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Official Journal of the Japan Wood Research Society

Geographical origin classification of Phoebe zhennan and Phoebe bournei by solid phase micro-extraction and gas chromatography-mass spectrometry


Phoebe zhennan and Phoebe bournei are favored for their fine-grain, insect resistance, special fragrance, not easily cracked and deformed, which had been widely used in furniture and construction. Wood authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. Due to the difference in wood quality characteristics and economic value of genus Phoebe species from different geographical origins, it is important to establish a fast, efficient, and reliable method to discriminate the geographical origin of Phoebe zhennan and Phoebe bournei. Solid phase micro-extraction (SPME) was used as sample preparation approach and gas chromatography-mass spectrometry (GC–MS) technique was employed to analyze the volatile compounds. As a result, a total of 40 volatiles were identified by SPME–GC–MS among Phoebe zhennan samples from four origins, while 34 substances identified among Phoebe bournei samples. The wood samples of Phoebe zhennan and Phoebe bournei from different major production areas in China were able be discriminated by GC–MS spectroscopy. This finding indicated that genus Phoebe species from different climate zones had different chemical composition, which verified that the SPME–GC–MS technique can be used to classify different species of wood with characteristic compounds.


Phoebe zhennan and Phoebe bournei are trees belonging to the genus Phoebe Nees of the Lauraceae family [1]. Phoebe zhennan is found mainly in western Hubei, northwest Guizhou, and Sichuan, while Phoebe bournei is usually cultivated in Guangdong, Fujian, Guangxi, and Guizhou. These two kinds of wood are favored for their fine-grain, insect resistance, special fragrance, not easily cracked and deformed, which had been widely used in furniture and construction [2, 3]. The wood properties of Phoebe zhennan and Phoebe bournei depend on the geographical origin, growing environment and cultivation method. It is commonly believed that the Phoebe zhennan population from Sichuan Province showed the best wood quality traits and have the highest price in comparison to all origins[4]. With the rapid globalization of wood trade, the issue of wood authenticity has become an urgent and worldwide problem, such as the confusion of varieties and origins.

According to our findings, the macrostructural and microstructural differences of genus Phoebe species from different geographical origins are not obvious. It is very difficult for non-professional identifiers to distinguish among them. However, both Phoebe zhennan and Phoebe bournei have a special odor, making them suitable for the analysis by gas chromatography–mass spectrometry (GC–MS) approach [5]. The technique of GC–MS has been widely employed to analyze complex and unknown samples because of its rapid, sensitivity, strong qualitative and other superior traits. Besides, solid phase microextraction (SPME) technique has been employed to extract volatile compounds. SPME was considered as a fast economical sensitive and solvent free volatile extraction method before the GC–MS analysis. In recent years, the SPME–GC–MS technique has been used analyzing the volatile profiles of wood samples [6]. Chemometric has also been used to predict some wood qualities and properties, for example authenticity of oak [7,8,9], and quality of eaglewood [10].

In this study, the technique of SPME- GC–MS was used to identify volatile compounds in Phoebe zhennan and Phoebe bournei samples. Meanwhile, the datasets of GC–MS were subjected to chemometric analyses to identify the geographical origins of Phoebe zhennan. The proposed SPME–GC–MS will provide an accurate and quick way for the geographical origin identification of genus Phoebe species.

Materials and methods


The Latin names, Chinese names, and information on sampling location are presented in Table 1. The samples were obtained from China National Forestry and Grassland Administration Wildlife Criminal Evidence Identification Center (Nanjing Forest police college), collected from natural plant populations. Phoebe zhennan samples (130 samples) were collected from Hubei Province, Sichuan Province, Yunnan Province, and Guizhou Province, respectively, in China. Phoebe bournei samples (45 samples) were collected from Guizhou Province, Hubei Province, and Guangxi Province. The sampled trees had an average diameter at breast of 21.7 cm and were from 18 to 28 years old. From each tree, one cores was extracted at 1.3 m stem height by a 12-mm-diameter borer. Wood samples were chopped into pieces and finely powdered to pass a 40-mesh sieve. Then, 1.5 g of wood powder was added into a headspace bottle and extracted by SPME technique.

Table 1 Listing of samples for testing

We declare that this study has the official permission to collect the plant sample and complies with the Chinese legislation. Wood species have been identified by Xiaoming Xue (based on phenotypic characteristics).

GC–MS instrument and conditions

GC–MS analysis was performed on an Agilent 7000B System (Agilent Technologies, USA). Separation was achieved using a DB-5MS column (length: 30 m, inner diameter: 0.25 mm, film thickness: 0.25 micro meter) with a temperature program shown in Table 2. Helium was used as the carrier gas with the gas flow rate through the column was 1 mL/min The mass spectrometry program was set to electron impact ionization mode, energy 70 eV, mass scanning range 33–450 amu and full scanning mode. Compounds were identified via the National Institute of Standards and Technology (NIST17; Gaithersburg, MD, USA) Mass Spectral Library. A mixture of 1 μL n-alkane (C7–C25) was run under the same capillary column and GC–MS conditions to obtain the retention index (RI). The peak area was chosen as the analytical signal for the relative amount. The relative contents of each volatile were calculated by area normalization, and the average value of all tested wood samples was taken.

Table 2 Temperature program setting

Data analysis

The Random Forest (RF) model and k-nearest neighbors (KNN) model were used to classify Phoebe zhennan samples by MATLAB R2018b (MathWorks Inc., Natick, MA, USA). The dataset was randomly divided into a training set (70%) and a test set (30%). Optimal parameters adjust and five-fold cross-validation was performed by Toolbox 802 (Eigenvector Research, Inc., Manson, WA).

The RF algorithm was proposed by Breiman in 2001. This method based on a set of decision trees formed using bootstrap samples from a learning data set and can solve multi-classification and regression problems [11]. The final classification is determined by calculating the predicted frequency of each group in the entire forest. RF has achieved high performance in the classification of woods [12], foods [13], pests [14], pesticide [15], etc. In this study, we explore the potential of RF for modeling GC–MS data of Phoebe zhennan samples for identifying.

Besides the RF algorithm, the KNN are also used in modeling the same GC–MS data for comparison. As a well-known supervised pattern recognition method, KNN is based on the principle that the majority of the most similar samples of one sample belong to a certain category, this sample also belongs to this category [16]. This method is not limited by the number of categories and has proven to be suitable for GC–MS data analysis [17].

Due to the insufficient number of Phoebe bournei samples, its GC–MS data were not used for model building.

Results and discussion

Results of GC–MS analysis

The volatile compounds extracted from Phoebe zhennan and Phoebe bournei were separated by GC–MS, in total 51 kinds of chemical composition (peak area above 0.5%) were scrutinized (Tables 3, 4), including alkanes, olefins, alcohols, naphthalene, and aldehydes compounds.

Table 3 Chemical composition of extractives analyzed by GC–MS (Phoebe zhennan)
Table 4 Prediction accuracy of RF and KNN models for Phoebe zhennan samples based on their GC–MS full-spectrum data

Figure 1 shows the total ion chromatograms of Phoebe zhennan, the results clearly exhibited the different characteristic peaks of the samples from four geographical origins. The characteristic peaks of the wood samples from Hubei and Sichuan mainly appeared between 6 and 25 min while the samples from Yunnan and Guizhou mainly appeared between 9 and 25 min. We observed that the spectra of wood samples from Yunnan and Guizhou could not be used for geographical origin classification. We believe that the samples from Sichuan province can be well distinguished by the total ion chromatograms. The quality of Phoebe zhennan from Sichuan production areas is different from that produced in surrounding areas due to different geographical and climatic conditions [4]. Therefore, the accurate identification of samples from this region is the main focus of this study.

Fig. 1
figure 1

GC–MS total ion chromatogram of Phoebe zhennan. a GC–MS total ion chromatogram of specimens from Hubei. b GC–MS total ion chromatogram of specimens from Sichuan. c GC–MS total ion chromatogram of specimens from Yunnan. d GC–MS total ion chromatogram of specimens from Guizhou

Figure 2 shows the total ion chromatogram of Phoebe bournei obtained from GC–MS, it can be seen that the characteristic peaks of all specimens mainly appeared between 9 and 24 min, but the intensity of the peaks differed among the three geographical origins. Results demonstrated that three geographical origins of Phoebe bournei can be readily distinguished by visual inspection. In total, 34 volatiles were identified and presented in Table 5.

Fig. 2
figure 2

GC–MS total ion chromatogram of Phoebe bournei. a GC–MS total ion chromatogram of specimens from Guizhou. b GC–MS total ion chromatogram of specimens from Hubei. c GC–MS total ion chromatogram of specimens from Guangxi

Table 5 Chemical composition of extractives analyzed by GC–MS (Phoebe bournei)

The difference analysis among four geographical origins of Phoebe zhennan

The retention time, chemical name, relative content, RI, and CAS number are presented in Table 3. Wood samples of Phoebe zhennan from Hubei, Sichuan, Yunnan, and Guizhou Province were extracted by SPME technique. In this study, 40 kinds of chemical composition have been identified in wood samples.

Among all the identified compounds, verbenone was the constitute found with the highest proportion in the Phoebe zhennan samples of the total chromatogram area. The (E)-verbenol and α-copaene, were the second and the third most abundant volatiles, respectively. The relative content of α-cubebene, α-ylangene, β-elemene, aristolene, γ-muurolene, δ-cadinene, and α-agarofuran was significantly different in Phoebe zhennan samples from the four geographical origins. Six compounds were identified only in wood samples from Yunnan and Guizhou: (E)-verbenol, Cedrol, β-eudesmol, γ-eudesmol, agarospirol and valerianol. It suggests that these substances may be thought of as characteristic components for the origin classification of Phoebe zhennan in Yunnan and Guizhou. Closer inspection of the Table 3 revealed that a small difference in the composition of volatile compounds between samples from Yunnan and Guizhou, whereas the difference between other geographical origins is more evident.

The full-spectrum GC–MS data of Phoebe zhennan samples were used to establish RF and KNN models. Optimal parameters were selected according to the principle of the cross-validation and minimum discrimination rate. The highest discrimination rate of KNN was obtained when k = 1 (single nearest neighbor). The classification results of RF model and KNN model are shown in Table 4. In the RF model and KNN model, the classification rates of the training set and the test set for the Hubei and Sichuan samples were both 100%. The RF model training set classification rate for Yunnan samples was 71%, for Guizhou samples was 88%. While the KNN model training set classification rate for Yunnan samples was 57%, for Guizhou samples was 81%. These results showed that the Phoebe zhennan samples from Hubei and Sichuan can be accurately classified using RF and KNN models.

The sampling sites in Guizhou and Yunnan Province are located in Yunnan-Guizhou Plateau and exhibit typical karst landforms [18]. Being located in the southwestern of China, Yunnan-Guizhou Plateau possesses a short sunshine duration and low sunshine intensity. This region is located in a typical monsoon climate zone, with the highest moisture content in summer and the lowest moisture in winter. The monsoon’s strength or weakness, and advancement or retreat, often result in the occurrence of flooding or droughts [19]. The climatic characteristics of sampling sites in Yunnan and Guizhou are similar [20]. Meanwhile, the main soil type at Yunnan and Guizhou is red soil, mainly derived from limestone parent material [21], which is different from other sampling sites.

Therefore, it is unreasonable to divide Phoebe zhennan provenance through administrative regions. Because of the close geographical proximity and the similar climatic conditions, it seems reasonable to combine wood samples from Yunnan and Guizhou into a single category.

The difference analysis among three geographical origins of Phoebe bournei

The relative content of the same ingredient in Phoebe bournei samples from the three geographical origins were different. There were 25 common volatile compounds among the three kinds of wood specimens (Table 5). The main components of Phoebe bournei samples from Guangxi were verbenone (13.07%), (E)-verbenol (8.42%), α-copaene (6.97%), valerianol (6.85%), mustakone (6.80%), and agarospirol (6.04%), while Guizhou origins were verbenone (13.09%), (E)-verbenol (10.24%) and agarospirol (6.40%). Analysis also revealed the presence of compounds in Hubei origin with verbenone (19.63%), (E)-verbenol (13.52%) and thujopsene (6.49%) being the most predominant.

In addition to these volatiles, it is worth noting the presence of borneol, Cubenol, Ylangenal, since these unique compounds can be used as evidence for the classification of the tree origins. cubenol was found exclusively in Phoebe bournei of Guangxi origin and borneol was only found in Guizhou origin, a geographical classification could be achieved using a detect of these compounds.

Unlike Phoebe zhennan, the chemical composition of Phoebe bournei samples taken from different sampling locations showed significant variation, probably due to climate factors and genetic diversity existing within plant populations. Overall, GC–MS analyzing and comparing characteristic components of fingerprint can provide theory basis for the geographical origin classification of Phoebe bournei.

The difference analysis between Phoebe zhennan and Phoebe bournei

Phoebe zhennan was considered to be the most authentic Gold Phoebe, which has a much greater economic value than Phoebe bournei. As shown in Table 3 and Table 5, the relative contents in Phoebe zhennan samples were characterized by higher levels of α-cubebene and cedrol compared to Phoebe bournei, which allowed the differentiation between these two species. Interestingly, a high content of compounds such as verbenone and α-copaene could not be used as discriminating variables, since not all origins of genus Phoebe contained these substances.

To distinguish Phoebe zhennan and Phoebe bournei, the volatiles only detected in one species were defined as characteristic compounds. As shown in Table 6 and Table 7, 17 kinds of specific phytoconstituents were identified in Phoebe zhennan wood samples, while 12 kinds of volatiles were only identified and treated as characteristic compounds in Phoebe bournei. In summary, Phoebe zhennan and Phoebe bournei can be classified successfully according to chemical composition.

Table 6 Unique components extracted from Phoebe zhennan
Table 7 Unique components extracted from Phoebe bournei


In this study, SPME–GC–MS technique has been used to analyze Phoebe zhennan and Phoebe bournei wood samples. In total, 40 volatiles were identified in Phoebe zhennan and 34 substances identified in Phoebe bournei, these phytoconstituents were classified into alkanes, olefins, alcohols, naphthalene, and aldehydes compounds, with verbenone showed the highest relative content in both species.

The geographical origins of Phoebe zhennan and Phoebe bournei were well-separated based on GC–MS analysis, which indicated that volatile compounds play an important role in the geographical discrimination of wood samples. Meanwhile, our experimental data suggested that the chemical composition of Phoebe zhennan from Yunnan and Guizhou are highly similar. It seems reasonable to combine Yunnan origin and Guizhou origin into a single category. Using the content of characteristic compounds combined with the RF or KNN models is an accurate and effective method to identify the geographic origin of Phoebe zhennan.

Because Phoebe zhennan and Phoebe bournei wood are expensive, it’s valuable to establish a rapid and micro-destructive classification method. Although the prediction of Phoebe zhennan and Phoebe bournei by GC–MS technique and statistical analysis obtained high accuracy, given our limited number of samples, more validation studies are still necessary. Meanwhile, the composition of chemical substances in plants is determined by a combination of intrinsic and extrinsic factors, and how to define geographical boundaries between different plant populations is still a question worthy of further study.

We also believe that the wood geographical origin classification would be much more accuracy if more detailed analyses are performed. Therefore, it is necessary to combine chemical constituents of woods with other wood properties (such as vessels distribution, ring width or density, etc.) to make sure the classification results are accurate and satisfactorily reliable.

Availability of data and materials

The datasets generated and analyzed during the current study are available in the Figshare repository, [].



Solid phase micro-extraction


Gas chromatography-mass spectrometry


Retention index


Random Forest


K-nearest neighbors


  1. Zhang Q, Ning L, Ding W, Xing H, Zhou Q, Wei Z (2019) Chemical constituents of the essential oil extracted from Phoebe bournei. Chem Nat Compd 55:754–755.

    Article  CAS  Google Scholar 

  2. Pan F, Chen L, Jiang Y, Xiong L, Min L, Xie J, Qi J, Xiao H, Chen Y, De Hoop CF (2018) Bio-based UV protective films prepared with polylactic acid (PLA) and Phoebe zhennan extractives. Int J Biol Macromol 119:582–587.

    Article  CAS  PubMed  Google Scholar 

  3. Wang J, Ning L, Gao Q, Zhang S, Chen Q (2019) The identification and characteristics of Phoebe zhennan buried wood. IAWA J 40:804–819.

    Article  Google Scholar 

  4. Xiao J-H, Ding X, Li L, Ma H, Ci X-Q, van der Merwe M, Conran JG, Li J (2020) Miocene diversification of a golden-thread nanmu tree species (Phoebe zhennan, Lauraceae) around the Sichuan Basin shaped by the East Asian monsoon. Ecol Evol 10:10543–10557

    Article  PubMed  PubMed Central  Google Scholar 

  5. Xie J, Qi J, Huang X, Zhou N, Hu Y (2015) Comparative analysis of modern and ancient buried Phoebe zhennan wood: surface color, chemical components, infrared spectroscopy, and essential oil composition. J For Res 26:501–507.

    Article  CAS  Google Scholar 

  6. Xu B, Zhu T, Li J, Liu S (2013) Identification of wood between Phoebe zhennan and Machilus pingii using the gas chromatography-mass spectrometry direct injection technique. Eur J Mass Spectrom (Chichester) 19:187–193.

    Article  CAS  PubMed  Google Scholar 

  7. Duval CJ, Sok N, Laroche J, Gourrat K, Prida A, Lequin S, Chassagne D, Gougeon RD (2013) Dry vs soaked wood: modulating the volatile extractible fraction of oak wood by heat treatments. Food Chem 138:270–277.

    Article  CAS  PubMed  Google Scholar 

  8. Duval CJ, Gourrat K, Perre P, Prida A, Gougeon RD (2013) A HS–SPME–GC–MS analysis of IR heated wood: Impact of the water content on the depth profile of oak wood aromas extractability. Food Res Int 54:277–284

    Article  CAS  Google Scholar 

  9. Vichi S, Santini C, Natali N, Riponi C, Lopez-Tamames E, Buxaderas S (2007) Volatile and semi-volatile components of oak wood chips analysed by accelerated solvent extraction (ASE) coupled to gas chromatography–mass spectrometry (GC–MS). Food Chem 102:1260–1269

    Article  CAS  Google Scholar 

  10. He ZZ, Fan YM, Xie XM, Yuan GF, Chen ZX (2012) GC-MS analysis of fragrance constituents from offcuts of eaglewood and Chinese Eaglewood in Guangdong. In: Advanced Materials Research. Trans Tech Publ, pp 1904–1907

  11. Breiman L (2001) Random forests. Mach Learn 45:5–32.

    Article  Google Scholar 

  12. Jiang Y, Zhang X, Zhang J, Chhin S (2022) Comparing the random forest algorithm with other modelling approaches to capture the complex patterns of intra-annual wood formation of Chinese fir with different ages. Dendrochronologia 77:126043.

  13. Mascellani A, Hoca G, Babisz M, Krska P, Kloucek P, Havlik J (2021) 1H NMR chemometric models for classification of Czech wine type and variety. Food Chem 339:127852

    Article  CAS  PubMed  Google Scholar 

  14. Li Y, Ercisli S (2023) Data-efficient crop pest recognition based on KNN distance entropy. Sustain Comput 38:100860

    Google Scholar 

  15. Zhu J, Sharma AS, Xu J, Xu Y, Jiao T, Ouyang Q, Li H, Chen Q (2021) Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering. Spectrochim Acta Part A Mol Biomol Spectrosc 246:118994

    Article  CAS  Google Scholar 

  16. Wang F, Zhao H, Yu C, Tang J, Wu W, Yang Q (2020) Determination of the geographical origin of maize (Zea mays L.) using mineral element fingerprints. J Sci Food Agric 100:1294–1300

    Article  CAS  PubMed  Google Scholar 

  17. Yu D, Zhang X, Guo S, Yan H, Wang J, Zhou J, Yang J, Duan J-A (2022) Headspace GC/MS and fast GC e-nose combined with chemometric analysis to identify the varieties and geographical origins of ginger (Zingiber officinale Roscoe). Food Chem 396:133672

    Article  CAS  PubMed  Google Scholar 

  18. Cheng Q, Gao L, Zhong F, Zuo X, Ma M (2020) Spatiotemporal variations of drought in the Yunnan-Guizhou Plateau, southwest China, during 1960–2013 and their association with large-scale circulations and historical records. Ecol Ind 112:106041

    Article  Google Scholar 

  19. Zheng JM, Zhang WC, Chen Y, Ma T (2015) Analysis on climatologic characteristics and causes of the severe drought during 2009–2010 in Yunnan Province. J Meteorol Sci 35:488–496

    Google Scholar 

  20. Zhijia G, Xingwu D, Bing L, Jinming H, Jiaonan H (2016) The spatial distribution and temporal variation of rainfall erosivity in the Yunnan Plateau, Southwest China: 1960–2012. CATENA 145:291–300

    Article  Google Scholar 

  21. Liu W-J, Liu C-Q, Zhao Z-Q, Xu Z-F, Liang C-S, Li L, Feng J-Y (2013) Elemental and strontium isotopic geochemistry of the soil profiles developed on limestone and sandstone in karstic terrain on Yunnan-Guizhou Plateau, China: implications for chemical weathering and parent materials. J Asian Earth Sci 67:138–152

    Article  Google Scholar 

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Jiangsu Province Forestry Science and Technology Innovation and Extension Project: Research on propagation and efficient cultivation of precious green wood species of Phoebe (LYKJ【2019】46); Excellent scientific and technological innovation team of higher education in Jiangsu Province(2019–29); National College Student Innovation Training Programme(202112213019Z); Key Laboratory of State Forest and Grassland Administration on Wildlife Evidence Technology.

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RC and ZG completed the experiments for the text; ZC and HW wrote the main manuscript text and prepared all figures; XX presented the idea of the text; HG revised the text.

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Correspondence to Xiaoming Xue.

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Chen, Z., Xue, X., Cheng, R. et al. Geographical origin classification of Phoebe zhennan and Phoebe bournei by solid phase micro-extraction and gas chromatography-mass spectrometry. J Wood Sci 69, 21 (2023).

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  • SPME
  • GC–MS
  • Geographical origin
  • Fingerprint analysis
  • Wood