Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Characterization of Botanicals by Nuclear Magnetic Resonance and Mass Spectrometric Chemical Profiling

Wang, Xinyi
http://orcid.org/0000-0001-8769-4030
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1521718129716851

Abstract Details

2018, Doctor of Philosophy (PhD), Ohio University, Chemistry and Biochemistry (Arts and Sciences).
For authentication of botanical materials, it is difficult to obtain representative reference materials, because components in botanicals vary significantly with respect to cultivation conditions. Chemical profiling of plant extracts or spectral fingerprinting can differentiate botanicals and group them by their chemical profiles. Nuclear magnetic resonance (NMR) spectroscopy, high-resolution mass spectrometry (HRMS), and inductively coupled plasma mass spectrometry (ICP-MS) yield powerful and useful methods for profiling plant extracts. Instead of the targeted analysis using a lot of time to optimize extracting conditions for selected metabolites and separate the overlapping peaks, a high-throughput, rapid and direct extraction method with chemical profiling to compare from point to point is preferred for characterization of botanicals. A whole chemical profile is usually collected with a fast scanning method with untargeted analysis and coupled with chemometrics methods. The chemical components of botanicals always have high dynamic range, and some of the low-intensity compounds have higher importance than the high-intensity compounds to differentiate from variety to variety. Thus, selecting and optimizing data preprocessing methods, including but not limited to, different transformations, singular value decomposition, normalization, error scaling, standard normal variate renormalization, binning strategies, and resolving powers are important for NMR and MS measurements combined with pattern recognition to be an authentication and characterization tool for various products. The established NMR spectral profiling method was applied on 25 Cannabis extracts, 20 hemp extracts, 8 liquor samples, 9 hops extracts, and 12 tea extracts, respectively. The established direct infusion measurements HRMS spectral profiling method was applied on 25 Cannabis extracts, 20 hemp extracts, and 8 liquor samples. The established ICP-MS spectral profiling method was applied on 9 rice samples. Among multivariate classification methods, a fuzzy rule-building expert system (FuRES), linear discriminant analysis (LDA), super partial least squares discriminant analysis (sPLS-DA), support vector machine (SVM), and support vector machine classification trees (SVMTreeG and SVMTreeH) were evaluated with the prediction accuracy. It is proved that data preprocessing method optimization is necessary to improve the classification performance for each NMR and MS profiling and for each variety of botanicals.
Peter Harrington, Ph.D. (Advisor)
170 p.
Analytical Chemistry; Chemistry
Chemometrics; chemical profiling; NMR; HRMS; ICP-MS; botanicals; data preprocessing; Cannabis

Recommended Citations

Citations

  • Wang, X. (2018). Characterization of Botanicals by Nuclear Magnetic Resonance and Mass Spectrometric Chemical Profiling [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1521718129716851

    APA Style (7th edition)

  • Wang, Xinyi. Characterization of Botanicals by Nuclear Magnetic Resonance and Mass Spectrometric Chemical Profiling. 2018. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1521718129716851.

    MLA Style (8th edition)

  • Wang, Xinyi. "Characterization of Botanicals by Nuclear Magnetic Resonance and Mass Spectrometric Chemical Profiling." Doctoral dissertation, Ohio University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1521718129716851

    Chicago Manual of Style (17th edition)

ohiou1521718129716851
603
© 2018, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.