Journal of Applied Spectroscopy

, Volume 82, Issue 4, pp 628–633 | Cite as

Quantification of Cannabinoid Content in Cannabis

  • Y. Tian
  • F. Zhang
  • K. Jia
  • M. Wen
  • Ch. Yuan

Cannabis is an economically important plant that is used in many fields, in addition to being the most commonly consumed illicit drug worldwide. Monitoring the spatial distribution of cannabis cultivation and judging whether it is drug- or fiber-type cannabis is critical for governments and international communities to understand the scale of the illegal drug trade. The aim of this study was to investigate whether the cannabinoids content in cannabis could be spectrally quantified using a spectrometer and to identify the optimal wavebands for quantifying the cannabinoid content. Spectral reflectance data of dried cannabis leaf samples and the cannabis canopy were measured in the laboratory and in the field, respectively. Correlation analysis and the stepwise multivariate regression method were used to select the optimal wavebands for cannabinoid content quantification based on the laboratory-measured spectral data. The results indicated that the delta-9-tetrahydrocannabinol (THC) content in cannabis leaves could be quantified using laboratory-measured spectral reflectance data and that the 695 nm band is the optimal band for THC content quantification. This study provides prerequisite information for designing spectral equipment to enable immediate quantification of THC content in cannabis and to discriminate drug- from fiber-type cannabis based on THC content quantification in the field.


cannabis spectral data cannabinoids content delta-9-tetrahydrocannabinol 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Y. Tian, K. Jia, B. Wu, and Q. Li, Spectrosc. Spect. Anal., 30, 3334–3337 (2010).Google Scholar
  2. 2.
    P. Fusar-Poli, J. A. Crippa, S. Bhattacharyya, S. J. Borgwardt, P. Allen, R. Martin-Santos, M. Seal, S. A. Surguladze, C. O'Carrol, Z. Atakan, A. W. Zuardi, and P. K. McGuire, Arch. Gen. Psychiatry, 66, 95–105 (2009).CrossRefGoogle Scholar
  3. 3.
    R. Johnson, Determining if Cannabis is Drug or Fiber Type, (accessed on 1st April, 2013).
  4. 4.
    C. S. T. Daughtry and C. L. Walthall, Remote Sens. Environ., 64, 192–201 (1998).CrossRefGoogle Scholar
  5. 5.
    M. Pesaresi, Int. J. Remote Sens., 29, 6985–7002 (2008).CrossRefADSGoogle Scholar
  6. 6.
    K. Jia, B. Wu, Y. Tian, Q. Li, and X. Du, IEEE Trans. Geosci. Remote Sens., 49, 3414–3422 (2011).CrossRefADSGoogle Scholar
  7. 7.
    Y. Tian, B. Wu, L. Zhang, Q. Li, K. Jia, and M. Wen, Int. J. Drug Policy, 22, 278–284 (2011).CrossRefGoogle Scholar
  8. 8.
    UNODC. 2012 World drug report, UNODC, Vienna, Austria, 2012.Google Scholar
  9. 9.
    D. J. Potter, P. Clark, and M. B. Brown, J. Forens. Sci., 53, 90–94 (2008).CrossRefGoogle Scholar
  10. 10.
    J. Broséus, F. Anglada, and P. Esseiva, Forens. Sci. Int., 200, 87–92 (2010).CrossRefGoogle Scholar
  11. 11.
    K. Jia, B. Wu, Y. Tian, Y. Zeng, and Q. Li, Int. J. Remote Sens., 32, 9307–9325 (2011).CrossRefADSGoogle Scholar
  12. 12.
    P. J. Curran, Remote Sens. Environ., 30, 271–278 (1989).CrossRefGoogle Scholar
  13. 13.
    N. C. Coops, M. L. Smith, M. E. Martin, and S. V. Ollinger, IEEE Trans. Geosci. Remote Sens., 41, 1338–1346 (2003).CrossRefADSGoogle Scholar
  14. 14.
    R. F. Kokaly, G. P. Asner, S. V. Ollinger, M. E. Martin, and C. A. Wessman, Remote Sens. Environ., 113, S78–S91 (2009).CrossRefGoogle Scholar
  15. 15.
    S. L. Ustin, A. A. Gitelson, S. Jacquemoud, M. Schaepman, G. P. Asner, J. A. Gamon, and P. Zarco-Tejada, Remote Sens. Environ., 113, S67–S77 (2009).CrossRefGoogle Scholar
  16. 16.
    ASD. Handheld Spectroradiometer: User's Guide, ASD Inc., Boulder, CO, 2005.Google Scholar
  17. 17.
    P. S. Thenkabail, R. B. Smith, and E. De Pauw, Remote Sens. Environ., 71, 158–182 (2000).CrossRefGoogle Scholar
  18. 18.
    P. J. Curran, J. L. Dungan, and D. L. Peterson, Remote Sens. Environ., 76, 349–359 (2001).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.State Key Laboratory of Remote Sensing Science, School of GeographyBeijing Normal UniversityBeijingChina

Personalised recommendations