection of novel cultivars, assessment of the most suitable time for harvesting the cones, and/or discriminating intraspecific diversity of H. lupulus (particularly for wild hops) prior to precise quantitative analyses using bench-top instruments. With the development and recently available capabilities in person-portable GC–MS, the applicability of PPGC–ITMS for rapid qualitative screening of hop volatiles has not yet been reported. In this regard, essential oils (EO) derived from selected experimental hybrid and commercial H. lupulus L. may be usefully analysed by PPGC–ITMS to provide rapid chemical differentiation and classification.

Ultrafast temperature programming is one of the key features of low-thermal mass GC, which ideally provides reproducible temperature programming at up to 100 °C min−1 [21]. To further study resolution and overall analysis time, different ultrafast temperature programs were investigated for the analysis of H. lupulus L. EO. Figure 2 reports the separation of a hybrid H. lupulus L. EO by using different oven programs, with two examples illustrated (60 and 120 °C min−1). As expected, a marginally reduced separation was observed (see boxed regions) when the faster oven ramp (120 °C min−1) was used, although substantial co-elutions occurred for both the mono-(MT) and sesquiterpene (ST) regions. Overall, the slower oven ramp (60 °C min−1 versus 120 °C min−1) provided better resolution (Fig. 2) for the terpenic compounds, with an average base peak width (wb) of about 1 s, albeit with a much longer analysis time (increased by ~ 65%). Considering its better chromatographic separation, oven ramping of 60 °C min−1 was applied for further studies. Intra-day precision for PPGC–ITMS was assessed by performing HS-SPME analysis of a mixture of alkanes (C7–C20) on the same day (three injections), and inter-day precision by introducing the same alkane mixtures for three consecutive days. Excellent intra- and inter-day precision was obtained for retention time (RSD < 0.4%), indicating acceptable repeatability of ppGC–ITMS.
Fig. 2

TICs of hop essential oil (sample H1). Variation of oven ramping: A 120 °C min−1; and B 60 °C min−1

In order to compare the separation efficiencies of PPGC–ITMS against bench-top GC–MS, a hybrid hop sample was analysed separately using both GC–MS systems. The acquired total ion current (TIC) chromatograms are illustrated in Fig. 3. Notably, the chromatographic profiles obtained using both methods were similar with respect to the main peaks, but with considerably lower resolution and increased peak co-elutions observed for PPGC–ITMS analysis; this is anticipated due to use of a much shorter (5 m) column and greater temperature programming rate for the latter. The rationale for on-site portable GC–MS is predicated on fast, low-resolution analysis in order to significantly increase throughput. Major hop volatile components (isobutyl isobutanoate, α-pinene, β-pinene, myrcene, pentyl isobutanoate, caryophyllene, humulene, caryophyllene oxide, and humulene epoxide II) were able to be detected using both bench-top GC–MS and PPGC–ITMS, albeit the latter is completed in a much shorter run time (15 min versus 2 min; an approximate sevenfold reduction). Resistive heating also provides narrow and symmetrical peaks (wb ∼ 1 s) versus bench-top GC–MS using convection oven heating, with wb of about 5 s. Figure 4 illustrates the TIC produced by HS-SPME sampling and analysis of an experimental hybrid (Fig. 4Bi) and commercial H. lupulus EO (Fig. 4Ai) using PPGC–ITMS. The degree of variation of phytoconstituents (based on visual comparison in Fig. 4) corresponding to different hop cultivars (experimental hybrid versus commercial) is noteworthy, in both the MT and ST regions. Many peaks appear to be qualitatively common across the two, but with differences in quantitative amount; some peaks are unique to each sample, at the level of data reproduction here. This highlights the potential of PPGC–ITMS for rapid in-field classification of hop cultivars, provided that an in-house library is available using the PPGC–ITMS system. The next part of this study aimed to demonstrate the potential of PPGC–ITMS for immediate on-site analysis of plant-emitted biogenic VOCs.
Fig. 3

TICs of experimental hybrid hop essential oil (sample H2) analysed using: A bench-top GC-MS; BppGC–ITMS. Peaks: 1, isobutyl isobutanoate; 2, α-pinene; 3, β-pinene; 4, myrcene; 5, pentyl isobutanoate; 6, caryophyllene; 7, humulene; 8, caryophyllene oxide; and 9, humulene epoxide II. MT, monoterpenes; ST, sesquiterpenes

Fig. 4

HS-SPME with PPGC–ITMS analysis of hop essential oils: Ai cascade; Bi hybrid sample H2. Aii Expansion of selected time interval shown in Ai; and Bii expansion of selected time interval shown in Bi. Peak identities 1: isobutyl isobutanoate; 2: myrcene; 3: pentyl isobutanoate; MT: monoterpenes; ST: sesquiterpenes

Qualitative Screening of Plant-Emitted Biogenic VOCs

Representative TICs produced by HS-SPME sampling and compositional details of designated plant leaves (B. heterophylla, S. odoratus, and Z. cytisoides) using PPGC–ITMS are shown in Fig. 5. A range of biogenic VOCs were detected and the chromatographic peak heights spanned > three orders of magnitude. The analysis time (3 min) with an upper T of 270 °C was sufficient to elute all solutes. Including the 2-min sampling time, the sampling/analysis turnaround time was 5 min; sample throughput was about 10 analyses h−1 (including a blank run between each sample analysis). High throughout is attributed to the resistively heated LTM column bundle, with fast T programming at 2 °C s−1 via a portable 15 V rechargeable Li ion battery power supply. Narrow chromatographic peaks (Fig. 5; wb ca. 1 s) require sufficient spectrum acquisition speed to adequately define the peak. The ITMS employs a predictive algorithm (i.e. automated ionisation timing control) that controls the ionisation time from scan to scan in order to fill the trap to the same ion loading (i.e. prefixed range of allowable amount of ions from the lowest, at approximately the ion statistics limit, to the upper space charge limit) for each scan, so the acquisition rate will vary with the feedback-controlled ionisation time. The ITMS with electron ionisation and frequency scanning for ion ejection, provides an acquisition rate ranging from ca. 8 to 16 scans s−1, giving 8–16 scans peak−1. This is sufficient to adequately define the peak [9].
Fig. 5

TIC for HS-SPME with PPGC–ITMS analysis of VOCs from: AiZ. cytisoides, BiB. heterophylla, and CiS. odoratus. Aii Expansion of boxed region in Ai; Bii expansion of boxed region in Bi; Cii expansion of boxed region in Ci. Biii and Ciii correspond to extracted mass spectra for peaks indicated by asterisk in Bii, Cii

Fig. 6

Ai TIC of Z. cytisoides leaf volatiles. Aii Expansion of boxed region in Ai highlighting co-elutions of the detected VOCs. Aiii Mass spectrum deconvolution of the boxed region in Aii. Wi, Wii, Wiii, and Wiv correspond to the extracted mass spectra (without applying deconvolution) corresponding to peaks 1, 2, 3 and 4 in Aii. Di, Dii, Diii, Div, and Dv correspond to the extracted mass spectra (with deconvolution) corresponding to peaks D1, D2, D3, D4, and D5 in Aiii

Solute tR and extracted mass spectra, including retention indices, can be employed as criteria for compound identification or clustering among different plant VOCs provided that no co-elution occurs (which can result in “imprecision” of mass spectra), as illustrated in Fig. 5Biii, Ciii (due to their equivalent retention times, peaks marked by asterisk in B. heterophylla and S. odoratus have high probability of being the same component, especially if their mass spectra are a good match). Chromatographically, some of the VOCs are poorly resolved (see Fig. 6Ai), partly due to the relatively high solute loading for some compounds on the GC column, and partly due to the rapid temperature program used (resulting in reduced peak capacity) [13]. Mass spectrum deconvolution was applied to the TIC for all plant samples. As shown in Fig. 6Aiii, five closely co-eluting compounds were located by applying deconvolution over the ca. 2 s GC elution window of the overlapping peak cluster. Note that some smaller peaks were not identified due to insufficient ion abundance/concentration. By performing deconvolution, unique spectra were available for each compound (Fig. 6Di–Dv), compared with the non-deconvoluted mass spectra (Fig. 6Wi–Wiv). A sufficient number of scans are mandatory for accurate MS deconvolution of chromatographic peaks; deconvolution algorithms also play a critical role. For instance, peak 3 (Fig. 6Aii) was apparently not effectively deconvoluted due to the limited number of scans across the peak (number of scans was ca. 3; from 51.76 to 52.02 s) whilst others have ≥ 5 scans. Since a miniature toroidal ITMS was employed, a predictive algorithm controls ionisation time across scans, so variation in scan rate across chromatographic peaks can be expected. The important correlation of number of scans per GC peak width, with separation speed, suggests that further technical improvement of scanning speed for the toroidal ITMS may improve spectrum deconvolution.

Identification based on direct comparison of detected compound mass spectra with those available in the MS library is not effective in the current case. This is attributed to differences in spectra obtained via the miniature toroidal ion trap mechanism, compared with commonly used quadrupole and magnetic sector mass analysers which are used to create most of the NIST library entries. This observation is logical taking into account the differences in mass separation/detection (µs time scale for quadrupole and ms time scale for IT) for the mass analysers. Self-chemical ionisation effects may also arise in the toroidal ITMS (proceeding via proton donation from the precursor molecules during 70 eV ionisation) that produce pseudomolecular ions [10]. This highlights the necessity of generating a miniature toroidal ITMS database for effective compound searching and identification. This can be relatively easy if relevant standards are available for compounds of particular interest, but renders the library match protocol against the NIST library unreliable for other compounds, and for untargeted analysis.

Compound tR and MS data (i.e. their fragmentation patterns) were used as metrics for tentative clustering of the detected VOCs, and the treated data were subjected to PCA to objectively compare biogenic VOCs of different plants. The PCA scores plot obtained from the combined VOC profiles of 12 designated plants is shown in Fig. 7. The first three principal components (PCs) had the greatest eigenvalues and thus contained the chemically relevant variance, so PC1, PC2, and PC3 were used for the construction of the plots. The scores clustered into five groups according to the plant VOC profiles. Discriminating biogenic volatiles among different plants can be readily observed from the loadings plot (Fig. 7b). These results demonstrated that the production and emission of plant VOCs show genotypic variation and phenotypic plasticity; it is relatively complex and likely involves the interplay of several biochemical pathways and hundreds of genes [1]. Being confined to a small number of samples, the findings presented above are exploratory in nature and interpretation of putative VOC variations should be drawn with care, as their relevance and consistency needs to be more widely evaluated.
Fig. 7

PCA of measured leaf volatiles from 12 designated plants: A 3D scores plot, and B 3D loading plot. PC 1, PC 2, and PC 3 capture the significant VOC variations (57% of the variance). Variance % explained by each principal component is indicated in parentheses. For A a: E. willisii; b: Z. cytisoides; c: B. megastigma; d: M. australis; e: P. incise; f: K. pauciflora; g: D. citriodora; h: P. undulatum; i: B. heterophylla; j: S. odoratus; k: E. risdonii and l: D. collina. Numbering in B correspond to the measured volatiles (total of 121). This figure is based on tR and mass spectrum data similarities of peaks across the different analyses, to confirm that the respective peaks correspond to the same compound. Note that their tentative identity is not confirmed due to unavailability of a reference ITMS database

This study demonstrated the opportunity and feasibility of using PPGC–ITMS to monitor fast changes in leaf VOC emissions (e.g. on the scale of several min), which has potential to aid biologists to elucidate plant metabolic processes, to examine responses to external stimuli and stresses, and facilitate in vivo studies of VOC biosynthesis. In a broader context, monitoring of non-plant VOCs, such as environmental pollutants (in water, soil, or chemical spills, etc.), and gas-phase contaminants (forensic applications, chemical warfare agents, etc.) that require immediate responses are also applicable. Creating a purpose-designed MS library for specific target analyses, e.g. BTEX, can be readily completed by analysing the required standards, but for untargeted analysis as here, a dedicated miniature toroidal ITMS library will be mandatory.

Conclusion

This study demonstrates the efficacy and potential of a PPGC–ITMS as an analytical platform for rapid on-site measurement of biogenic plant VOCs. Separation of many of the plant volatile secondary compounds was achievable. Applying deconvolution can compensate for some of the loss of chromatographic resolution due to the short column length and high column ramping rate, and subjecting data to PCA allows classification of different leaf VOCs, characterised according to their chemical composition, and provides information on discriminating metabolites in each family or species. This should allow plant analysts to utilise measured plant volatile compound fractions to quickly locate regions of interest for deconvolution, quantification and discovery of important markers that are diagnostic of plant origin, phenotype, and variety/cultivar. Future work will be directed to generating a plant secondary compound database for the miniature toroidal ITMS to improve compound searching and identification, and will potentially be useful for untargeted profiling purposes. The use of different column phase chemistries, including an enantioselective column for rapid on-site chiral analysis of isomeric plant-emitted volatiles (normally terpenic compounds), would be an interesting option to investigate.

Notes

Acknowledgements

DDY gratefully acknowledges the provision of a Tasmania Graduate Research Scholarship. The authors also thank PerkinElmer for providing the ppGC–ITMS system used in this study. The authors acknowledge University of Messina for support through the “Research and Mobility” collaborative project.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Studies involving with human or animals participants

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

10337_2018_3605_MOESM1_ESM.pdf (130 kb)
Supplementary material 1 (PDF 129 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Chemical SciencesUniversiti Sains MalaysiaPenangMalaysia
  2. 2.Australian Centre for Research on Separation Science, School of ChemistryMonash UniversityMelbourneAustralia
  3. 3.Australian Centre for Research on Separation Science, School of Natural SciencesUniversity of TasmaniaHobartAustralia
  4. 4.Trajan Scientific and MedicalRingwoodAustralia
  5. 5.Centre for Advanced Sensory Science (CASS), School of Exercise and Nutrition SciencesDeakin UniversityBurwoodAustralia
  6. 6.Dipartimento di “Scienze Chimiche, Biologiche, Farmaceutiche ed Ambientali”University of MessinaMessinaItaly

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