Skip to main content
SpringerLink
Log in

High-Throughput Chemotyping of Cannabis and Hemp Extracts Using an Ultraviolet Microplate Reader and Multivariate Classifiers

  • Original Paper
  • Published:
Journal of Analysis and Testing Aims and scope Submit manuscript

Abstract

As the use of Cannabis products as natural medicines burgeons, it is also appearing as a food ingredient. It is important to screen Cannabis samples as ingredients by profiling their chemical compositions, which is referred to as chemotyping. Two sets of botanical extracts were studied. The first set is referred to as Cannabis contained plant materials from 15 samples of the sativa, indica, and hybrids of the two species. The second set contained 20 extracts from the variety of Cannabis sativa with low tetrahydrocannabinol (THC) concentrations, i.e., below 0.3%, and, henceforth, will be referred to as hemp. An ultraviolet (UV) microplate reader provides a cost-effective and high-throughput method for identifying chemotypes of plant extracts by their spectra. The microplate reader affords rapid measurements of small volumes, e.g., 50 µL, which demonstrates a potential to significantly reduce the analysis time and cost for Cannabis and hemp chemotyping or chemical profiling. Replicate samples were measured on different days to demonstrate the robustness of the method. Projected difference resolution (PDR) maps were used to visualize the separations among the classes. Five multivariate classifiers, fuzzy rule-building expert system (FuRES), super partial least squares-discriminant analysis (sPLS-DA), support vector machine (SVM), and two tree-based support vector machines (SVMtreeG and SVMtreeH) were evaluated. The classifiers were validated with ten bootstrapped Latin partitions (BLPs). For the Cannabis extracts, the SVMtreeG yielded the best performance and the classification accuracy was 99.1 ± 0.4% for spectra collected in the nonlinear absorbance range. For the hemp extracts, the SVM classifier performed the best with a 97.4 ± 0.6% classification accuracy. These results demonstrate that the UV microplate reader coupled with multivariate classifiers can be used as a high-throughput and cost-effective approach for chemotyping Cannabis.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. State Medical Marijuana Laws. http://www.ncsl.org/research/health/state-medical-marijuana-laws.aspx. Accessed 15 Sep 2017.

  2. Wang X-S, Tang C-H, Yang X-Q, Gao W-R. Characterization, amino acid composition and in vitro digestibility of hemp (Cannabis sativa L.) proteins. Food Chem. 2008;107(1):11–8.

    Article  CAS  Google Scholar 

  3. Hartsel J, Eades J, Hickory B, Makriyannis A. Cannabis sativa and Hemp. Nutraceuticals: efficacy, safety and toxicity. London: Elsevier Inc; 2016. p. 735–54.

    Google Scholar 

  4. Pollastro F, Taglialatela-Scafati O, Allara M, Munoz E, Di Marzo V, De Petrocellis L, et al. Bioactive prenylogous cannabinoid from fiber hemp (Cannabis sativa). J Nat Prod. 2011;74(9):2019–22.

    Article  CAS  Google Scholar 

  5. ElSohly MA, Radwan MM, Gul W, Chandra S, Galal A. Phytochemistry of Cannabis sativa L. Phytocannabinoids. New York: Springer; 2017. p. 1–36.

    Google Scholar 

  6. Mechoulam R, Peters M, Murillo-Rodriguez E, Hanuš LO. Cannabidiol—recent advances. Chem Biodivers. 2007;4(8):1678–92.

    Article  CAS  Google Scholar 

  7. Wang X, Harrington PDB, Baugh SF. Comparative study of NMR spectral profiling for the characterization and authentication of Cannabis. J AOAC Int. 2017;100(5):1356–64.

    Article  CAS  Google Scholar 

  8. Sativa vs. indica vs. hybrid: what’s the difference between Cannabis types? https://www.leafly.com/news/Cannabis-101/sativa-indica-and-hybrid-whats-the-difference-between-Cannabis-ty. Accessed 27 Sep 2017.

  9. Hazekamp A, Fischedick JT. Cannabis—from cultivar to chemovar. Drug Test Anal. 2012;4(7–8):660–7.

    Article  CAS  Google Scholar 

  10. Lowry S, Ritter G, Woodruff H, Isenhour T. Selecting liquid phases for multiple column gas chromatography from their eigenvector projections. J Chromatogr Sci. 1976;14(3):126–9.

    Article  CAS  Google Scholar 

  11. Brighenti V, Pellati F, Steinbach M, Maran D, Benvenuti S. Development of a new extraction technique and HPLC method for the analysis of non-psychoactive cannabinoids in fibre-type Cannabis sativa L. (hemp). J Pharm Biomed Anal. 2017;143(Supplement C):228–36.

    Article  CAS  Google Scholar 

  12. Escrivá Ú, Andrés-Costa MJ, Andreu V, Picó Y. Analysis of cannabinoids by liquid chromatography–mass spectrometry in milk, liver and hemp seed to ensure food safety. Food Chem. 2017;228(Supplement C):177–85.

    Article  Google Scholar 

  13. Patel B, Wene D, Fan ZT. Qualitative and quantitative measurement of cannabinoids in Cannabis using modified HPLC/DAD method. J Pharm Biomed Anal. 2017;146:15–23.

    Article  CAS  Google Scholar 

  14. De Backer B, Debrus B, Lebrun P, Theunis L, Dubois N, Decock L, et al. Innovative development and validation of an HPLC/DAD method for the qualitative and quantitative determination of major cannabinoids in Cannabis plant material. J Chromatogr B. 2009;877(32):4115–24.

    Article  Google Scholar 

  15. Harrington PDB, Wang X. Spectral representation of proton NMR spectroscopy for the pattern recognition of complex materials. J Anal Test. 2017;1(2):10.

    Article  Google Scholar 

  16. Wang X, Harrington PDB, Baugh SF. Effect of preprocessing high-resolution mass spectra on the pattern recognition of Cannabis, hemp, and liquor. Talanta. 2018;180:229–38.

    Article  CAS  Google Scholar 

  17. Landry J, Malovichko G, Zhu X. High-throughput dose-response measurement using a label-free microarray-in-microplate assay platform. Anal Chem. 2015;87(11):5640–8.

    Article  CAS  Google Scholar 

  18. Vermeer LM, Higgins CA, Roman DL, Doorn JA. Real-time monitoring of tyrosine hydroxylase activity using a plate reader assay. Anal Biochem. 2013;432(1):11–5.

    Article  CAS  Google Scholar 

  19. Takebayashi J, Iwahashi N, Ishimi Y, Tai A. Development of a simple 96-well plate method for evaluation of antioxidant activity based on the oxidative haemolysis inhibition assay (OxHLIA). Food Chem. 2012;134(1):606–10.

    Article  CAS  Google Scholar 

  20. Metrani R, Jayaprakasha GK, Patil BS. Optimized method for the quantification of pyruvic acid in onions by microplate reader and confirmation by high resolution mass spectra. Food Chem. 2018;242(Supplement C):451–8.

    Article  CAS  Google Scholar 

  21. Robert-Peillard F, Boudenne J-L, Coulomb B. Development of a simple fluorescence-based microplate method for the high-throughput analysis of proline in wine samples. Food Chem. 2014;150:274–9.

    Article  CAS  Google Scholar 

  22. Chen P, Luthria D, Harrington PD, Harnly JM. Discrimination among panax species using spectral fingerprinting. J AOAC Int. 2011;94(5):1411–21.

    Article  CAS  Google Scholar 

  23. Harrington PB. Fuzzy multivariate rule-building expert systems: minimal neural networks. J Chemom. 1991;5(5):467–86.

    Article  Google Scholar 

  24. Chen Z, Zhang Z, Zhu R, Xiang Y, Harrington PB. Diagnosis of patients with chronic kidney disease by using two fuzzy classifiers. Chemometr Intell Lab Syst. 2016;153:140–5.

    Article  CAS  Google Scholar 

  25. Chen Z, Gu A, Zhang X, Zhang Z. Authentication and inference of seal stamps on Chinese traditional painting by using multivariate classification and near-infrared spectroscopy. Chemometr Intell Lab Syst. 2017;171:226–33.

    Article  CAS  Google Scholar 

  26. Aloglu AK, Harrington PB, Sahin S, Demir C. Prediction of total antioxidant activity of Prunella L. species by automatic partial least square regression applied to 2-way liquid chromatographic UV spectral images. Talanta. 2016;161:503–10.

    Article  CAS  Google Scholar 

  27. Chen Z, Zhang X, Zhang Z. Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models. Int Urol Nephrol. 2016;48(12):2069–75.

    Article  Google Scholar 

  28. Harrington PdB. Automated support vector regression. J Chemom. 2017;31(4):e2867.

    Article  Google Scholar 

  29. de Boves Harrington P. Support vector machine classification trees. Anal Chem. 2015;87(21):11065–71.

    Article  Google Scholar 

  30. de Boves Harrington P. Support vector machine classification trees based on fuzzy entropy of classification. Anal Chim Acta. 2017;954:14–21.

    Article  Google Scholar 

  31. Lu W, Rankin JG, Bondra A, Trader C, Heeren A, Harrington PDB. Ignitable liquid identification using gas chromatography/mass spectrometry data by projected difference resolution mapping and fuzzy rule-building expert system classification. Forensic Sci Int. 2012;220(1):210–8.

    Article  CAS  Google Scholar 

  32. Chen Z, Zhang Z, Zhu R, Xiang Y, Yang Y, Harrington PB. Application of terahertz time-domain spectroscopy combined with chemometrics to quantitative analysis of imidacloprid in rice samples. J Quant Spectrosc Radiat Transfer. 2015;167:1–9.

    Article  CAS  Google Scholar 

  33. de Boves Harrington P. Statistical validation of classification and calibration models using bootstrapped Latin partitions. TrAC, Trends Anal Chem. 2006;25(11):1112–24.

    Article  Google Scholar 

  34. Harrington PdB. Multiple versus single set validation of multivariate models to avoid mistakes. Crit Rev Anal Chem. 2018;48(1):33–46.

    Article  CAS  Google Scholar 

  35. UV-visible spectroscopy. http://www.kayelaby.npl.co.uk/chemistry/3_8/3_8_7.html. Accessed 5 Sep 2017.

  36. Harrington PD. Fuzzy grid encoded independent modeling for class analogies (FIMCA). Anal Chem. 2014;86(10):4883–92.

    Article  Google Scholar 

  37. Harnly J, Chen P, Harrington PD. Probability of identification: adulteration of American Ginseng with Asian ginseng. J AOAC Int. 2013;96(6):1258–65.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Chemistry Mapping, Inc. is thanked for supplying Cannabis and hemp extracts. Authors appreciate Xinyi Wang and Ahmet Aloglu for their useful comments. Authors also appreciate Dr. Justin Holub and Tang Tang for their help with the microplate reader measurement.

Author information

Authors and Affiliations

  1. Clippinger Laboratories, Department of Chemistry and Biochemistry, Center for Intelligent Chemical Instrumentation, Ohio University, Athens, OH, 45701, USA

    Zewei Chen & Peter de Boves Harrington

  2. Chemistry Mapping, Inc., 5902 McIntyre St., Golden, CO, 80403, USA

    Steven F. Baugh

Authors
  1. Zewei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter de Boves Harrington
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Steven F. Baugh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter de Boves Harrington.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 19 kb)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., de Boves Harrington, P. & Baugh, S.F. High-Throughput Chemotyping of Cannabis and Hemp Extracts Using an Ultraviolet Microplate Reader and Multivariate Classifiers. J. Anal. Test. 2, 210–222 (2018). https://doi.org/10.1007/s41664-018-0075-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41664-018-0075-3

Keywords

Search

Navigation