- Email: [email protected]
Chemotyping and identification of protected Dalbergiatimber using gas chromatography quadrupole time of flight mass spectrometry
Journal Pre-proof
Chemotyping and Identification of protected Dalbergia timber using gas chromatography quadrupole time of flight mass spectrometry Dayue Shang , Pamela Brunswick , Jeffrey Yan , Joy Bruno , Isabelle Duchesne , Nathalie Isabel , Graham VanAggelen , Marcus Kim , Philip D. Evans PII: DOI: Reference:
S0021-9673(19)31223-3 https://doi.org/10.1016/j.chroma.2019.460775 CHROMA 460775
To appear in:
Journal of Chromatography A
Received date: Revised date: Accepted date:
15 October 2019 3 December 2019 6 December 2019
Please cite this article as: Dayue Shang , Pamela Brunswick , Jeffrey Yan , Joy Bruno , Isabelle Duchesne , Nathalie Isabel , Graham VanAggelen , Marcus Kim , Philip D. Evans , Chemotyping and Identification of protected Dalbergia timber using gas chromatography quadrupole time of flight mass spectrometry, Journal of Chromatography A (2019), doi: https://doi.org/10.1016/j.chroma.2019.460775
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
Highlights • Timber extracts exploited using GC/QToF for chemotyping forensic analysis. • Differentiation and determination of wood achieved at genus/species level. • CITES listed wood species investigated and identified with the new method. • EI wood spectral library suitable for method transfer to routine environmental labs.
1 of 23
Chemotyping and Identification of protected Dalbergia timber using gas chromatography quadrupole time of flight mass spectrometry Dayue Shanga*, Pamela Brunswicka, Jeffrey Yana, Joy Bruno a, Isabelle Duchesneb, Nathalie Isabelb, Graham VanAggelena, Marcus Kimc and Philip D. Evansd,e a
Environment and Climate Change Canada (ECCC), North Vancouver, BC, Canada Natural Resources Canada, Canadian Forest Service, „Canadian Wood Fibre Centre, Québec City, QC, Canada Agilent Technologies Inc., Mississauga, ON, Canada d Faculty of Forestry, University of British Columbia (UBC), Vancouver, BC, Canada e Department of Applied Mathematics, Research School of Physics, The Australian National University, Canberra, Australia b c
ABSTRACT The international trade in illegally logged and environmentally endangered timber has spurred enforcement agencies to seek additional technical procedures for the identification of wood species. All Dalbergia species are listed under the Convention on International Trade in Endangered Species (CITES) which is the reason this genus was chosen for study. Multiple sources of the heartwood from different Dalbergia species were extracted and chromatographic profiles collected by gas chromatography with high resolution quadrupole Time of Flight mass spectrometry (GC/QToF). The collected data was mined to select peaks and mass ions representative of the investigated Dalbergia species, and used to develop a Microsoft Excel® template offering immediate graphical representation of the results. Using wood specimens sourced from different xylaria, this graphical fingerprint proved adept at definitive identification of Dalbergia species. The CITES Appendix I species, D. nigra, was easily distinguished from D. melanoxylon and look-alike species of other genera. Similarly, a number of other Dalbergia species were differentiated using this current approach. Kernel discrimination analysis (KDA) was applied to increase the confidence of the species identification. The mislabeling of specimens appears to be common, and the emerging technique of GC/QToF in combination with other techniques, offers improved confidence in identification. GC/QToF further provides automation, the dimension of chromatography to avoid interferences, and production of reproducible electron impact positive (EI+) spectra. The prospect of building an EI+ spectral database for future wood identification is an important feature considering the limited accessibility of authenticated wood species specimens. Keywords: Rosewood, Dalbergia, illegal logging, identification, GC/QToF, chemotyping
1.
Introduction
Illegal logging – the harvesting, processing, and trade of timber in violation of national laws -destroys forest ecosystems, deprives underdeveloped nations of income, and funds unlawful activities1. The timber species that are prohibited (or restricted) from trade are those listed in three appendices to an international treaty *
Corresponding author at: Environment and Climate Change Canada (ECCC), North Vancouver, BC, Canada Email address: [email protected] (Dayue Shang)
2 of 23
document known as the Convention on International Trade in Endangered Species of Wild Flora and Fauna (CITES): Appendix I lists species threatened with extinction, i.e., trade in these species is only permitted in exceptional circumstances; Appendix II lists species where trade is controlled to avoid unsustainable utilization, which if left unchecked, could lead to extinction; Appendix III lists species where trade is controlled in one or more countries, which may request assistance from other signatory countries to restrict trade in the listed species. In Canada, CITES is implemented under the Wild Animal and Plant Protection and Regulation of International and Interprovincial Trade Act (WAPPRIITA) and the Wild Animal and Plant Trade Regulations. The act of illegal import of wood circumvents payment of import duties that also undermines the Canadian economy and devalues local Canadian products. Many other countries also prohibit the importation of timber not obtained in accord with the laws of the country of origin, e.g., US Lacey Act (amended 2008); EU Timber Regulation (2010); and Australian Illegal Logging Prohibition Act (2012). The global impact of illegal logging has been of great concern for decades, but laws governing trade in illegally logged timber are difficult to enforce and criminal convictions are rare because of currently lacking the means to identify many protected timbers to a level of certainty required by customs or a court of law. For example, the rosewoods (Dalbergia spp.), which are listed in CITES Appendix I (Brazilian rosewood, Dalbergia nigra (Vell.) Allemão ex Benth) or Appendix II, need CITES permits to be traded in Canada including information on their scientific name (genus and species). Rosewoods are timber products of a rich red colour and are in great demand for musical instruments, furniture and carvings: they are the world‟s most commonly trafficked timber. „Rosewoods‟ can be assigned to the genus Dalbergia using light microscopy, but microscopy, even when combined with multivariate statistical analysis, cannot identify individual Dalbergia species, as required2. Hence, there is a pressing need for researchers to develop practical methods to meet law enforcement agencies‟ demand for identifying individual Dalbergia genera at species level. Because individual Dalbergia species cannot be identified by conventional taxonomic approaches using light microscopy, intensive research has been on going worldwide3-14. For example, a number of researchers initially developed techniques able to distinguish D. nigra (CITES Appendix I) from the other Dalbergia species (CITES Appendix II). Liquid chromatography/mass spectrometry achieved this goal by identifying a chemical marker, the neoflavanoid dalnigrin (6-hydroxy-7-methoxy-4-(4-methoxyphenyl)-2H-1-benzopyran -2-one (4′-Omethylmelanettin); which was present in D. nigra, but absent from some other Dalbergia species3. More recent research has focused on identification and separation of multiple individual Dalbergia species using other analytical techniques, and also DNA barcoding. DNA barcoding is capable of identifying multiple Dalbergia species, but it is hampered by the challenge of extracting good quality DNA from timber4,5,6. Several analytical techniques have also been entertained as a means of separating Dalbergia species, including spectroscopy (mid and near infra-red, and nuclear magnetic resonance spectroscopy), and gas chromatography/mass spectrometry (MS)7,8,9,10,11 and other forms of spectrometry, most notably, Direct Analysis in Real Time (DART) Time-of-Flight MS (DART/ToF). DART/ToF (AccuToFTM of JEOL Inc., hereafter JOEL ToF) is the only MS technique that has been widely tested, and it is currently accepted as the method of choice for identifying Dalbergia and other CITES listed species12-19. DART/JEOL ToF is not without its drawbacks, however, including potential interference by wood treatments (coatings and biocides), inability to automate analysis and, most importantly, the fact that the current DART/JEOL ToF mass spectral library derived from numerous authentic wood samples cannot be easily transferred to other ToF MS instruments due to the nature of
3 of 23
automorphic ionization of the DART design. A potential alternative to DART/JEOL ToF is GC/MS, which has only been tested on a handful of Dalbergia species, and has not used the full potential of GC/MS for forensic fingerprinting of wood9,10,11. The internationally recognized use of GC/MS in forensic fingerprinting of oil and petroleum products is much more advanced, and appears that the full potential of the GC/MS technique for the identification of timber has not been fully exploited 20. Over the years, oil spill researchers have found many characteristic ion patterns unique to different crude oils and petroleum products 15. Hence, we hypothesized that comparison of isobaric (and isomeric) ion patterns (IIP) of GC/QToF high resolution MS analysis, would be capable of distinguishing Dalbergia species. While recognizing the leap forward in forensic wood analysis achieved by DART/JEOL ToF chemotyping, the current work exploits the additional capabilities of chromatographic separation and EI based spectral database to provide a precise analytical procedure that can be easily adopted by routine analytical laboratories worldwide. 2.
Materials and Methods
2.1 Materials Methanol (HPLC-grade), ammonium hydroxide (≥99.99% purity) and formic acid (≥98% purity) were supplied by Sigma-Aldrich (Oakville, Ontario, Canada). HPLC grade isopropanol was purchased from Caledon Laboratories Ltd (Georgetown, ON). Wood samples were selected based on their listing in CITES appendices, similarity in appearance to listed species (look-alikes), or commercial importance. In total, 16 specimens from 11 species were obtained from wood collections (xylaria) located in Faculty of Forestry, University of British Columbia (Vancouver, Canada); Natural Resources Canada, Canadian Forestry Services (NRC-CFS); Jodrell Laboratory, Royal Botanic Gardens, Kew, London; and the Wildlife Enforcement Directorate, Environment and Climate Change Canada (WED, ECCC), (SI Table S1).
2.2 Sample preparation Thin slivers of heartwood were removed from xylarium specimens using a box knife or a chisel and razor saw. Note: wear anti-cut gloves when taking sample. Varnished surfaces and sapwood were avoided. The samples were weighed into ~15 mL glass tubes with Teflon lined lids. Each sample, approximately 50 to 100 mg of wood slivers, was extracted in 5 mL of methanol containing 1% v/v of formic acid. The sample was vigorously vortexed using a Fisher Scientific Multi-tube vortexer at a speed setting of 8 and extracted overnight at room temperature. After extraction, the samples were re-vortexed and centrifuged at 4645 xg for 2 min (or as required to settle solid materials), and a 1.5 mL aliquot of the supernatant was transferred by glass pipette to a GC vial for GC/QToF analysis. The remaining sample was stored at -40 ± 5 °C for future reference. 2.3 Instrument conditions and analysis Target compounds were analyzed using an Agilent 7890B GC interfaced to an Agilent 7250 QToF mass spectrometer. Typical operating conditions are summarized in SI Table S2, using 1 µL injection in pulsed splitless mode. GC separation was performed on a DB–5MS capillary column (Agilent Technologies, 30 m × 0.25 mm ID × 0.25 µm film thickness) or equivalent column. The GC oven was programmed at 50°C and held for 2
4 of 23
min, heated to 310°C at 6°C/min, and kept at maximum temperature for 8 min to remove higher boiling point compounds. A system suitability check was performed prior to each analytical sequence by external mass calibration using Perfluorotributylamine (PFTBA) tuning solution. Detection of the compounds in EI+ used centroid acquisition. Chromatographic peaks observed in total ion scan mode (TIC) were reviewed for patterns and characteristic mass ions. Peaks observed in blank control samples were excluded from the process. Mass spectral ions that were characteristic of the species were extracted from the TIC integrated for diagnostic ratio analysis. 2.4 Quality Control For each analytical sequence, a performance check solution consisted of 16 deuterated polycyclic aromatic hydrocarbons standard (SI Table S5) was run at the beginning, every 6 injections, and at end of the sequence to monitor any drifts of peak retention times or decrease in sensitivity. Additionally, to avoid retention time shifts throughout and between runs, a retention time lock method was implemented to ensure consistent peak selection during data analysis and processing. To eliminate any contaminant or background peaks, a baseline reagent and/or preparation blank background was collected with each batch. Any significant response in the blank background will be considered during data analysis. Furthermore, all wood samples were analyzed in duplicate to verify repeatability. During method development, sample extraction and analysis were duplicated eight times to confirm repeatability. 3.
Results and Discussion
3.1 Optimization of sample extraction Ideally, extraction solvent(s) for the chromatographic analysis of wood require a combination of properties: ability to penetrate into wood micropores; efficiency in extracting organic compounds; miscibility with wood containing moisture; suitability for long term storage e.g., at -40 °C; and compatibility with both LC/MS and GC/MS. We examined several solvents and their combinations to find out if they could fulfill these criteria3,10. The solvents studied included methanol (MeOH), ethanol, MeOH with 1% formic acid, MeOH with 1% ammonium hydroxide, binary solvent of MeOH with isopropanol at 3 compositions (25:75, 50:50, 75:25, v/v), and acidified binary solvent of MeOH with isopropanol and formic acid (75:24:1, v/v/v). The duration of extraction was also varied: 1, 2, 4, 8, 12, 24 and 48 hours. The results of the extraction experiments are shown in SI Table S3 and Figure S1. Based on the outcome of the experimentation, the solvent and extraction procedure selected for this study was methanol acidified with 1% formic acid and extraction for ≥12 h. The developed wood extraction procedure produced solutions of various hues corresponding to the timber being extracted. In particular, the darker woods of the CITES Appendix I listed Dalbergia nigra, produced dark brown colored solution that was notable. Based on visual observation of solution color, an initial laboratory identification process can begin. This observation is also under consideration for use in the field by enforcement officers, where rapid apprehension of CITES listed timbers is critical to stem the flow of illegal trafficking. A quick on-site extraction of slivers of wood taken at an inspection site can be compared with a set
5 of 23
of color-coded cards typical to common Dalbergia spp. and their look-alikes. The triaged wood extracts can be shipped to the authors‟ lab for confirmation. Our developed method provides field enforcement officers with a practical tool in determining if the suspected rosewood sample is worth apprehending for confirmatory analysis. 3.2 TIC chromatographic comparison of Dalbergia species It is well established that each wood species has its own distinct chemical fingerprint6. In our work, multiple Dalbergia species were analyzed by GC/QToF, together with two look-alike wood samples. All showed distinguishable chromatographic profiles in total ion chromatographic scan mode (TIC) in terms of retention time regions, patterns, and number of peaks (Figure 1). For reference, a blank TIC control is shown in SI (Figure S2). When the TIC of an unknown sample was compared visually with those of the reference woods belonging to different species, it was possible to rapidly hone in on the most similar species to the unknown specimen (Figure 1). Following the visual pattern recognition approach, a collection of over 100 Dalbergia and 50 other species‟ GC/QToF TICs were assembled for rapid screening of Dalbergia genus and species assignment (data not shown). With such an extended wood TIC database, it was possible to use the pattern recognition to distinguish between Dalbergia species and look-alike woods of a different genus, such as Diospyros and Cordia species (Figure 1). It is noted that spurious siloxane peaks sourced from the GC column were not considered during comparisons. In addition, saturation of high responding ions in the major chromatographic peaks for some species was unavoidable where lower responding chromatographic peaks were of interest. Furthermore, while members of the same Dalbergia species had similar TIC profiles, upon close examination, minor differences become apparent (Figure 1). These minor differences were likely due to intraspecific variation and length of time and conditions under which the sample was stored. The observed minor differences did not significantly affect the visual profiles for individual species, or the respective characteristic ions under the peaks of interest. This finding accords with those of recent publications that used low resolution GC/MS TIC profiles, together with FTIR, to identify Dalbergia species9,10,11. DART/JEOL ToF is currently the most well established chemotyping technique and extensive spectral libraries are being built around the statistical analysis of data collated from extensive wood sources 9,10,11. The DART system has a proven record when combined with JEOL ToF instrumentation, but the mass spectral library is not always compatible with analyses performed on ToF designs from other manufacturers. As a result of this, and the limitations placed on most analytical laboratories by the lack of extensive and vouchered wood libraries, an aim of the current work was to initiate an analytical method to support the DART/JEOL ToF approach, while offering mass spectral data comparable between laboratories. We suggest that the DART/JOEL ToF and GC/QToF techniques described here are complimentary to one another, each with their own advantages. While DART/JEOL ToF analysis time is rapid, it is hampered by an inability to automate the process. By comparison, a simple overnight sample extraction combined with GC/QToF analysis can be automated to run continuously even over a weekend. Compared to the DART/ToF method of chemotyping by direct mass spectrometry13,14,18, the use of GC/QToF offers an added dimension of chromatographic compound separation. This has the advantage of reducing potential interferences caused by contamination of wood samples with chemicals picked up during storage, sawing, and conversion into manufactured wood products. At this stage, the presented GC/QToF approach should be considered as a complimentary method of wood
6 of 23
species identification in support of the established DART/ToF analysis. Meanwhile, our results are noteworthy because ion fingerprints from GC/QToF in TIC could quickly identify genus and species, without the need for multivariate analysis of data as employed by DART/JEOL ToF and also avoiding potential interferences. 3.3 Extracted ion chromatographic (EIC) comparison of Dalbergia species Based on data in the literature, together with careful examination of multiple Dalbergia species and their wood look-alikes, many characteristic ions were identified in the mass spectra of the total ion chromatographic scan mode (TIC) retention time peaks. These mass ions were exploited for their isomeric and isobaric ion chromatographic patterns (IIPs) to be used as markers for species identification. For example, six characteristic chemotyping (species-specific) markers for D. nigra were identified at 123.009 m/z, 134.074 m/z, 178.065 m/z, 255.068 m/z, 270.092 m/z, and 274.090 m/z and their IIPs were compared to those of other Dalbergia species and look-alike woods (Figure 2, SI Figures S5 and S6). It is noted that the ions selected were not necessarily the highest responders in a peak spectrum but were based on ability to consistently perform as identifying ions. In this sense, the ions selected would not necessarily be indicative of a compound molecular mass. A visual assessment of selected ion chromatographic profiles was readily able to distinguish D. nigra from all of the other Dalbergia species, in particular, by strong single ion peaks that were not present at the same retention time in the other comparison species (Figure 2, SI Figures S5 and S6). Different sources of Dalbergia samples showed similarities within each species set, suggesting that while these 6 ions were specific to D. nigra, they were further useful in distinguishing other species. Similarly, other ions typical of other Dalbergia species were exploited further for this purpose (details to be discussed in the following Sections). Dalbergia nigra, a CITES Appendix I listed species, and Dalbergia melanoxylon, a CITES Appendix II listed species, can be similar in appearance, making visual identification difficult. With the aid of the 6 Dalbergia nigra specific ions and IIP, the current method could achieve a rapid and definitive differentiation between the two closely related species using 4 specimens of each species (Figure 3, SI Figures S7 and S8). Having analyzed over one hundred different species of wood extractives and several genera by GC/QToF (data not shown), we found that a combination of TIC and IIP chromatographic profiles was a powerful tool for identifying and separating closely related timber species. This view accords with the results of research that used LC separation with MS to distinguish D. nigra from other Dalbergia species3. Our expectation is that the currently reported GC/QToF method can be transferred easily to other laboratories because of the consistent ion fragmentation patterns of EI+. Thus, the ability to offer a characteristic, speciesspecific, library is a distinct advantage because of the logistical difficulties in obtaining vouched wood specimens. Such specimens are sourced from a limited number of xylaria around the world that are actively curated and hold comprehensive collections of CITES-listed species24,25,26. Our future aim is to build upon the presented wood species-specific information, and to enable any analytical laboratory with GC/QToF capability, with the ability to provide a wood species identification service in support of environmental protection. The building of an EI based wood spectral library is currently underway at the authors‟ lab.
7 of 23
3.4 Dalbergia species fingerprint identification While visual comparison of ion chromatograms may give a clear indication that a timber or a wood product is a “match” to vouchered specimens of a particular Dalbergia species, it may not be sufficient for forensic work. The careful review of multiple extracted ion chromatograms may be difficult to present in a legal court of law. A clearer picture can be gained by processing the data in the format of a fingerprint graph. Multiple sources of different Dalbergia species extracts analyzed by GC/QToF were used to select performing mass ions at specific retention times (referred to as ID ions) for all of the species as a group (SI Table S4). Species included multiple source samples of identified Dalbergia from xylaria (refer to acknowledgements), which included nigra, melanoxylon, retusa, latifolia, sissoo, stevensonii, oliveri, and limited samples of sissoides, spruceana, minahassae, and glaucenscens. The performing ion peaks were extracted and integrated, using Agilent Mass Hunter quantitative software, to provide a peak height response value. Resulting data was inserted into a template prepared in Microsoft Excel®, in which the integrated peak heights could be compared between two samples, or between the means of two samples, in a similar manner to the template currently used for oil spill forensic analysis20,21,22. The comparison showed peak height response on the y-axis and the identifying ion with retention time on the x-axis. Using this template, a rapid comparison could be made between a sample and potential species of interest for identification purposes. Since multiple ID ions have been included in the fingerprint template in order to encompass as many woods from Dalbergia genus as possible, there could be responses in the template that did not correspond. However, when a sample was compared with another of the same species, the “paired” fingerprint ID ions matched with fewer random unpaired ions presented in the figure. An example is given of a comparison between replicate extracts from two different sources of the same Dalbergia species, with replicates showing good “pairing” between the fingerprint ID ions (Figure 4, SI Figure S9). A match is observed by the “pairing” of bar responses in the graph for the sample (red) and comparison sample(s) (blue) for each particular ID ion. Extracts of different concentrations will show different intensity of response, but “paired” responses are the important factor for comparison rather than peak intensity. Different species have different numbers of performing ID ions, with some more available than others (Figure 4, SI Figure S9). To verify the applicability of the data processing method, the template was expanded to compare a single duplicate sample (e.g., unknown) with the mean response from groups of other known specimen sample replicates. Example is given for a typical D. nigra sample (red) versus D. melanoxylon replicates from 5 different xylaria sources (blue), with the graph showing a distinct “no match” (Figure 5). Further examples are given for a typical D. retusa (red) versus D. stevensonii (blue) from 5 different xylaria sources, with the graph again showing a “no match” (SI Figure S10). In contrast, an unknown sample was definitively identified as D. melanoxylon by a “match” with 5 different xylaria sources of the same species (Figure 6). While the paired ID ions are evident in the fingerprint graphs, it is noted that even within a single wood species, differences can occur due to intraspecific variation. This variation within a species is allowed for in the assessment of the fingerprint ID matching by selecting acceptable criteria. For the data collected to date, an acceptable “match” result was found to be ≥65 % pairing of the total observed ID ion responses being
8 of 23
considered. Fewer paired ID ion responses of 50-60 % total observed, was considered a “chemically related match”. A “no match” was assessed to be <50 % pairing of the total observed ID ion responses. These performing ID ions and criteria have been proposed based on current data, and with constant addition to our present wood species collection and the on-going building of the wood spectral library, the selection of ions and criteria will be subject to further adjustment for improved selectivity. 3.5 Chemically related Dalbergia species While the fingerprint template was rapidly able to identify the encompassed Dalbergia species, an interesting observation came to light during sample analysis. Some of the donated Dalbergia species appeared to be much more similar in chemotype fingerprint; namely, D. oliveri was similar to D. stevensonii, and D. sissoides and D. sissoo were similar to D. latifolia. A careful review of the fingerprint graphs showed that, while these species were almost identical, there were subtle differences and nuances. For example, different sources of D. oliveri matched well (SI Figure S3), while comparison with D. stevensonii consistently showed four distinctive unpaired responses at m/z 164 (RT 41.4), 229 (RT 37.5), 257 (RT 42.6), 259 (RT 48.3), 286 (RT 36.6), 300 (RT 35.7), and 300 (RT 42.5) (Figure 7). These unpaired responses were able to distinguish D. stevensonii from D. oliveri, although they are very similar chemically. A publication in 2013 reported the first molecular phylogenic study of the genus Dalbergia, concluding that Dalbergia has monophyletic ancestry; although the study did not reference D. stevensonii or D. sissoides27, so no comparison could be made in that regard. With respect to the D. sissoo and D. sissoides versus D. latifolia from six different xylaria sources, conclusive evidence was limited by availability of specimens. D. sissoo species could be distinguished from D. latifolia by distinctly higher responses at m/z 154 (RT 35.3), 154 (RT 38.2), 184 (RT 34.6), 195 (RT 32.8), 212 (RT 34.6), 221 (RT 36.3), 223 (RT 32.8), 253 (RT 36.3), 270 (RT 32.7), and 300 (RT 38.2) (SI Figure S11). The relationship between D. latifolia and D. sissoides was stronger and appeared as a “match” for the fingerprint pairing (Figure 8). While specimens of D. sissoides were limited, current data showed a potential differentiation by strong responses at m/z 270 (RT 32.7) and 282 (RT 38.9), plus other lower mass ions. This was upheld by a kernel discrimination analysis (KDA) of the selected ID ion data that showed distinct groupings for these three Dalbergia species with a 91.89% validation (Figure 9). The close relationship between these three species highlighted the potential for some wood species to be labelled incorrectly by single techniques. Knowing that statistical analysis of the fingerprint data (inclusive of chromatographic separation) can readily differentiate between very closely related species, we believe that this GC/QToF method could be used as a powerful tool for wood species differentiation and is worth further exploration. The mislabeling of specimens appears to be common, and the emerging technologies of DART/JEOL ToF13,14,18 together with GC/QToF in support of identification conclusions, offers a route to ensure agreement. 4.
Conclusion
A GC/QToF method was developed to analyze wood extracts for chemotyping in support of wood species identification relating to international trade in illegally logged endangered timber. While the well-established technique of DART/JEOL ToF has proven applicable, the current procedure has added the dimension of
9 of 23
chromatography to the profiling. The developed method consists of 3 steps for wood species identification. The first step is a visual comparison of GC/QToF chromatograms of both TIC and extracted ions, which provides an indication of the probable Dalbergia genera and species. The second step is to quarry data collected from GC/QToF runs and to select mass ions at specific retention times (referred to as ID ions) that are representative of the Dalbergia species as a group. Presentation of these performing ID ions in graphic fingerprint form leads to a rapid species identification in a format that would be more understandable to the general public and a court of law. For more challenging samples, the third step is to produce KDA plots with the ID ions which enable distinction between the chemotyping of similar species. The lack of authenticated wood specimens emphasizes a current problem for most laboratories worldwide. Considering the transferability of EI+ GC mass spectra, the current work focused on a readily transferable GC/QToF screening procedure that could be used either in support of DART/ToF results, or as an alternative. The application of statistical analysis to integrate GC/QToF data, inclusive of the added dimension of chromatographic separation, was shown to have significant potential for future development. Our current GC/QToF results presented by fingerprint graphs, demonstrated the close chemotype relationship between some species. Taken together with natural species intra- and inter-variability, this emphasizes the problems faced by all techniques in the identification process, as a result not only of the method, but on the availability of multiple specimens. The propensity for misidentification was noted even within the xylaria specimens collated for this study. The mislabeling of specimens appears to be common, and the emerging technique of GC/QToF in support of conclusions made by other techniques, offers improved confidence in an identification. ACKNOWLEDGEMENTS The authors gratefully acknowledge the initial training and generous assistance of Dr. Edgard Espinoza, Dr. Cady Lancaster, and Pamela J. McClure from the National Fish & Wildlife Forensic Lab, Ashland, Oregon. Wood reference materials were generously supplied by the Faculty of Forestry, University of British Columbia (UBC), FPInnovations, Dr. Peter Gasson of Jodrell Laboratory/Kew Gardens, Natural Resource Canada, Canadian Forestry Services (NRC-CFS), and the Wildlife Enforcement Directorate, Environment and Climate Change Canada (WED, ECCC). Jean-François Dubois of WED ECCC is acknowledged for his valuable technical support. DS, PB, JY, JB, and GV also thank the patient support of their colleagues, Honoria Kwok, Taylor Filewood, Liane Chow, Ceara MacInnis, Faith Park, Oxana Blajkevitch, Lauretta Liem, and Norman Berke of the PESC, Environment and Climate Change Canada, North Vancouver, BC. P.D.E. thanks Faculty of Forestry, FPInnovations, Tolko and Viance for their support of his BC Leadership Chair at the University of British Columbia, and The Australian National University (ANU) for an Honorary Professorship in the Department of Applied Mathematics, Research School of Physics and Engineering at the ANU. REFERENCES 1.
A. Wiedenhoeft. Best Practice Guide for Forensic Timber Identification, English, Publishing and Library Section, United Nations Office at Vienna, 2016.
10 of 23
2.
P. Gasson, R. Miller, D.J. Stekel, F. Whinder, K. Ziemińska. Wood identification of Dalbergia nigra (CITES Appendix I) using quantitative wood anatomy, principal components analysis and naïve Bayes classification. Ann. Bot. 105 (2009) 45-56.
3.
G.C. Kite, P.W. Green, N.C. Veitch, M.C. Groves, P.E. Gasson, M.S. Simmonds. Dalnigrin, a neoflavonoid marker for the identification of Brazilian rosewood (Dalbergia nigra) in CITES enforcement. Phytochem. 71 (2010) 1122-1131.
4.
I. Hartvig, M. Czako, E. D. Kjær, L. R. Nielsen, I. Theilade. The use of DNA barcoding in identification and conservation of rosewood (Dalbergia spp.). PLoS One 10 (2015) e0138231.
5.
M. Yu, L. Jiao, J. Guo J, A.C. Wiedenhoeft, T. He, X. Jiang, Y. Yin. DNA barcoding of vouchered xylarium wood specimens of nine endangered Dalbergia species. Planta. 246 (2017) 1165-1176.
6.
E.E. Dormontt, M. Boner, B. Braun, G. Breulmann, B. Degen, E. Espinoza, S. Gardner, P. Guillery, J.C. Hermanson, G. Koch, S.L. Lee, M. Kanashiro, A. Rimbawanto, D. Thomas, A.C. Wiedenhoeft, Y. Yin, J. Zahnen, A.J. Lowe. Forensic timber identification: It‟s time to integrate disciplines to combat illegal logging, Biological Conservation 191 (2015) 790-798.
7.
F.A Snel, J.W. Braga, D. da Silva, A.C. Wiedenhoeft, A. Costa, R. Soares, V.T. Coradin, T.C. Pastore. Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES. Wood Sci. Technol. 52 (2018) 1411-1427.
8.
G. Shou, W. Zhang, Y. Gu, D. Zhao. Application of near infrared spectroscopy for discrimination of similar rare woods in the Chinese market. J. Near Infrared Spectroscopy. 22 (2014) 423-432.
9.
F. Wang, A. Huang, X. Yin, W. Wang, J. Chen. Multilevel profiling and identification of Dalbergia odorifera and Dalbergia stevensonii by FTIR, NMR and GC/MS. Chinese Chem. Lett. 29 (2018) 1395-1398.
10. X. Yin, A. Huang, S. Zhang, R. Liu, F. Ma. Identification of Three Dalbergia Species Based on Differences in Extractive Components. Molecules 23 (2018) 2163. 11. S.N. Wang, F.D. Zhang, A.M. Huang, Q. Zhou. Distinction of four Dalbergia species by FTIR, 2nd derivative IR, and 2D-IR spectroscopy of their ethanol-benzene extractives. Holzforschung 70 (2016) 503510. 12. P.D. Evans, I.A. Mundo, M.C. Wiemann, G.D. Chavarria, P. J. McClure, D. Voin, E.O. Espinoza. Identification of selected CITES-protected Araucariaceae using DART TOF MS. IAWA J. 38 (2017) 266281. 13. C. Lancaster, E. Espinoza. Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement. Rapid Commun. Mass Spectrom. 26 (2012) 1147-1156. 14. P.J. McClure, G.D. Chavarria, E. Espinoza. Metabolic chemotypes of CITES protected Dalbergia timbers from Africa, Madagascar, and Asia. Rapid Commun. Mass Spectrom. 29 (2015) 783-788. 15. K. Finch, E. Espinoza, F.A. Jones, R. Cronn. Source identification of western Oregon Douglas-Fir wood cores using mass spectrometry and random forest classification. Appl. Plant Sci. 5 (2017) 1600158.
11 of 23
16. R.A. Musah, E.O. Espinoza, R.B. Cody, A.D. Lesiak, E.D. Christensen, H.E. Moore, S. Maleknia, F.P. Drijfhout. A high throughput ambient mass spectrometric approach to species identification and classification from chemical fingerprint signatures. Sci. Rep. 5 (2015) 11520. 17. E.O. Espinoza, C.A. Lancaster, N.M. Kreitals, M. Hata, R.B. Cody, R.A. Blanchette. Distinguishing wild from cultivated agarwood (Aquilaria spp.) using direct analysis in real time and time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 28 (2014) 281-289. 18. E.O. Espinoza, M.C. Wiemann, J. Barajas-Morales, G.D. Chavarria, P.J. McClure. Forensic analysis of cites-protected Dalbergia timber from the Americas. IAWA J. 36 (2015) 311-325. 19. K. Paredes-Villanueva, E. Espinoza, J. Ottenburghs, M.G. Sterken, F. Bongers, P.A. Zuidema. Chemical differentiation of Bolivian Cedrela species as a tool to trace illegal timber trade. Forestry 00 (2018) 1-11. 20. C. Yang, Z. Wang, C.E. Brown, M. Landriault, Z. Yang, B. Hollebone, P. Lambert and G. Zhang. In Oil spill Environmental Forensics Case Studies, ed. S. Stout, Z. Wang, Elsevier Sci. & Technol. Ch. 3 (2017) 4965. 21. Z. Wang, S.A. Stout. Oil Spill Environmental Forensics: Fingerprinting and Source Identification. Elsevier, 2010. 22. C. Yang, M. Kim, C.E. Brown, Z. Wang, Z. Yang, B.P. Hollebone, P. Lambert. In Standard Handbook Oil Spill Environmental Forensics. Elsevier Ch. 9 (2016) 449-480. 23. A. Braga de Oliveira, O.R. Gottlieb, W.D. Ollis, C.T. Rizzini. A Phylogenetic correlation of the genera Dalbergia and Machaerium. Phytochemistry 10 (1971) 1863-1876. 24. C. Cornish, P. Gasson, M. Nesbitt. The wood collection (xylarium) of the Royal Botanic Gardens, Kew. IAWA J. 35 (2014) 85-104. 25. J. Dargavel, P.D. Evans, G. Dadswell. From science to heritage: the history of a wood collection. Historical Records of Aust. Sci. 25 (2014) 43-54. 26. G. Dadswell, J. Dargavel, P.D. Evans. Wood collections in Australia: a history of expansion and retraction. Aust. For. 78 (2015) 18-28. 27. Vatanparast M., Klitgaard B. B. Adema F.A.C.B., Pennington R.T., Yahara T., Kajita T.. First molecular phylogeny of the pantropical genus Dalbergia: Implications for infrageneric circumscription and biogeography. South African Journal of Botany 89 (2013) 143–149.
12 of 23
Conflict of Interest Statement There are no conflicts of interest to declare.
Author Contribution Statement Dayue Shang: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration. Pamela Brunswick: Methodology, Validation, Formal analysis, Investigation, Writing - Review & Editing, Visualization. Jeffrey Yan: Methodology, Software, Validation, Formal analysis, Investigation, Writing - Review & Editing, Visualization. Joy Bruno: Conceptualization, Supervision, Writing - Review & Editing, Project administration. Isabelle Duchesne: Resources, Writing - Review & Editing, Project administration. Nathalie Isabel: Resources, Writing - Review & Editing, Project administration. Graham VanAggelen: Writing - Review & Editing, Supervision, Funding acquisition. Marcus Kim: Conceptualization, Software, Resources. Philip D. Evans: Conceptualization, Resources, Writing - Review & Editing, Project administration.
13 of 23
Figure 1:
GC/QToF total ion chromatograms of Dalbergia spp. and look-alikes
14 of 23
123.009 m/z Figure 2:
134.074 m/z
GC/QToF extracted ion chromatograms at 123.009 m/z (left) and 134.074 m/z (right) for Dalbergia spp.
15 of 23
123.009 m/z Figure 3:
134.074 m/z
GC/QToF extracted ion chromatograms of ion 123.009 m/z and 134.074 m/z of three different sources of Dalbergia nigra (in color) and Dalbergia melanoxylon (black) species.
16 of 23
Figure 4:
Fingerprint of replicate Dalbergia nigra species from two different xylaria sources showing a pairing “match”. 17 of 23
18 of 23
Figure 5:
Fingerprint of replicate Dalbergia nigra versus Dalbergia melanoxylon from 5 different xylaria sources showing a pairing “no match”.
19 of 23
Figure 6:
Identification of an unknown sample versus replicate Dalbergia melanoxylon specimens from 5 different xylaria sources showing a pairing “match”. 20 of 23
Figure 7:
Fingerprint of replicate Dalbergia oliveri species versus replicate Dalbergia stevensonii from 5 different xylaria sources showing a “chemically related match”.
21 of 23
22 of 23
Figure 8:
Fingerprint of replicate Dalbergia sissoides species versus Dalbergia latifolia species from 7 different xylaria sources showing a “chemically related match”.
Figure 9:
Kernal Discriminant Analysis using the selected ID ions for Dalbergia sissoides, sissoo, and latifolia species (validation 91.89%).
23 of 23
Chemotyping and Identification of protected Dalbergia timber using gas chromatography quadrupole time of flight mass spectrometry Dayue Shang , Pamela Brunswick , Jeffrey Yan , Joy Bruno , Isabelle Duchesne , Nathalie Isabel , Graham VanAggelen , Marcus Kim , Philip D. Evans PII: DOI: Reference:
S0021-9673(19)31223-3 https://doi.org/10.1016/j.chroma.2019.460775 CHROMA 460775
To appear in:
Journal of Chromatography A
Received date: Revised date: Accepted date:
15 October 2019 3 December 2019 6 December 2019
Please cite this article as: Dayue Shang , Pamela Brunswick , Jeffrey Yan , Joy Bruno , Isabelle Duchesne , Nathalie Isabel , Graham VanAggelen , Marcus Kim , Philip D. Evans , Chemotyping and Identification of protected Dalbergia timber using gas chromatography quadrupole time of flight mass spectrometry, Journal of Chromatography A (2019), doi: https://doi.org/10.1016/j.chroma.2019.460775
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
Highlights • Timber extracts exploited using GC/QToF for chemotyping forensic analysis. • Differentiation and determination of wood achieved at genus/species level. • CITES listed wood species investigated and identified with the new method. • EI wood spectral library suitable for method transfer to routine environmental labs.
1 of 23
Chemotyping and Identification of protected Dalbergia timber using gas chromatography quadrupole time of flight mass spectrometry Dayue Shanga*, Pamela Brunswicka, Jeffrey Yana, Joy Bruno a, Isabelle Duchesneb, Nathalie Isabelb, Graham VanAggelena, Marcus Kimc and Philip D. Evansd,e a
Environment and Climate Change Canada (ECCC), North Vancouver, BC, Canada Natural Resources Canada, Canadian Forest Service, „Canadian Wood Fibre Centre, Québec City, QC, Canada Agilent Technologies Inc., Mississauga, ON, Canada d Faculty of Forestry, University of British Columbia (UBC), Vancouver, BC, Canada e Department of Applied Mathematics, Research School of Physics, The Australian National University, Canberra, Australia b c
ABSTRACT The international trade in illegally logged and environmentally endangered timber has spurred enforcement agencies to seek additional technical procedures for the identification of wood species. All Dalbergia species are listed under the Convention on International Trade in Endangered Species (CITES) which is the reason this genus was chosen for study. Multiple sources of the heartwood from different Dalbergia species were extracted and chromatographic profiles collected by gas chromatography with high resolution quadrupole Time of Flight mass spectrometry (GC/QToF). The collected data was mined to select peaks and mass ions representative of the investigated Dalbergia species, and used to develop a Microsoft Excel® template offering immediate graphical representation of the results. Using wood specimens sourced from different xylaria, this graphical fingerprint proved adept at definitive identification of Dalbergia species. The CITES Appendix I species, D. nigra, was easily distinguished from D. melanoxylon and look-alike species of other genera. Similarly, a number of other Dalbergia species were differentiated using this current approach. Kernel discrimination analysis (KDA) was applied to increase the confidence of the species identification. The mislabeling of specimens appears to be common, and the emerging technique of GC/QToF in combination with other techniques, offers improved confidence in identification. GC/QToF further provides automation, the dimension of chromatography to avoid interferences, and production of reproducible electron impact positive (EI+) spectra. The prospect of building an EI+ spectral database for future wood identification is an important feature considering the limited accessibility of authenticated wood species specimens. Keywords: Rosewood, Dalbergia, illegal logging, identification, GC/QToF, chemotyping
1.
Introduction
Illegal logging – the harvesting, processing, and trade of timber in violation of national laws -destroys forest ecosystems, deprives underdeveloped nations of income, and funds unlawful activities1. The timber species that are prohibited (or restricted) from trade are those listed in three appendices to an international treaty *
Corresponding author at: Environment and Climate Change Canada (ECCC), North Vancouver, BC, Canada Email address: [email protected] (Dayue Shang)
2 of 23
document known as the Convention on International Trade in Endangered Species of Wild Flora and Fauna (CITES): Appendix I lists species threatened with extinction, i.e., trade in these species is only permitted in exceptional circumstances; Appendix II lists species where trade is controlled to avoid unsustainable utilization, which if left unchecked, could lead to extinction; Appendix III lists species where trade is controlled in one or more countries, which may request assistance from other signatory countries to restrict trade in the listed species. In Canada, CITES is implemented under the Wild Animal and Plant Protection and Regulation of International and Interprovincial Trade Act (WAPPRIITA) and the Wild Animal and Plant Trade Regulations. The act of illegal import of wood circumvents payment of import duties that also undermines the Canadian economy and devalues local Canadian products. Many other countries also prohibit the importation of timber not obtained in accord with the laws of the country of origin, e.g., US Lacey Act (amended 2008); EU Timber Regulation (2010); and Australian Illegal Logging Prohibition Act (2012). The global impact of illegal logging has been of great concern for decades, but laws governing trade in illegally logged timber are difficult to enforce and criminal convictions are rare because of currently lacking the means to identify many protected timbers to a level of certainty required by customs or a court of law. For example, the rosewoods (Dalbergia spp.), which are listed in CITES Appendix I (Brazilian rosewood, Dalbergia nigra (Vell.) Allemão ex Benth) or Appendix II, need CITES permits to be traded in Canada including information on their scientific name (genus and species). Rosewoods are timber products of a rich red colour and are in great demand for musical instruments, furniture and carvings: they are the world‟s most commonly trafficked timber. „Rosewoods‟ can be assigned to the genus Dalbergia using light microscopy, but microscopy, even when combined with multivariate statistical analysis, cannot identify individual Dalbergia species, as required2. Hence, there is a pressing need for researchers to develop practical methods to meet law enforcement agencies‟ demand for identifying individual Dalbergia genera at species level. Because individual Dalbergia species cannot be identified by conventional taxonomic approaches using light microscopy, intensive research has been on going worldwide3-14. For example, a number of researchers initially developed techniques able to distinguish D. nigra (CITES Appendix I) from the other Dalbergia species (CITES Appendix II). Liquid chromatography/mass spectrometry achieved this goal by identifying a chemical marker, the neoflavanoid dalnigrin (6-hydroxy-7-methoxy-4-(4-methoxyphenyl)-2H-1-benzopyran -2-one (4′-Omethylmelanettin); which was present in D. nigra, but absent from some other Dalbergia species3. More recent research has focused on identification and separation of multiple individual Dalbergia species using other analytical techniques, and also DNA barcoding. DNA barcoding is capable of identifying multiple Dalbergia species, but it is hampered by the challenge of extracting good quality DNA from timber4,5,6. Several analytical techniques have also been entertained as a means of separating Dalbergia species, including spectroscopy (mid and near infra-red, and nuclear magnetic resonance spectroscopy), and gas chromatography/mass spectrometry (MS)7,8,9,10,11 and other forms of spectrometry, most notably, Direct Analysis in Real Time (DART) Time-of-Flight MS (DART/ToF). DART/ToF (AccuToFTM of JEOL Inc., hereafter JOEL ToF) is the only MS technique that has been widely tested, and it is currently accepted as the method of choice for identifying Dalbergia and other CITES listed species12-19. DART/JEOL ToF is not without its drawbacks, however, including potential interference by wood treatments (coatings and biocides), inability to automate analysis and, most importantly, the fact that the current DART/JEOL ToF mass spectral library derived from numerous authentic wood samples cannot be easily transferred to other ToF MS instruments due to the nature of
3 of 23
automorphic ionization of the DART design. A potential alternative to DART/JEOL ToF is GC/MS, which has only been tested on a handful of Dalbergia species, and has not used the full potential of GC/MS for forensic fingerprinting of wood9,10,11. The internationally recognized use of GC/MS in forensic fingerprinting of oil and petroleum products is much more advanced, and appears that the full potential of the GC/MS technique for the identification of timber has not been fully exploited 20. Over the years, oil spill researchers have found many characteristic ion patterns unique to different crude oils and petroleum products 15. Hence, we hypothesized that comparison of isobaric (and isomeric) ion patterns (IIP) of GC/QToF high resolution MS analysis, would be capable of distinguishing Dalbergia species. While recognizing the leap forward in forensic wood analysis achieved by DART/JEOL ToF chemotyping, the current work exploits the additional capabilities of chromatographic separation and EI based spectral database to provide a precise analytical procedure that can be easily adopted by routine analytical laboratories worldwide. 2.
Materials and Methods
2.1 Materials Methanol (HPLC-grade), ammonium hydroxide (≥99.99% purity) and formic acid (≥98% purity) were supplied by Sigma-Aldrich (Oakville, Ontario, Canada). HPLC grade isopropanol was purchased from Caledon Laboratories Ltd (Georgetown, ON). Wood samples were selected based on their listing in CITES appendices, similarity in appearance to listed species (look-alikes), or commercial importance. In total, 16 specimens from 11 species were obtained from wood collections (xylaria) located in Faculty of Forestry, University of British Columbia (Vancouver, Canada); Natural Resources Canada, Canadian Forestry Services (NRC-CFS); Jodrell Laboratory, Royal Botanic Gardens, Kew, London; and the Wildlife Enforcement Directorate, Environment and Climate Change Canada (WED, ECCC), (SI Table S1).
2.2 Sample preparation Thin slivers of heartwood were removed from xylarium specimens using a box knife or a chisel and razor saw. Note: wear anti-cut gloves when taking sample. Varnished surfaces and sapwood were avoided. The samples were weighed into ~15 mL glass tubes with Teflon lined lids. Each sample, approximately 50 to 100 mg of wood slivers, was extracted in 5 mL of methanol containing 1% v/v of formic acid. The sample was vigorously vortexed using a Fisher Scientific Multi-tube vortexer at a speed setting of 8 and extracted overnight at room temperature. After extraction, the samples were re-vortexed and centrifuged at 4645 xg for 2 min (or as required to settle solid materials), and a 1.5 mL aliquot of the supernatant was transferred by glass pipette to a GC vial for GC/QToF analysis. The remaining sample was stored at -40 ± 5 °C for future reference. 2.3 Instrument conditions and analysis Target compounds were analyzed using an Agilent 7890B GC interfaced to an Agilent 7250 QToF mass spectrometer. Typical operating conditions are summarized in SI Table S2, using 1 µL injection in pulsed splitless mode. GC separation was performed on a DB–5MS capillary column (Agilent Technologies, 30 m × 0.25 mm ID × 0.25 µm film thickness) or equivalent column. The GC oven was programmed at 50°C and held for 2
4 of 23
min, heated to 310°C at 6°C/min, and kept at maximum temperature for 8 min to remove higher boiling point compounds. A system suitability check was performed prior to each analytical sequence by external mass calibration using Perfluorotributylamine (PFTBA) tuning solution. Detection of the compounds in EI+ used centroid acquisition. Chromatographic peaks observed in total ion scan mode (TIC) were reviewed for patterns and characteristic mass ions. Peaks observed in blank control samples were excluded from the process. Mass spectral ions that were characteristic of the species were extracted from the TIC integrated for diagnostic ratio analysis. 2.4 Quality Control For each analytical sequence, a performance check solution consisted of 16 deuterated polycyclic aromatic hydrocarbons standard (SI Table S5) was run at the beginning, every 6 injections, and at end of the sequence to monitor any drifts of peak retention times or decrease in sensitivity. Additionally, to avoid retention time shifts throughout and between runs, a retention time lock method was implemented to ensure consistent peak selection during data analysis and processing. To eliminate any contaminant or background peaks, a baseline reagent and/or preparation blank background was collected with each batch. Any significant response in the blank background will be considered during data analysis. Furthermore, all wood samples were analyzed in duplicate to verify repeatability. During method development, sample extraction and analysis were duplicated eight times to confirm repeatability. 3.
Results and Discussion
3.1 Optimization of sample extraction Ideally, extraction solvent(s) for the chromatographic analysis of wood require a combination of properties: ability to penetrate into wood micropores; efficiency in extracting organic compounds; miscibility with wood containing moisture; suitability for long term storage e.g., at -40 °C; and compatibility with both LC/MS and GC/MS. We examined several solvents and their combinations to find out if they could fulfill these criteria3,10. The solvents studied included methanol (MeOH), ethanol, MeOH with 1% formic acid, MeOH with 1% ammonium hydroxide, binary solvent of MeOH with isopropanol at 3 compositions (25:75, 50:50, 75:25, v/v), and acidified binary solvent of MeOH with isopropanol and formic acid (75:24:1, v/v/v). The duration of extraction was also varied: 1, 2, 4, 8, 12, 24 and 48 hours. The results of the extraction experiments are shown in SI Table S3 and Figure S1. Based on the outcome of the experimentation, the solvent and extraction procedure selected for this study was methanol acidified with 1% formic acid and extraction for ≥12 h. The developed wood extraction procedure produced solutions of various hues corresponding to the timber being extracted. In particular, the darker woods of the CITES Appendix I listed Dalbergia nigra, produced dark brown colored solution that was notable. Based on visual observation of solution color, an initial laboratory identification process can begin. This observation is also under consideration for use in the field by enforcement officers, where rapid apprehension of CITES listed timbers is critical to stem the flow of illegal trafficking. A quick on-site extraction of slivers of wood taken at an inspection site can be compared with a set
5 of 23
of color-coded cards typical to common Dalbergia spp. and their look-alikes. The triaged wood extracts can be shipped to the authors‟ lab for confirmation. Our developed method provides field enforcement officers with a practical tool in determining if the suspected rosewood sample is worth apprehending for confirmatory analysis. 3.2 TIC chromatographic comparison of Dalbergia species It is well established that each wood species has its own distinct chemical fingerprint6. In our work, multiple Dalbergia species were analyzed by GC/QToF, together with two look-alike wood samples. All showed distinguishable chromatographic profiles in total ion chromatographic scan mode (TIC) in terms of retention time regions, patterns, and number of peaks (Figure 1). For reference, a blank TIC control is shown in SI (Figure S2). When the TIC of an unknown sample was compared visually with those of the reference woods belonging to different species, it was possible to rapidly hone in on the most similar species to the unknown specimen (Figure 1). Following the visual pattern recognition approach, a collection of over 100 Dalbergia and 50 other species‟ GC/QToF TICs were assembled for rapid screening of Dalbergia genus and species assignment (data not shown). With such an extended wood TIC database, it was possible to use the pattern recognition to distinguish between Dalbergia species and look-alike woods of a different genus, such as Diospyros and Cordia species (Figure 1). It is noted that spurious siloxane peaks sourced from the GC column were not considered during comparisons. In addition, saturation of high responding ions in the major chromatographic peaks for some species was unavoidable where lower responding chromatographic peaks were of interest. Furthermore, while members of the same Dalbergia species had similar TIC profiles, upon close examination, minor differences become apparent (Figure 1). These minor differences were likely due to intraspecific variation and length of time and conditions under which the sample was stored. The observed minor differences did not significantly affect the visual profiles for individual species, or the respective characteristic ions under the peaks of interest. This finding accords with those of recent publications that used low resolution GC/MS TIC profiles, together with FTIR, to identify Dalbergia species9,10,11. DART/JEOL ToF is currently the most well established chemotyping technique and extensive spectral libraries are being built around the statistical analysis of data collated from extensive wood sources 9,10,11. The DART system has a proven record when combined with JEOL ToF instrumentation, but the mass spectral library is not always compatible with analyses performed on ToF designs from other manufacturers. As a result of this, and the limitations placed on most analytical laboratories by the lack of extensive and vouchered wood libraries, an aim of the current work was to initiate an analytical method to support the DART/JEOL ToF approach, while offering mass spectral data comparable between laboratories. We suggest that the DART/JOEL ToF and GC/QToF techniques described here are complimentary to one another, each with their own advantages. While DART/JEOL ToF analysis time is rapid, it is hampered by an inability to automate the process. By comparison, a simple overnight sample extraction combined with GC/QToF analysis can be automated to run continuously even over a weekend. Compared to the DART/ToF method of chemotyping by direct mass spectrometry13,14,18, the use of GC/QToF offers an added dimension of chromatographic compound separation. This has the advantage of reducing potential interferences caused by contamination of wood samples with chemicals picked up during storage, sawing, and conversion into manufactured wood products. At this stage, the presented GC/QToF approach should be considered as a complimentary method of wood
6 of 23
species identification in support of the established DART/ToF analysis. Meanwhile, our results are noteworthy because ion fingerprints from GC/QToF in TIC could quickly identify genus and species, without the need for multivariate analysis of data as employed by DART/JEOL ToF and also avoiding potential interferences. 3.3 Extracted ion chromatographic (EIC) comparison of Dalbergia species Based on data in the literature, together with careful examination of multiple Dalbergia species and their wood look-alikes, many characteristic ions were identified in the mass spectra of the total ion chromatographic scan mode (TIC) retention time peaks. These mass ions were exploited for their isomeric and isobaric ion chromatographic patterns (IIPs) to be used as markers for species identification. For example, six characteristic chemotyping (species-specific) markers for D. nigra were identified at 123.009 m/z, 134.074 m/z, 178.065 m/z, 255.068 m/z, 270.092 m/z, and 274.090 m/z and their IIPs were compared to those of other Dalbergia species and look-alike woods (Figure 2, SI Figures S5 and S6). It is noted that the ions selected were not necessarily the highest responders in a peak spectrum but were based on ability to consistently perform as identifying ions. In this sense, the ions selected would not necessarily be indicative of a compound molecular mass. A visual assessment of selected ion chromatographic profiles was readily able to distinguish D. nigra from all of the other Dalbergia species, in particular, by strong single ion peaks that were not present at the same retention time in the other comparison species (Figure 2, SI Figures S5 and S6). Different sources of Dalbergia samples showed similarities within each species set, suggesting that while these 6 ions were specific to D. nigra, they were further useful in distinguishing other species. Similarly, other ions typical of other Dalbergia species were exploited further for this purpose (details to be discussed in the following Sections). Dalbergia nigra, a CITES Appendix I listed species, and Dalbergia melanoxylon, a CITES Appendix II listed species, can be similar in appearance, making visual identification difficult. With the aid of the 6 Dalbergia nigra specific ions and IIP, the current method could achieve a rapid and definitive differentiation between the two closely related species using 4 specimens of each species (Figure 3, SI Figures S7 and S8). Having analyzed over one hundred different species of wood extractives and several genera by GC/QToF (data not shown), we found that a combination of TIC and IIP chromatographic profiles was a powerful tool for identifying and separating closely related timber species. This view accords with the results of research that used LC separation with MS to distinguish D. nigra from other Dalbergia species3. Our expectation is that the currently reported GC/QToF method can be transferred easily to other laboratories because of the consistent ion fragmentation patterns of EI+. Thus, the ability to offer a characteristic, speciesspecific, library is a distinct advantage because of the logistical difficulties in obtaining vouched wood specimens. Such specimens are sourced from a limited number of xylaria around the world that are actively curated and hold comprehensive collections of CITES-listed species24,25,26. Our future aim is to build upon the presented wood species-specific information, and to enable any analytical laboratory with GC/QToF capability, with the ability to provide a wood species identification service in support of environmental protection. The building of an EI based wood spectral library is currently underway at the authors‟ lab.
7 of 23
3.4 Dalbergia species fingerprint identification While visual comparison of ion chromatograms may give a clear indication that a timber or a wood product is a “match” to vouchered specimens of a particular Dalbergia species, it may not be sufficient for forensic work. The careful review of multiple extracted ion chromatograms may be difficult to present in a legal court of law. A clearer picture can be gained by processing the data in the format of a fingerprint graph. Multiple sources of different Dalbergia species extracts analyzed by GC/QToF were used to select performing mass ions at specific retention times (referred to as ID ions) for all of the species as a group (SI Table S4). Species included multiple source samples of identified Dalbergia from xylaria (refer to acknowledgements), which included nigra, melanoxylon, retusa, latifolia, sissoo, stevensonii, oliveri, and limited samples of sissoides, spruceana, minahassae, and glaucenscens. The performing ion peaks were extracted and integrated, using Agilent Mass Hunter quantitative software, to provide a peak height response value. Resulting data was inserted into a template prepared in Microsoft Excel®, in which the integrated peak heights could be compared between two samples, or between the means of two samples, in a similar manner to the template currently used for oil spill forensic analysis20,21,22. The comparison showed peak height response on the y-axis and the identifying ion with retention time on the x-axis. Using this template, a rapid comparison could be made between a sample and potential species of interest for identification purposes. Since multiple ID ions have been included in the fingerprint template in order to encompass as many woods from Dalbergia genus as possible, there could be responses in the template that did not correspond. However, when a sample was compared with another of the same species, the “paired” fingerprint ID ions matched with fewer random unpaired ions presented in the figure. An example is given of a comparison between replicate extracts from two different sources of the same Dalbergia species, with replicates showing good “pairing” between the fingerprint ID ions (Figure 4, SI Figure S9). A match is observed by the “pairing” of bar responses in the graph for the sample (red) and comparison sample(s) (blue) for each particular ID ion. Extracts of different concentrations will show different intensity of response, but “paired” responses are the important factor for comparison rather than peak intensity. Different species have different numbers of performing ID ions, with some more available than others (Figure 4, SI Figure S9). To verify the applicability of the data processing method, the template was expanded to compare a single duplicate sample (e.g., unknown) with the mean response from groups of other known specimen sample replicates. Example is given for a typical D. nigra sample (red) versus D. melanoxylon replicates from 5 different xylaria sources (blue), with the graph showing a distinct “no match” (Figure 5). Further examples are given for a typical D. retusa (red) versus D. stevensonii (blue) from 5 different xylaria sources, with the graph again showing a “no match” (SI Figure S10). In contrast, an unknown sample was definitively identified as D. melanoxylon by a “match” with 5 different xylaria sources of the same species (Figure 6). While the paired ID ions are evident in the fingerprint graphs, it is noted that even within a single wood species, differences can occur due to intraspecific variation. This variation within a species is allowed for in the assessment of the fingerprint ID matching by selecting acceptable criteria. For the data collected to date, an acceptable “match” result was found to be ≥65 % pairing of the total observed ID ion responses being
8 of 23
considered. Fewer paired ID ion responses of 50-60 % total observed, was considered a “chemically related match”. A “no match” was assessed to be <50 % pairing of the total observed ID ion responses. These performing ID ions and criteria have been proposed based on current data, and with constant addition to our present wood species collection and the on-going building of the wood spectral library, the selection of ions and criteria will be subject to further adjustment for improved selectivity. 3.5 Chemically related Dalbergia species While the fingerprint template was rapidly able to identify the encompassed Dalbergia species, an interesting observation came to light during sample analysis. Some of the donated Dalbergia species appeared to be much more similar in chemotype fingerprint; namely, D. oliveri was similar to D. stevensonii, and D. sissoides and D. sissoo were similar to D. latifolia. A careful review of the fingerprint graphs showed that, while these species were almost identical, there were subtle differences and nuances. For example, different sources of D. oliveri matched well (SI Figure S3), while comparison with D. stevensonii consistently showed four distinctive unpaired responses at m/z 164 (RT 41.4), 229 (RT 37.5), 257 (RT 42.6), 259 (RT 48.3), 286 (RT 36.6), 300 (RT 35.7), and 300 (RT 42.5) (Figure 7). These unpaired responses were able to distinguish D. stevensonii from D. oliveri, although they are very similar chemically. A publication in 2013 reported the first molecular phylogenic study of the genus Dalbergia, concluding that Dalbergia has monophyletic ancestry; although the study did not reference D. stevensonii or D. sissoides27, so no comparison could be made in that regard. With respect to the D. sissoo and D. sissoides versus D. latifolia from six different xylaria sources, conclusive evidence was limited by availability of specimens. D. sissoo species could be distinguished from D. latifolia by distinctly higher responses at m/z 154 (RT 35.3), 154 (RT 38.2), 184 (RT 34.6), 195 (RT 32.8), 212 (RT 34.6), 221 (RT 36.3), 223 (RT 32.8), 253 (RT 36.3), 270 (RT 32.7), and 300 (RT 38.2) (SI Figure S11). The relationship between D. latifolia and D. sissoides was stronger and appeared as a “match” for the fingerprint pairing (Figure 8). While specimens of D. sissoides were limited, current data showed a potential differentiation by strong responses at m/z 270 (RT 32.7) and 282 (RT 38.9), plus other lower mass ions. This was upheld by a kernel discrimination analysis (KDA) of the selected ID ion data that showed distinct groupings for these three Dalbergia species with a 91.89% validation (Figure 9). The close relationship between these three species highlighted the potential for some wood species to be labelled incorrectly by single techniques. Knowing that statistical analysis of the fingerprint data (inclusive of chromatographic separation) can readily differentiate between very closely related species, we believe that this GC/QToF method could be used as a powerful tool for wood species differentiation and is worth further exploration. The mislabeling of specimens appears to be common, and the emerging technologies of DART/JEOL ToF13,14,18 together with GC/QToF in support of identification conclusions, offers a route to ensure agreement. 4.
Conclusion
A GC/QToF method was developed to analyze wood extracts for chemotyping in support of wood species identification relating to international trade in illegally logged endangered timber. While the well-established technique of DART/JEOL ToF has proven applicable, the current procedure has added the dimension of
9 of 23
chromatography to the profiling. The developed method consists of 3 steps for wood species identification. The first step is a visual comparison of GC/QToF chromatograms of both TIC and extracted ions, which provides an indication of the probable Dalbergia genera and species. The second step is to quarry data collected from GC/QToF runs and to select mass ions at specific retention times (referred to as ID ions) that are representative of the Dalbergia species as a group. Presentation of these performing ID ions in graphic fingerprint form leads to a rapid species identification in a format that would be more understandable to the general public and a court of law. For more challenging samples, the third step is to produce KDA plots with the ID ions which enable distinction between the chemotyping of similar species. The lack of authenticated wood specimens emphasizes a current problem for most laboratories worldwide. Considering the transferability of EI+ GC mass spectra, the current work focused on a readily transferable GC/QToF screening procedure that could be used either in support of DART/ToF results, or as an alternative. The application of statistical analysis to integrate GC/QToF data, inclusive of the added dimension of chromatographic separation, was shown to have significant potential for future development. Our current GC/QToF results presented by fingerprint graphs, demonstrated the close chemotype relationship between some species. Taken together with natural species intra- and inter-variability, this emphasizes the problems faced by all techniques in the identification process, as a result not only of the method, but on the availability of multiple specimens. The propensity for misidentification was noted even within the xylaria specimens collated for this study. The mislabeling of specimens appears to be common, and the emerging technique of GC/QToF in support of conclusions made by other techniques, offers improved confidence in an identification. ACKNOWLEDGEMENTS The authors gratefully acknowledge the initial training and generous assistance of Dr. Edgard Espinoza, Dr. Cady Lancaster, and Pamela J. McClure from the National Fish & Wildlife Forensic Lab, Ashland, Oregon. Wood reference materials were generously supplied by the Faculty of Forestry, University of British Columbia (UBC), FPInnovations, Dr. Peter Gasson of Jodrell Laboratory/Kew Gardens, Natural Resource Canada, Canadian Forestry Services (NRC-CFS), and the Wildlife Enforcement Directorate, Environment and Climate Change Canada (WED, ECCC). Jean-François Dubois of WED ECCC is acknowledged for his valuable technical support. DS, PB, JY, JB, and GV also thank the patient support of their colleagues, Honoria Kwok, Taylor Filewood, Liane Chow, Ceara MacInnis, Faith Park, Oxana Blajkevitch, Lauretta Liem, and Norman Berke of the PESC, Environment and Climate Change Canada, North Vancouver, BC. P.D.E. thanks Faculty of Forestry, FPInnovations, Tolko and Viance for their support of his BC Leadership Chair at the University of British Columbia, and The Australian National University (ANU) for an Honorary Professorship in the Department of Applied Mathematics, Research School of Physics and Engineering at the ANU. REFERENCES 1.
A. Wiedenhoeft. Best Practice Guide for Forensic Timber Identification, English, Publishing and Library Section, United Nations Office at Vienna, 2016.
10 of 23
2.
P. Gasson, R. Miller, D.J. Stekel, F. Whinder, K. Ziemińska. Wood identification of Dalbergia nigra (CITES Appendix I) using quantitative wood anatomy, principal components analysis and naïve Bayes classification. Ann. Bot. 105 (2009) 45-56.
3.
G.C. Kite, P.W. Green, N.C. Veitch, M.C. Groves, P.E. Gasson, M.S. Simmonds. Dalnigrin, a neoflavonoid marker for the identification of Brazilian rosewood (Dalbergia nigra) in CITES enforcement. Phytochem. 71 (2010) 1122-1131.
4.
I. Hartvig, M. Czako, E. D. Kjær, L. R. Nielsen, I. Theilade. The use of DNA barcoding in identification and conservation of rosewood (Dalbergia spp.). PLoS One 10 (2015) e0138231.
5.
M. Yu, L. Jiao, J. Guo J, A.C. Wiedenhoeft, T. He, X. Jiang, Y. Yin. DNA barcoding of vouchered xylarium wood specimens of nine endangered Dalbergia species. Planta. 246 (2017) 1165-1176.
6.
E.E. Dormontt, M. Boner, B. Braun, G. Breulmann, B. Degen, E. Espinoza, S. Gardner, P. Guillery, J.C. Hermanson, G. Koch, S.L. Lee, M. Kanashiro, A. Rimbawanto, D. Thomas, A.C. Wiedenhoeft, Y. Yin, J. Zahnen, A.J. Lowe. Forensic timber identification: It‟s time to integrate disciplines to combat illegal logging, Biological Conservation 191 (2015) 790-798.
7.
F.A Snel, J.W. Braga, D. da Silva, A.C. Wiedenhoeft, A. Costa, R. Soares, V.T. Coradin, T.C. Pastore. Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES. Wood Sci. Technol. 52 (2018) 1411-1427.
8.
G. Shou, W. Zhang, Y. Gu, D. Zhao. Application of near infrared spectroscopy for discrimination of similar rare woods in the Chinese market. J. Near Infrared Spectroscopy. 22 (2014) 423-432.
9.
F. Wang, A. Huang, X. Yin, W. Wang, J. Chen. Multilevel profiling and identification of Dalbergia odorifera and Dalbergia stevensonii by FTIR, NMR and GC/MS. Chinese Chem. Lett. 29 (2018) 1395-1398.
10. X. Yin, A. Huang, S. Zhang, R. Liu, F. Ma. Identification of Three Dalbergia Species Based on Differences in Extractive Components. Molecules 23 (2018) 2163. 11. S.N. Wang, F.D. Zhang, A.M. Huang, Q. Zhou. Distinction of four Dalbergia species by FTIR, 2nd derivative IR, and 2D-IR spectroscopy of their ethanol-benzene extractives. Holzforschung 70 (2016) 503510. 12. P.D. Evans, I.A. Mundo, M.C. Wiemann, G.D. Chavarria, P. J. McClure, D. Voin, E.O. Espinoza. Identification of selected CITES-protected Araucariaceae using DART TOF MS. IAWA J. 38 (2017) 266281. 13. C. Lancaster, E. Espinoza. Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement. Rapid Commun. Mass Spectrom. 26 (2012) 1147-1156. 14. P.J. McClure, G.D. Chavarria, E. Espinoza. Metabolic chemotypes of CITES protected Dalbergia timbers from Africa, Madagascar, and Asia. Rapid Commun. Mass Spectrom. 29 (2015) 783-788. 15. K. Finch, E. Espinoza, F.A. Jones, R. Cronn. Source identification of western Oregon Douglas-Fir wood cores using mass spectrometry and random forest classification. Appl. Plant Sci. 5 (2017) 1600158.
11 of 23
16. R.A. Musah, E.O. Espinoza, R.B. Cody, A.D. Lesiak, E.D. Christensen, H.E. Moore, S. Maleknia, F.P. Drijfhout. A high throughput ambient mass spectrometric approach to species identification and classification from chemical fingerprint signatures. Sci. Rep. 5 (2015) 11520. 17. E.O. Espinoza, C.A. Lancaster, N.M. Kreitals, M. Hata, R.B. Cody, R.A. Blanchette. Distinguishing wild from cultivated agarwood (Aquilaria spp.) using direct analysis in real time and time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 28 (2014) 281-289. 18. E.O. Espinoza, M.C. Wiemann, J. Barajas-Morales, G.D. Chavarria, P.J. McClure. Forensic analysis of cites-protected Dalbergia timber from the Americas. IAWA J. 36 (2015) 311-325. 19. K. Paredes-Villanueva, E. Espinoza, J. Ottenburghs, M.G. Sterken, F. Bongers, P.A. Zuidema. Chemical differentiation of Bolivian Cedrela species as a tool to trace illegal timber trade. Forestry 00 (2018) 1-11. 20. C. Yang, Z. Wang, C.E. Brown, M. Landriault, Z. Yang, B. Hollebone, P. Lambert and G. Zhang. In Oil spill Environmental Forensics Case Studies, ed. S. Stout, Z. Wang, Elsevier Sci. & Technol. Ch. 3 (2017) 4965. 21. Z. Wang, S.A. Stout. Oil Spill Environmental Forensics: Fingerprinting and Source Identification. Elsevier, 2010. 22. C. Yang, M. Kim, C.E. Brown, Z. Wang, Z. Yang, B.P. Hollebone, P. Lambert. In Standard Handbook Oil Spill Environmental Forensics. Elsevier Ch. 9 (2016) 449-480. 23. A. Braga de Oliveira, O.R. Gottlieb, W.D. Ollis, C.T. Rizzini. A Phylogenetic correlation of the genera Dalbergia and Machaerium. Phytochemistry 10 (1971) 1863-1876. 24. C. Cornish, P. Gasson, M. Nesbitt. The wood collection (xylarium) of the Royal Botanic Gardens, Kew. IAWA J. 35 (2014) 85-104. 25. J. Dargavel, P.D. Evans, G. Dadswell. From science to heritage: the history of a wood collection. Historical Records of Aust. Sci. 25 (2014) 43-54. 26. G. Dadswell, J. Dargavel, P.D. Evans. Wood collections in Australia: a history of expansion and retraction. Aust. For. 78 (2015) 18-28. 27. Vatanparast M., Klitgaard B. B. Adema F.A.C.B., Pennington R.T., Yahara T., Kajita T.. First molecular phylogeny of the pantropical genus Dalbergia: Implications for infrageneric circumscription and biogeography. South African Journal of Botany 89 (2013) 143–149.
12 of 23
Conflict of Interest Statement There are no conflicts of interest to declare.
Author Contribution Statement Dayue Shang: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration. Pamela Brunswick: Methodology, Validation, Formal analysis, Investigation, Writing - Review & Editing, Visualization. Jeffrey Yan: Methodology, Software, Validation, Formal analysis, Investigation, Writing - Review & Editing, Visualization. Joy Bruno: Conceptualization, Supervision, Writing - Review & Editing, Project administration. Isabelle Duchesne: Resources, Writing - Review & Editing, Project administration. Nathalie Isabel: Resources, Writing - Review & Editing, Project administration. Graham VanAggelen: Writing - Review & Editing, Supervision, Funding acquisition. Marcus Kim: Conceptualization, Software, Resources. Philip D. Evans: Conceptualization, Resources, Writing - Review & Editing, Project administration.
13 of 23
Figure 1:
GC/QToF total ion chromatograms of Dalbergia spp. and look-alikes
14 of 23
123.009 m/z Figure 2:
134.074 m/z
GC/QToF extracted ion chromatograms at 123.009 m/z (left) and 134.074 m/z (right) for Dalbergia spp.
15 of 23
123.009 m/z Figure 3:
134.074 m/z
GC/QToF extracted ion chromatograms of ion 123.009 m/z and 134.074 m/z of three different sources of Dalbergia nigra (in color) and Dalbergia melanoxylon (black) species.
16 of 23
Figure 4:
Fingerprint of replicate Dalbergia nigra species from two different xylaria sources showing a pairing “match”. 17 of 23
18 of 23
Figure 5:
Fingerprint of replicate Dalbergia nigra versus Dalbergia melanoxylon from 5 different xylaria sources showing a pairing “no match”.
19 of 23
Figure 6:
Identification of an unknown sample versus replicate Dalbergia melanoxylon specimens from 5 different xylaria sources showing a pairing “match”. 20 of 23
Figure 7:
Fingerprint of replicate Dalbergia oliveri species versus replicate Dalbergia stevensonii from 5 different xylaria sources showing a “chemically related match”.
21 of 23
22 of 23
Figure 8:
Fingerprint of replicate Dalbergia sissoides species versus Dalbergia latifolia species from 7 different xylaria sources showing a “chemically related match”.
Figure 9:
Kernal Discriminant Analysis using the selected ID ions for Dalbergia sissoides, sissoo, and latifolia species (validation 91.89%).
23 of 23