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  1. Free, publicly-accessible full text available March 18, 2023
  2. After attending this presentation, attendees will gain knowledge in the strategy to achieve high-throughput and simultaneous analysis of cannabinoids and appreciate a validated LC-UV method for analysis of twelve cannabinoids in hemp oil. This presentation will first impact the forensic science community by introducing three fast LC separations of twelve cannabinoids that can be used with either UV or mass spectrometric (MS) detection. It will further impact the forensic science community by introducing a validated LC-UV method for high-throughput and simultaneous analysis of twelve cannabinoids in hemp oil, which can be routinely used by cannabis testing labs. In recent years,more »the use of products of Cannabis sativa L. for medicinal purposes has been in a rapid growth, although their preparation procedure has not been clearly standardized and their quality has not been well regulated. To analyze the therapeutic components, i.e. cannabinoids, in products of Cannabis sativa L., LC-UV has been frequently used, because LC-UV is commonly available and usually appropriate for routine analysis by the cannabis growers and commercial suppliers. In the literature, a few validated LC-UV methods have been described. However, so far, all validated LC-UV methods only focused in the quantification of eleven or less cannabinoids. Therefore, a method able to simultaneously analyze more cannabinoids in a shorter run time is still in high demand, because more and more cannabinoids have been isolated and many of them have shown medicinal properties. In this study, the LC separation of twelve cannabinoids, including cannabichromene (CBC), cannabidiolic acid (CBDA), cannabidiol (CBD), cannabidivarinic acid (CBDVA), cannabidivarin (CBDV), cannabigerolic acid (CBGA), cannabigerol (CBG), cannabinol (CBN), delta-8 tetrahydrocannabinol (Δ8-THC), delta-9 tetrahydrocannabinolic acid A (Δ9-THCA A), delta-9 tetrahydrocannabinol (Δ9-THC), and tetrahydrocannabivarin (THCV), has been systematically optimized using a Phenomenex Luna Omega 3 µm Polar C18 150 mm × 4.6 mm column with regard to the effects of the type of organic solvent, i.e. methanol and acetonitrile, the content of the organic solvent, and the pH of the mobile phase. The optimization has resulted in three LC conditions at 1.0 mL/minute able to separate the twelve cannabinoids: 1) a mobile phase consisting of water and methanol, both containing 0.1% formic acid (pH 2.69), with a gradient elution at 75% methanol for the first 3 minutes and then linearly increase to 100% methanol at 12.5 minutes; 2) a mobile phase consisting of water and 90% (v/v) acetonitrile in water, both containing 0.1% formic acid and 20 mM ammonium formate (pH 3.69), with an isocratic elution at 75% acetonitrile for 14 minutes; and 3) a mobile phase consisting of water and 90% (v/v) acetonitrile in water, both containing 0.03% formic acid and 20 mM ammonium formate (pH 4.20), with an isocratic elution at 75% acetonitrile for 14 minutes. In order to demonstrate the effectiveness of the achieved LC separations, a LC-UV method is further validated for the high-throughput and simultaneous analysis of twelve cannabinoids. The method used the mobile phase at pH 3.69, which resulted in significant improvement in throughput compared to other validated LC-UV methods published so far. The method used flurbiprofen as the internal standard. The linear calibration range of all the cannabinoids were between 0.1 to 25 ppm with R2≥0.9993. The LOQ (S/N=10) of the cannabinoids was between 17.8 and 74.2 ppb. The validation used a hemp oil containing 3.2 wt% CBD and no other cannabinoids, which was reported by the vendor with a certificate of analysis, as the matrix to prepare control samples: the hemp oil was first extracted using liquid-liquid extraction (LLE) with methanol; cannabinoids were then spiked into the extract at both 0.5 ppm and 5 ppm level. Afterwards, the recovery, precision (%RSD) and accuracy (%Error) of the control samples were assessed and the results met the requirements by the ISO/IEC 17025 and ASTM E2549-14 guidelines.« less
  3. A method using strong anion exchange solid phase extraction (SAX-SPE) followed by liquid chromatography ultraviolet detection (LC-UV) for the analysis of flunixin in equine plasma for doping control in horse racing has been developed. By using SAX-SPE, commonly regulated non-steroidal anti-inflammatory drugs (NSAIDs) by the United States Equestrian Federation (USEF), i.e. PBZ, OPBZ, diclofenac, flunixin, ketoprofen, meclofenamic acid and naproxen, and an internal standard, i.e. flurbiprofen, were first selectively extracted. Then, baseline separation of flunixin from other NSAIDs, the internal standard, and residual components of equine plasma was achieved using LC-UV. Finally, flunixin in equine plasma was quantified after anmore »internal calibration curve was created.« less
  4. A method using strong anion exchange solid phase extraction (SAX-SPE) followed by liquid chromatography ultraviolet detection (LC-UV) for the analysis of phenylbutazone (PBZ) and its metabolite oxyphenbutazone (OPBZ) in equine plasma for doping control in horse racing has been developed. By using SAX-SPE, commonly regulated non-steroidal anti-inflammatory drugs (NSAIDs) by the United States Equestrian Federation (USEF), i.e. PBZ, OPBZ, diclofenac, flunixin, ketoprofen, meclofenamic acid and naproxen, and an internal standard, i.e. tolfenamic acid, were first selectively extracted. Then, baseline separation of PBZ, OPBZ from other NSAIDs, internal standard, and residual components of equine plasma was achieved using LC-UV. Finally, PBZmore »and OPBZ in equine plasma were quantified after an internal calibration curve was created.« less
  5. Free, publicly-accessible full text available October 1, 2022
  6. Estimating the future event sequence conditioned on current observations is a long-standing and challenging task in temporal analysis. On one hand for many real-world problems the underlying dynamics can be very complex and often unknown. This renders the traditional parametric point process models often fail to fit the data for their limited capacity. On the other hand, long-term prediction suffers from the problem of bias exposure where the error accumulates and propagates to future prediction. Our new model builds upon the sequence to sequence (seq2seq) prediction network. Compared with parametric point process models, its modeling capacity is higher and hasmore »better flexibility for fitting real-world data. The main novelty of the paper is to mitigate the second challenge by introducing the likelihood-free loss based on Wasserstein distance between point processes, besides negative maximum likelihood loss used in the traditional seq2seq model. Wasserstein distance, unlike KL divergence i.e. MLE loss, is sensitive to the underlying geometry between samples and can robustly enforce close geometry structure between them. This technique is proven able to improve the vanilla seq2seq model by a notable margin on various tasks.« less