Screening for More than 1,000 Pesticides and Environmental Contaminants in Cannabis by GC/Q-TOF

[High-throughput screening of 54 alternative plasticizers in sesame oil using gas chromatography-quadrupole time-of-flight mass spectrometry]

Restrictions on the use of phthalates have led to the wide use of alternative plasticizers (APs) such as organophosphate, adipate, citrate, and sebacate. However, because plasticizers combine with polymers in plastic products via unstable noncovalent bonds, they can easily migrate out of these products, causing environmental pollution. In particular, their migration out of food packaging, containers, and other food-contact materials and into food has raised great concerns. Toxicological studies have shown that APs contain potentially toxic substances that can affect endocrine functions and cause neurotoxicity, genotoxicity, and other adverse effects. Thus, their potential risks to food should not be underestimated. Sesame oil is a necessity in daily cooking. The results of risk monitoring in recent years have indicated that sesame oil often contains phthalates in excess of the standard limits. However, the potential risks of APs in sesame oil have not yet been reported. Some common detection methods for APs include gas chromatography-mass spectrometry, gas chromatography-triple quadrupole mass spectrometry, and liquid chromatography-triple quadrupole mass spectrometry. Unfortunately, these methods use low-resolution mass spectrometry and are limited by the resolution, scan rate, and analysis mode. Gas chromatography-quadrupole time-of-flight mass spectrometry (GC-Q-TOF/MS) has the advantages of high resolution, sensitivity, and analysis speed. In full-scan mode, GC-Q-TOF/MS can accurately collect the full-spectrum mass number of target compounds with low content levels in complex substrates, thereby realizing efficient screening and quantitative analysis. It shows outstanding advantages in the trace analysis of pesticide residues and pollutants. Furthermore, it features strong qualitative and high screening abilities. Establishment of a personal compound database and library (PCDL) addresses limitations in the number of compounds that can be measured and enables the rapid identification of targets without the use of standard products. In addition, increasing the number of targets for synchronous screening enables the retrospective analysis of new targets. In this study, a method based on GC-Q-TOF/MS was developed for the determination of 54 APs in sesame oil. The samples were extracted with acetonitrile and purified using a PSA/silica solid-phase extraction column. The mass-spectral information of the samples was then collected by GC-Q-TOF/MS in full-scan mode, and the 54 APs were searched using an established high-resolution mass-spectrum database to simultaneously achieve the broad-spectrum screening, qualitative identification, and quantitative analysis of multiple targets. The effects of different extraction solvents and purification methods on sample extraction and purification were compared. The accuracy of the screening results was improved by optimizing the GC-separation conditions, quality-extraction window, retention-time deviation, and other screening parameters. The screening detection limits (SDLs) of the 54 APs ranged from 0.01 to 0.02 mg/kg; specifically, the SDL of 41 compounds was 0.01 mg/kg and that of 13 compounds were 0.02 mg/kg. The limits of quantification were in the range of 0.02-0.04 mg/kg. A total of 80 sesame-oil samples were rapidly screened using this method under optimal conditions. Five APs were identified from the 80 sesame-oil samples and quantitatively analyzed using the matrix-matched external-standard method. The results of this quantitative methodology showed that the five APs had good linear relationships in the range of 0.01-0.2 mg/L, with all correlation coefficients greater than 0.99. The accuracy and precision of the method were verified using a standard recovery test with blank sesame-oil samples. Under the three standard levels of 0.04, 0.08, and 0.2 mg/kg, the recoveries of the five APs ranged from 71.3% to 97.8%, and the relative standard deviations (RSDs) ranged from 0.4% to 6.1%(n=6). The developed method is fast, accurate, sensitive, and has high throughput. Thus, it can realize the efficient screening, qualitative identification, and quantitative analysis of the 54 APs in sesame oil and provides a potential solution for the monitoring of other contaminants in food.

Modeling a pesticide remediation strategy for preparative liquid chromatography using high-performance liquid chromatography

BACKGROUND

Cannabis sativa L. also known as industrial hemp, is primarily cultivated as source material for cannabinoids cannabidiol (CBD) and ∆9-tetrahydrocannabinol (∆9-THC). Pesticide contamination during plant growth is a common issue in the cannabis industry which can render plant biomass and products made from contaminated material unusable. Remediation strategies to ensure safety compliance are vital to the industry, and special consideration should be given to methods that are non-destructive to concomitant cannabinoids. Preparative liquid chromatography (PLC) is an attractive strategy for remediating pesticide contaminants while also facilitating targeted isolation cannabinoids in cannabis biomass.

METHODS

The present study evaluated the benchtop-scale suitability of pesticide remediation by liquid chromatographic eluent fractionation, by comparing retention times of 11 pesticides relative to 26 cannabinoids. The ten pesticides evaluated for retention times are clothianidin, imidacloprid, piperonyl butoxide, pyrethrins (I/II mixture), diuron, permethrin, boscalid, carbaryl, spinosyn A, and myclobutanil. Analytes were separated prior to quantification on an Agilent Infinity II 1260 high performance liquid chromatography with diode array detection (HPLC-DAD). The detection wavelengths used were 208, 220, 230, and 240 nm. Primary studies were performed using an Agilent InfinityLab Poroshell 120 EC-C18 3.0 × 50 mm column with 2.7 μm particle diameter, using a binary gradient. Preliminary studies on Phenomenex Luna 10 μm C18 PREP stationary phase were performed using a 150 × 4.6 mm column.

RESULTS

The retention times of standards and cannabis matrices were evaluated. The matrices used were raw cannabis flower, ethanol crude extract, CO₂ crude extract, distillate, distillation mother liquors, and distillation bottoms. The pesticides clothianidin, imidacloprid, carbaryl, diuron, spinosyn A, and myclobutanil eluted in the first 3.6 min, and all cannabinoids (except for 7-OH-CBD) eluted in the final 12.6 min of the 19-minute gradient for all matrices evaluated. The elution times of 7-OH-CBD and boscalid were 3.44 and 3.55 min, respectively.

DISCUSSION

7-OH-CBD is a metabolite of CBD and was not observed in the cannabis matrices evaluated. Thus, the present method is suitable for separating 7/11 pesticides and 25/26 cannabinoids tested in the six cannabis matrices tested. 7-OH-CBD, pyrethrins I and II (RT_A: 6.8 min, RT_B: 10.5 min), permethrin (RT_A: 11.9 min, RT_B: 12.2 min), and piperonyl butoxide (RT_A: 8.3 min, RT_B: 11.7 min), will require additional fractionation or purification steps.

CONCLUSIONS

The benchtop method was demonstrated have congruent elution profiles using preparative-scale stationary phase. The resolution of pesticides from cannabinoids in this method indicates that eluent fractionation is a highly attractive industrial solution for pesticide remediation of contaminated cannabis materials and targeted isolation of cannabinoids.

Application of gas chromatographic data and 2D molecular descriptors for accurate global mobility potential prediction

Mobility is a key feature affecting the environmental fate, which is of particular importance in the case of persistent organic pollutants (POPs) and emerging pollutants (EPs). In this study, the global mobility classification artificial neural networks-based models employing GC retention times (RT) and 2D molecular descriptors were constructed and validated. The high usability of RT was confirmed based on the feature selection step performed using the multivariate adaptive regression splines (MARS) tool. Although RT was found to be the most important, according to Kruskal-Wallis ANOVA analysis, it is insufficient to build a robust model, which justifies the need to expand the input layer with 2D descriptors. Therefore the following molecular descriptors: MPC10, WTPT-2, AATS8s, minaaCH, GATS7c, RotBtFrac, ATSC7v and ATSC1p, which were characterized by a high predicting potential were used to improve the classification performance. As a result of machine learning procedure ten of the most accurate neural networks were selected. The external validation showed that the final models are characterized by a high general accuracy score (85.71-96.43%). The high predicting abilities were also confirmed by the micro-averaged Matthews correlation coefficient (MAMCC) (0.73-0.88). To evaluate the applicability of the models, new retention times of selected POPs and EPs including pesticides, polycyclic aromatic hydrocarbons, pharmaceuticals, fragrances and personal care products were measured and used for mobility prediction. Further, the classifiers were used for photodegradation and chlorination products of two popular sunscreen agents, 2-ethyl-hexyl-4-methoxycinnamate and 2-ethylhexyl 4-(dimethylamino)benzoate.

Chemical Authentication and Speciation of Salvia Botanicals: An Investigation Utilizing GC/Q-ToF and Chemometrics

Members of the genus Salvia are used as culinary herbs and are prized for their purported medicinal attributes. Since physiological effects can vary widely between species of Salvia, it is of great importance to accurately identify botanical material to ensure safety for consumers. In the present study, an in-depth chemical investigation is performed utilizing GC/Q-ToF combined with chemometrics. Twenty-four authentic plant samples representing five commonly used Salvia species, viz. S. apiana, S. divinorum, S. mellifera, S. miltiorrhiza, and S. officinalis, are analyzed using a GC/Q-ToF technique. High-resolution spectral data are employed to construct a sample class prediction (SCP) model followed by principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). This model demonstrates 100% accuracy for both prediction and recognition abilities. Additionally, the marker compounds present in each species are identified. Furthermore, to reduce the time required and increase the confidence level for compound identification and the classification of different Salvia species, a personal compound database and library (PCDL) containing marker and characteristic compounds is constructed. By combining GC/Q-ToF, chemometrics, and PCDL, the unambiguous identification of Salvia botanicals is achieved. This high-throughput method can be utilized for species specificity and to probe the overall quality of various Salvia-based products.

Current Role of Mass Spectrometry in the Determination of Pesticide Residues in Food

The extensive use of pesticides represents a risk to human health. Consequently, legal frameworks have been established to ensure food safety, including control programs for pesticide residues. In this context, the performance of analytical methods acquires special relevance. Such methods are expected to be able to determine the largest number of compounds at trace concentration levels in complex food matrices, which represents a great analytical challenge. Technical advances in mass spectrometry (MS) have led to the development of more efficient analytical methods for the determination of pesticides. This review provides an overview of current analytical strategies applied in pesticide analysis, with a special focus on MS methods. Current targeted MS methods allow the simultaneous determination of hundreds of pesticides, whereas non-targeted MS methods are now applicable to the identification of pesticide metabolites and transformation products. New trends in pesticide analysis are also presented, including approaches for the simultaneous determination of pesticide residues and other food contaminants (i.e., mega-methods), or the recent application of techniques such as ion mobility–mass spectrometry (IM–MS) for this purpose.

Pesticide residues in fruits and vegetables in Indonesia: findings of five-year proficiency testing

The first proficiency testing of pesticides in fruits and vegetables in Indonesia is reported. This report covers the findings of five-year proficiency testings. Every year, from 2016 to 2020, 18-25 laboratories join the proficiency testings and analyze 5-11 pesticides in tomato, orange, lettuce, brown rice, strawberry respectively. The number of laboratories participating in the proficiency testings tends to increase, although only 38 % of the laboratories are able to report all pesticides. More than 72 % of participants use QuEChERS or its modifications for sample preparation, all participants use gas chromatography or liquid chromatography for separation, at least 20 % of participants still rely on detectors other than mass spectrophotometer for detection, and 20 %-60 % of participants use matrix-matched calibration for quantification. The performance of laboratories is evaluated as z-score with an average of 90.8 % achieves satisfactory results while 3.3 % and 5.9 % achieve questionable and unsatisfactory results correspondingly. Overall, the performance of laboratory participants during proficiency testings is good. However, improvement is still needed, especially for the number of target pesticides for multi-residue pesticide analysis. Moreover, unsatisfactory z-scores are likely to be resulted from laboratories which use conventional solvent extraction, use detectors other than mass spectrometers, and are not accredited.

In vitro metabolism of tebuconazole, flurtamone, fenhexamid, metalaxyl-M and spirodiclofen in Cannabis sativa L. (hemp) callus cultures

BACKGROUND

Cannabis sativa L. (hemp) is a medicinal plant producing various cannabinoids. Its consumption is legalized for medical use due to the alleged positive health effects of these cannabinoids. To satisfy the demand, C. sativa plants are propagated in contained growth chambers. During indoor propagation, pesticides usually are used to ensure efficient production. However, pesticide registration and safe application in C. sativa has not been investigated in detail.

RESULTS

With this study the metabolic degradation of pesticides in recently established C. sativa callus cultures was examined. Tebuconazole, metalaxyl-M fenhexamid, flurtamone and spirodiclofen were applied at 10 μm for 21 days. Results were compared with metabolism data obtained from Brassica napus L., Glycine max (L.) Merr., Zea mays L. and Tritium aestivum L. callus cultures as well as in metabolism guideline studies. The successfully established C. sativa callus cultures were able to degrade pesticides by oxidation, demethylation, and cleavage of ester bonds in phase I, as well as glycosylation and conjugation with malonic acid in phase II and III. Initial metabolites were detected after Day (D)7 and were traced at D21.

CONCLUSION

The resulting pathways demonstrate the same main degradation strategies as crop plants. Because metabolites could be the main residue, the exposure of consumers to these residues will be of high importance. We present here an in vitro assay for a first estimation of pesticide metabolism in C. sativa. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Toxicological aspects of cannabinoid, pesticide and metal levels detected in light Cannabis inflorescences grown in Italy

Recently, the cultivation of light Cannabis, with a total THC content less than 0.6%, has been encouraged due to its industrial and therapeutic potential. This has increased the consumption of hemp for both smoking purposes and food preparation. Even so, Cannabis inflorescences are not subject to EU regulations and standards provided for food and tobacco products. A study was carried out on thirty-one inflorescences samples, collected in different Italian regions, in order to determine cannabinoids, pesticides and metals and to evaluate the exposure of consumers to contaminants and ensure a safe consumption. Contents of THC were always below 0.5%, while CBD ranged between 0.3 and 8.64%. The determination of 154 pesticides showed that 87% of the samples contained fungicides and insecticides in the range 0.01-185 μg/g. The most found are spinosad and cyprodinil. The concentration of metals ranged from 1 to more than 100 μg/g and As, Cd, Co, Cr, Hg, Cu, Mo, Ni and V exceeded the regulatory US limits for inhaled Cannabis products, while Pb exceeded them for both oral and inhaled products. These contaminants are intrinsically toxic and may affect public health. Actions are needed to establish regulatory measures and reduce the adverse effects caused by contaminants in Cannabis.

Validation of a high-throughput method for analysis of pesticide residues in hemp and hemp products

Hemp has been an agricultural commodity for millennia, and it has been undergoing a resurgence in interest and production due to its high content of cannabinoids, protein, fiber and other ingredients. For legal possession and use throughout the USA, hemp and hemp products must have delta-9-tetrahydrocannabinol (THC) concentration < 0.3%. As with most crops, pesticides may be applied when farming hemp, which need to be monitored in food, feed, and medicinal products. The aim of this work was to evaluate and validate the recently developed "quick, easy, cheap, effective, rugged, safe, efficient, and robust" (QuEChERSER) sample preparation mega-method to determine pesticide residues in hemp plants, flowers, powders, oils, and pellets. High-throughput analysis of final extracts for 106 targeted pesticides and metabolites from North American monitoring lists entailed: 1) ultrahigh-performance liquid chromatography - tandem mass spectrometry (UHPLC-MS/MS) with column back-flushing, and 2) instrument-top sample preparation + low-pressure gas chromatography (ITSP+LPGC-MS/MS). In QuEChERSER, 2 g sample is extracted with 10 mL 4/1 (v/v) acetonitrile/water by mechanical shaking for 10 min, followed by 3 min centrifugation. For LC, 0.2 mL of extract is taken and solvent exchanged into initial mobile phase followed by 5 min ultra-centrifugation prior to the 10 min analysis. For GC-amenable pesticides, the remaining initial extract is partitioned with 4/1 (w/w) anh. MgSO₄/NaCl, and 1 mL is taken for automated ITSP cleanup in parallel with 10 min LPGC analysis. In the former case, the UHPLC column is back-flushed with 1/1 (v/v) methanol/acetonitrile for 3 min between each injection to keep the system clean and avoid ghost peaks. Multi-level, multi-day validation results achieved 70-120% recoveries with RSDs < 20% for more than 80% of the analytes in hemp protein powder, oil, pellets, and fresh plant (dried hemp plant and flower were too complex). Limits of quantification (LOQs) were < 10 ng/g were achieved for nearly all pesticides, yielding 2.8% false negatives among >13,000 analyte results in the spiked samples. The QuEChERSER method was demonstrated to meet the challenge for several complex hemp matrices.