September 23, 2020

Five scientists, one a college student, were selected by the AOAC Technical Programming Council judging panel as winners of the contest for first-time AOAC poster presenters.

Posters were evaluated based on content and overall formatting during the author’s presentation time at the Meeting. Winners have the opportunity to submit their work for consideration for publication in the Journal of AOAC INTERNATIONAL.

The announcement came at the 2020 AOAC INTERNATIONAL Virtual Annual Meeting.


Presenter: Jennifer Sanderson, Agilent Technologies, Inc., Wilmington, DE, USA

Determination of %THC in Hemp and Cannabis Flower by Derivatization GC-MS

The legal distinction between classifying Cannabis Sativa L as either Hemp or Cannabis is defined by the percentage of total THC the plant contains. Federal law mandates that the percentage must be less than 0.3% by dry weight (ref). Different methodologies can be deployed to accurately quantitate total THC, among which is the determination of total THC by offline derivatization and analysis by GC-MS. Presented here is a complete, robust method from Agilent to determine total THC by quantitation of (−)-trans-Δ9-Tetrahydrocannabinol (D9-THC) and it’s acid, delta(9)-Tetrahydrocannabinolic acid (THCA), by derivatization and GC-MS analysis.

Jennifer Sanderson, Jessica Westland, Agilent Technologies, Inc., Wilmington, DE, USA

Presenter: Adam Gilmore, HORIBA Instruments Incorporated, Piscataway, NJ, USA

Rapid, Sensitive Identification and Quantification of Cannabinoids by Absorbance-Transmittance and Fluorescence Excitation Emission Matrix (A-TEEM) Spectroscopy

The increasing global market for cannabinoid products drives the need for rapid, accurate tetrahydrocannabinol (THC) and cannabidiol (CBD) measurements. While, HPLC is the conventional method for quantifying the four major acid and neutral forms, THCA, THC, CBDA and CBD, respectively, it requires up to 20 min per analysis. Here we used the patented A-TEEM spectroscopy method that requires 30-45 s per sample and less than 100 fold lower amounts of material than HPLC. Sample analyses included purified cannabinoid standard solutions and methanolic flower extracts from varieties widely ranging in THC(A) and CBD(A) concentrations. The major cannabinoids exhibited unique A-TEEM fingerprints and Parallel Factor Analysis (PARAFAC) decomposed their distinct excitation and emission spectral loadings and scores. The unfolded EEM components of the A-TEEM data (2-way array) and absorbance spectra were simultaneously evaluated using Extreme Gradient Boosted Discrimination Analysis (XGBDA) to accurately classify the cannabinoid compounds and flower varieties.  XGB regression analysis of the flower extracts yielded high correlations for each cannabinoid comparable to HPLC in detection sensitivity. We conclude the A-TEEM is a rapid, effective tool for qualitative and quantitative cannabinoid analysis especially suitable to identify plant varieties with varying concentrations of THC(A) and CBD(A). Future studies will include additional cannabinoids, plant varieties and sample matrices.

Adam Gilmore, A Robinson, HORIBA Instruments Incorporated, Piscataway, NJ, USA, Mostafa Elhendawy, Mahmoud Elsohly, Mohamed Radwan, Amira Wanas, University of Mississippi, Oxford, MS, USA, Jana Hildreth, Synutra International, Inc., Rockville, MD, USA

Presenter: Erasmus Cudjoe, PerkinElmer, Inc., Woodbridge, ON, Canada

A Quantitative High-Performance Liquid Chromatography-Ultraviolet Validated Method for Analysis of 16 Cannabinoids in Dry Cannabis/Hemp Flower

In recent times, there is growing demand for cannabis/hemp flower and associated products for recreational and therapeutic purposes, and as a government regulatory bodies have implemented measures for accurate quantification of cannabinoids potency. The implementation was critical to assure consumers of the quality and safety of the cannabis and associated products. In view of this, it is paramount that robust and reliable quantitative analytical methods are developed for accurate determination of some of the important bioactive cannabinoids of the cannabis/hemp plant. In this study, we present a validated analytical procedure for accurate and reliable quantification of 16 cannabinoids in dry cannabis flower using a reverse phase liquid chromatographic method with an ultra-violet detector. A 10 min chromatographic separation method on a C-18 reverse phase analytical column was adequate to guarantee effective separation of all cannabinoids. In this study a single selected wavelength sufficed the limit of quantification required for accurate determination of all 16 cannabinoids in both cannabis/hemp samples. The precision for analytes’ retention times and peak areas was ≤0.5 and 1.6% RSD, respectively, for 10 replicates. Limit of quantification using spiked cannabis samples was ≤ 0.05% (w/w), making it applicable for in hemp samples, which has relatively lower concentration of tetrahydrocannabinol content. Coefficient of regression (r2 value) was >0.995 for all analytes.

Erasmus Cudjoe, PerkinElmer, Inc., Woodbridge, ON, Canada, Luke Ward, Ben Armstrong, Ellen Parkin, Juniper Analytics, Bend, OR, USA, Jason Weisenseel, Jacob Jalali, PerkinElmer, Inc., Shelton, CT, USA

Presenter: Tarun Anumol, Agilent Technologies, Inc., Wilmington, DE, USA, Email: [email protected]

An End-to-End Workflow Solution for Quick and Easy Quantitative Analysis of Multiclass Veterinary Drug Residues in Meat

Improper use of veterinary drugs in animal farming can result in accumulation in animal-derived foods, causing adverse effects to consumers. Global regulations define limits for vet-drugs in food of animal origin to protect public health. Triple quadrupole LC/MS is a widely accepted for this analysis however laboratories traditionally run individual analyses based on compound class. This can be inefficient and result in high operating costs. In this poster we describe a single, comprehensive screening and quantitative workflow solution for highly sensitive, and reproducible analysis of >200 multi-class veterinary drugs in various animal origin food matrices using LC-MS/MS. The end-to-end workflow includes extraction and matrix cleanup, chromatographic separation, MS detection, and quantitation. Chicken, beef, and pork muscle matrices were used to assess the method performance. A simple sample preparation protocol based on solvent extraction and Captiva EMR-Lipid cleanup was used to extract target analytes. Method sensitivity, linearity, accuracy, and precision data were measured using matrix-matched spike samples for a range from 0.1-100 ppb. Method recovery analysis was performed using matrix-spiked samples at 3 concentrations. The aim was to assist in the setup of a routine analysis for vet drugs, replacing multiple class-specific analytical methods and extraction procedures. The workflow performance verified the broad applicability of VD screening in various food matrices.

Tarun Anumol, Ruben Garnica, Agilent Technologies, Inc., Wilmington, DE, USA, Siji Joseph, Aimei Zou, Agilent Technologies, Inc., Singapore, Singapore

Presenter: Isaac Rukundo, University of Nebraska, Lincoln, NE, USA

Classifying Metanil Yellow-Adulterated Turmeric Powder According to Source and Quantifying the Level of Adulteration Using a Handheld NIR Spectrometer

The feasibility of using a transportable near infrared (NIR) spectrometer to quantify metanil yellow content (MY, % w/w) in turmeric spice and to identify the source of adulteration was studied. Turmeric sourced from six retailers were processed into a powder and adulterated with MY (0.0-30% w/w). Spectra were collected with a benchtop NIR spectrometer and a handheld NIR spectrometer. Partial least squares (PLS) regression models were used to compare prediction performance of the two instruments based on coefficients of determination of calibration and validation (R2 and r2, respectively), root-mean-square errors of calibration and validation (RMSEC and RMSEP, respectively), and ratio of prediction error to standard deviation (RPD). Spectra from the handheld NIR were subjected to principal component analysis (PCA) and soft independent modeling class analogy (SIMCA) to classify adulterated turmeric samples by source. The PCA-SIMCA model was validated using an independent data set. PLS models for both the benchtop and handheld NIR yielded high R2, r2and RPD, and low RMSEC, RMSECV, and RMSEP. No significant difference was found between their predicted MY values. At a 5% level of significance, all of the samples were correctly classed for source during validation. The combination of handheld NIR technology, PLS regression, and PCA-SIMCA modeling is a great tool to detect and, potentially, deter adulteration of turmeric powder, and provide a framework for assuring authenticity.

Presenter: Isaac Rukundo, University of Nebraska, Lincoln, NE, USA, Email: [email protected]