Dr. Peter de B. Harrington
Dr. Peter de B. Harrington, Professor of Chemistry & Biochemistry, was quoted in an Analytical Cannabis article headlined “Chemotyping: Classifying cannabis strains by chemical composition.”
An approach to optimizing the chemotyping of cannabis strains has been outlined by researchers at Ohio University and Chemistry Mapping Inc. Specifically, they evaluated the impact of several mass spectrometry (MS) data pre-processing techniques to identify which strategy helps to provide the most accurate and useful chemical fingerprint of cannabis samples. Their findings were published in Talanta. The research highlights the importance of carefully evaluating and selecting data pre-processing parameters.
Speaking to us about the story behind the group’s work, corresponding author, Peter Harrington, Professor of Chemistry, Ohio University, explained that the team was first recruited to work on characterizing botanicals using chemical profiling by the United States Department of Agriculture (USDA). Following the publication of many papers on characterizing ginsengs and black cohosh, they were enlisted by Chemistry Mapping Inc. to apply their techniques to cannabis products. Since then, the team have published several papers on cannabis. Dr. Harrington highlighted two in particular relating to a high-throughput method of extracting plant material into deuterated chloroform and then characterizing it by nuclear magnetic resonance spectroscopy.
Prof. Harrington told us, “The goal is to develop a quick method of measuring the chemical composition of cannabis, so we use spectroscopic methods to analyze extracts, and skip a chromatographic separation step that usually takes longer. Instead of identifying and quantifying each component in the spectrum, the spectrum is treated as a fingerprint. Using chemometrics and machine learning, we then can group the samples into classes based on their observed chemical composition. We refer to this procedure as chemotyping. The goal is to correlate these groups with desired pharmacological properties, so that industry can have some quality control over products and provide an avenue to achieve personalized medicine.”
Read more of the interview in Talanta.
Harrington’s research article on “Effect of preprocessing high-resolution mass spectra on the pattern recognition of Cannabis, hemp, and liquor” in Talanta was co-authored by Xinyi Wang, a chemistry graduate student at Ohio University who is the lead author, and Steven F. Baugh of Chemistry Mapping Inc.
Abstract: High-resolution mass spectrometry (HRMS) combined with pattern recognition was used to discriminate among twenty-five Cannabis samples, twenty hemp samples, and eight liquor samples. The effects of preprocessing on multivariate data analysis were evaluated for Orbitrap high-resolution mass spectra. Different root transformations were evaluated with respect to the bin width and the average classification rates. In addition, linear binning and proportional binning with various resolving powers were studied with respect to the average classification rates. The proportional binning with the square root transformation gave the best overall performance for chemical profiling or spectral fingerprinting. Six classification methods, fuzzy rule-building expert system (FuRES), linear discriminant analysis (LDA), super partial least squares discriminant analysis (sPLS-DA), support vector machine (SVM), SVM classification tree type gap (SVMTreeG), and SVM classification tree type entropy (SVMTreeH) had similar trends in prediction rate with respect to the resolving power. The optimal proportional mass bin width may depend on the data set, i.e., for the Cannabis samples is resolving power 10−4, for the hemp samples and the liquor samples are resolving power 10−3. Hence, data preprocessing methods such as different transformations, binning strategies, and resolving powers are important factors to be optimized for HRMS direct infusion measurements combined with pattern recognition to be an authentication and characterization tool for various products.
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