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

2020

Anal. Chem.

Self Cite

Chemometrics is widely used to solve various quantitative and qualitative problems in analytical chemistry. A selfoptimizing chemometrics method facilitates scientists to exploit the advantages of chemometrics. In this report, a parameter-free support vector elastic net that self-optimizes two key regularization constants, i.e., λ for L2 regularization and t for L1 regularization, is developed and referred to as self-optimizing support vector elastic net (SOSVEN). Response surface modeling (RSM) and bootstrapped Latin partitions (BLPs) are incorporated for the optimization. Responses at a set of design points over the ranges of the two factors are evaluated with an internal BLP validation using a calibration set. A 2−dimensional interpolation with a cubic spline fits a response surface to determine the best condition that gives the best-estimated response. The SOSVEN with RSM had comparable performances with the one tuned by grid search, while the RSM is more efficient. The developed SOSVEN was compared with two parameter-free chemometrics methods, super partial least-squares regression (sPLSR) and super support vector regression (sSVR) for calibration, and sPLS-discriminant analysis (sPLS−DA) and support vector classification (SVC) for classification. For calibration, the SOSVEN with RSM worked equivalently well or better than the other two self-optimizing methods for the evaluations using meat and hemp oil data sets. For classification, a reference wine data set and mass spectra of different marijuana extracts were used. The three classifiers had similar performances to identify the cultivars of wines with nearly 98% of accuracy. The SOSVEN significantly outperformed sPLS-DA and SVC to classify the mass spectra of marijuana extracts with an overall accuracy of 97%. These results demonstrated excellent abilities of SOSVEN for classification and calibration.

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Section: Methodsmentioning

confidence: 99%

“…29 sPLS is the automated PLS that selfoptimizes the number of LVs by doing an internal BLPs evaluation with the calibration set. 30,31 The calibration set is randomly split into two subsets by the Latin partition. Each subset is iteratively used to build a local PLS model.…”

Section: T H Imentioning

confidence: 99%

Self-Optimizing Support Vector Elastic Net

2020

Anal. Chem.

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“…PLS-DA replaces the original measured variables with new latent variables, i.e., components, that maximize the covariance between the independent and dependent variables . PLS-DA was automated to furnish sPLS-DA that determines the number of latent variables by doing an internal bootstrapped Latin partition evaluation with the model-building set or calibration data. , The average prediction errors with respect to the number of latent variables were evaluated across 10 bootstraps with 2-Latin partitions. When calculating errors, estimated class designees that are greater than 1 or less than zero were respectively set to 1 and zero.…”

Section: Methodsmentioning

confidence: 99%

“…For the UV measurements, the 15 samples of marijuana in Table and the 20 hemp extracts were used. More details about the sample preparation can be found elsewhere . In short, the raw marijuana and hemp extracts were diluted by the solvent CDCl 3 (99.8% pure, Sigma-Aldrich, St. Louis, MO, U.S.A.) with a ratio of 1:10 ( v/v ).…”

Section: Methodsmentioning

confidence: 99%

“…More details about the sample preparation can be found elsewhere. 24 In short, the raw marijuana and hemp extracts were diluted by the solvent CDCl 3 (99.8% pure, Sigma-Aldrich, St. Louis, MO, U.S.A.) with a ratio of 1:10 (v/v). The UV spectra were measured in absorbance mode using a SpectraMax M5e multimode microplate reader (Molecular Devices, Sunnyvale, CA, U.S.A.) with Costar 3364 96-well standard microplates (polypropylene, flat-bottom, Corning Company, NY, U.S.A.).…”

Section: Methodsmentioning

confidence: 99%

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Pipeline for High-Throughput Modeling of Marijuana and Hemp Extracts

2019

Anal. Chem.

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Authentication of Cannabis products is important for assuring the quality of manufacturing, with the increasing consumption and regulation. In this report, a two-stage pipeline was developed for high-throughput screening and chemotyping the spectra from two sets of botanical extracts from the Cannabis genus. The first set contains different marijuana samples with higher concentrations of tetrahydrocannabinol (THC). The other set includes samples from hemp, a variety of Cannabis sativa with the THC concentration below 0.3%. The first stage applies the technique of class modeling to determine whether spectra belong to marijuana or hemp and reject novel spectra that may be neither marijuana nor hemp. An automatic soft independent modeling of class analogy (aSIMCA) that self-optimizes the number of principal components and the decision threshold is utilized in the first pipeline process to achieve excellent efficiency and efficacy. Once these spectra are recognized by aSIMCA as marijuana or hemp, they are then routed to the appropriate classifiers in the second stage for chemotyping the spectra, i.e., identifying these spectra into different chemotypes so that the pharmacological properties and cultivars of the spectra can be recognized. Three multivariate classifiers, a fuzzy rule building expert system (FuRES), super partial least-squares-discriminant analysis (sPLS-DA), and support vector machine tree type entropy (SVMtreeH), are employed for chemotyping. The discriminant ability of the pipeline was evaluated with different spectral data sets of these two groups of botanical samples, including proton nuclear magnetic resonance, mass, and ultraviolet spectra. All evaluations gave good results with accuracies greater than 95%, which demonstrated promising application of the pipeline for automated high-throughput screening and chemotyping marijuana and hemp, as well as other botanical products.

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Automatic soft independent modeling for class analogies

2019

Analytica Chimica Acta