ABSTRACT
The United States Agriculture Improvement Act passed in December of 2018 legalized the growing of Cannabis sativa containing not more than 0.3% total Delta-9 tetrahydrocannabinol (THC) in the country. While Cannabis sativa has been cultivated for hundreds of years, the illegal status of the plant in the United States, and elsewhere, has hindered the development of plant cultivars that meet this legal definition. To assess sampling strategies, and conformance to the THC limit, 14 cultivars of hemp were grown and tested by using gas chromatography with flame ionization detection for total delta-9 THC and total cannabidiol (CBD) during 2020, 2021 and 2022. Each year, samples of fresh plant material were collected from each cultivar weekly, beginning in mid-August and ending in late October, to examine the rate of increase in THC and CBD for different cultivars and select individual plants. The sampling demonstrated that both CBD and THC increase rapidly over a 1-2-week time frame with maximum concentrations (about 16% and 0.6%, respectively) around late September to early October. The testing of individual plants on the same day for select cultivars showed that while the ratio of CBD to THC remains constant (about 20:1 in compliant hemp) during the growing season, the individual plants are highly variable in concentration. Whereas previous studies have shown cultivar-dependent variability in THC production, this study demonstrated a novel plant-to-plant variability in the levels of THC within the same hemp cultivar. Understanding variability within and between hemp cultivars is useful to determine field sampling strategies and to assess the risk of crop embargoes to growers by compliance regulators.
ABSTRACT
Nontarget data acquisition for target analysis (nDATA) workflows using liquid chromatography-high-resolution accurate mass (LC-HRAM) spectrometry, spectral screening software, and a compound database have generated interest because of their potential for screening of pesticides in foods. However, these procedures and particularly the instrument processing software need to be thoroughly evaluated before implementation in routine analysis. In this work, 25 laboratories participated in a collaborative study to evaluate an nDATA workflow on high moisture produce (apple, banana, broccoli, carrot, grape, lettuce, orange, potato, strawberry, and tomato). Samples were extracted in each laboratory by quick, easy, cheap, effective, rugged, and safe (QuEChERS), and data were acquired by ultrahigh-performance liquid chromatography (UHPLC) coupled to a high-resolution quadrupole Orbitrap (QOrbitrap) or quadrupole time-of-flight (QTOF) mass spectrometer operating in full-scan mass spectrometry (MS) data-independent tandem mass spectrometry (LC-FS MS/DIA MS/MS) acquisition mode. The nDATA workflow was evaluated using a restricted compound database with 51 pesticides and vendor processing software. Pesticide identifications were determined by retention time (tR, ±0.5 min relative to the reference retention times used in the compound database) and mass errors (δM) of the precursor (RTP, δM ≤ ±5 ppm) and product ions (RTPI, δM ≤ ±10 ppm). The elution profiles of all 51 pesticides were within ±0.5 min among 24 of the participating laboratories. Successful screening was determined by false positive and false negative rates of <5% in unfortified (pesticide-free) and fortified (10 and 100 µg/kg) produce matrices. Pesticide responses were dependent on the pesticide, matrix, and instrument. The false negative rates were 0.7 and 0.1% at 10 and 100 µg/kg, respectively, and the false positive rate was 1.1% from results of the participating LC-HRAM platforms. Further evaluation was achieved by providing produce samples spiked with pesticides at concentrations blinded to the laboratories. Twenty-two of the 25 laboratories were successful in identifying all fortified pesticides (0-7 pesticides ranging from 5 to 50 µg/kg) for each produce sample (99.7% detection rate). These studies provide convincing evidence that the nDATA comprehensive approach broadens the screening capabilities of pesticide analyses and provide a platform with the potential to be easily extended to a larger number of other chemical residues and contaminants in foods.
