Profiling of pesticides and pesticide transformation products in Chinese herbal teas

Suspect and nontarget screening of pesticides and their transformation products in agricultural products using liquid chromatography-high-resolution mass spectrometry

Intensive agricultural production involves the extensive use of chemicals, leading to the presence of pesticides and their transformation products (TPs) in agricultural products. Our study developed a high-coverage method to map the occurrence of pesticides and their transformation products in agricultural products using liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Initially, a suspect list of 1265 pesticides was compiled based on in-house standards and online databases to identify potential parent pesticides. Besides, the reported and predicted TPs, as well as the multi-class characteristic fragment ions (CFIs) of pesticides, were summarized. Subsequently, nontarget features were identified by matching with 10226 TPs and 39-classes of CFIs. Both known and unknown parent pesticides and their TPs can be identified via suspect and nontarget screening procedures. Ultimately, the proposed method was applied to strawberry samples to demonstrate its effectiveness. We identified 67 parent pesticides and 57 TPs in 107 samples, with the majority at low concentrations, and preliminary traceability suggesting they may migrate from soil. The findings suggest that our method can enable suspect and nontarget screening of pesticides and their TPs, and it is also applicable to other food matrices. This method may facilitate regulatory agencies in strengthening the supervision of unknown risk substances or TPs, thereby comprehensively safeguarding consumer health.

Review of the Terminology, Approaches, and Formulations Used in the Guidelines on Quantitative Risk Assessment of Chemical Hazards in Food

This paper reviews the published terminology, mathematical models, and the possible approaches used to characterise the risk of foodborne chemical hazards, particularly pesticides, metals, mycotoxins, acrylamide, and polycyclic aromatic hydrocarbons (PAHs). The results confirmed the wide variability of the nomenclature used, e.g., 28 different ways of referencing exposure, 13 of cancer risk, or 9 of slope factor. On the other hand, a total of 16 equations were identified to formulate all the risk characterisation parameters of interest. Therefore, the present study proposes a terminology and formulation for some risk characterisation parameters based on the guidelines of international organisations and the literature review. The mathematical model used for non-genotoxic hazards is a ratio in all cases. However, the authors used the probability of cancer or different ratios, such as the margin of exposure (MOE) for genotoxic hazards. For each effect studied per hazard, the non-genotoxic effect was mostly studied in pesticides (79.73%), the genotoxic effect was mostly studied in PAHs (71.15%), and both effects were mainly studied in metals (59.4%). The authors of the works reviewed generally opted for a deterministic approach, although most of those who assessed the risk for mycotoxins or the ratio and risk for acrylamide used the probabilistic approach.

Screening and identification of unknown chemical contaminants in food based on liquid chromatography-high-resolution mass spectrometry and machine learning

Unknown or unexpected chemical contaminants and/or their transformation products in food that may be harmful to humans need to be discovered for comprehensive safety evaluation. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful tool for detecting chemical contaminants in food samples. However, identifying all of peaks in LC-HRMS is not possible, but if class information is known in advance, further identification will become easier. In this work, a novel MS2 spectra classification-driven screening strategy was constructed based on LC-HRMS and machine learning. First, the classification model was developed based on machine learning algorithm using class information and experimental MS2 data of chemical contaminants and other non-contaminants. By using the developed artificial neural network classification model, in total 32 classes of pesticides, veterinary drugs and mycotoxins were classified with good prediction accuracy and low false-positive rate. Based on the classification model, a screening procedure was developed in which the classes of unknown features in LC-HRMS were first predicted through the classification model, and then their structures were identified under the guidance of class information. Finally, the developed strategy was tentatively applied to the analysis of pork and aquatic products, and 8 chemical contaminants and 11 transformation products belonging to 8 classes were found. This strategy enables screening of unknown chemical contaminants and transformation products in complex food matrices.

Regulation and standardization of herbal drugs: Current status, limitation, challenge's and future prospective

Herbal drugs (HD) or traditional drugs have been used worldwide for centuries, especially in the developing countries. Global market of HD reaches billion of USD annually and increases every year. For ensuring the safety and efficacy of HD, the Drug Agency/Authority issues regulations for the registration & application of new HD, their manufacturing processes, controlling and monitoring in the market. The efficacy and safety of HD depend on their whole chemical contents. Quality assessment of HD should be performed using standardization methods according to the current Pharmacopoeias or Materia Medica. Unfortunately, the official methods of the compendia cannot be applied for evaluation of mixed herbs and their preparations.; HD's producers should develop, validate, and standardize the method for the quality assessment of their own specific products. Therefore, assuring the safety and efficacy of HD remains a challenging task due to the complex nature of HD, that typically consist of many constituents of herbs/extracts whose quality may vary among different sources of materials. This present review will describe, compare, and discuss the regulations and standardization methods of HD from US, EU countries, Japan, Taiwan, Hong Kong and Indonesia. The official standardization methods of HD, their current criteria, limitations, challenge and future prospective will be described and discussed. Official methods for quality assessment of HD should be state of the art, fast, low-cost, accurate and precise, and could be used for evaluation of all kinds of HD.

The bitter side of teas: Pesticide residues and their impact on human health

Tea (Camellia sinensis) is one of the most widely consumed non-alcoholic beverages globally, known for its rich composition of bioactive compounds that offer various health benefits to humans. However, the cultivation of tea plants often faces challenges due to their high vulnerability to pests and diseases, resulting in the heavy use of pesticides. Consequently, pesticide residues can be transferred to tea leaves, compromising their quality and safety and potentially posing risks to human health, including hormonal and reproductive disorders and cancer development. In light of these concerns, this review aims to: (I) present the maximum limits of pesticide residues established by different international regulatory agencies; (II) explore the characteristics of pesticides commonly employed in tea cultivation, encompassing aspects such as digestion, bioaccessibility, and the behavior of pesticide transfer; and (III) discuss the effectiveness of detection and removal methods for pesticides, the impacts of pesticides on both tea plants and human health and investigate emerging alternatives to replace these substances. By addressing these critical aspects, this review provides valuable insights into the management of pesticide residues in tea production, with the goal of ensuring the production of safe, high-quality tea while minimizing adverse effects on human health.

Au-Ag OHCs-based SERS sensor coupled with deep learning CNN algorithm to quantify thiram and pymetrozine in tea

Pesticide residue detection in food has become increasingly important. Herein, surface-enhanced Raman scattering (SERS) coupled with an intelligent algorithm was developed for the rapid and sensitive detection of pesticide residues in tea. By employing octahedral Cu₂O templates, Au-Ag octahedral hollow cages (Au-Ag OHCs) were developed, which improved the surface plasma effect via rough edges and hollow inner structure, amplifying the Raman signals of pesticide molecules. Afterward, convolutional neural network (CNN), partial least squares (PLS), and extreme learning machine (ELM) algorithms were applied for the quantitative prediction of thiram and pymetrozine. CNN algorithms performed optimally for thiram and pymetrozine, with correlation values of 0.995 and 0.977 and detection limits (LOD) of 0.286 and 29 ppb, respectively. Accordingly, no significant difference (P greater than 0.05) was observed between the developed approach and HPLC in detecting tea samples. Hence, the proposed Au-Ag OHCs-based SERS technique could be utilized for quantifying thiram and pymetrozine in tea.

Method Validation for Multi-Pesticide Residue Determination in Chrysanthemum

The chrysanthemum can be consumed in various forms, representing the "integration of medicine and food". Quantitative analysis of multi-pesticide residues in chrysanthemum matrices is therefore crucial for both product-safety assurance and consumer-risk evaluation. In the present study, a simple and effective method was developed for simultaneously detecting 15 pesticides frequently used in chrysanthemum cultivation in three matrices, including fresh flowers, dry chrysanthemum tea, and infusions. The calibration curves for the pesticides were linear in the 0.01-1 mg kg^-1 range, with correlation coefficients greater than 0.99. The limits of quantification (LOQs) for fresh flowers, dry chrysanthemum tea, and infusions were 0.01-0.05 mg kg^-1, 0.05 mg kg^-1, and 0.001-0.005 mg L^-1, respectively. In all selected matrices, satisfactory accuracy and precision were achieved, with recoveries ranging from 75.7 to 118.2% and relative standard deviations (RSDs) less than 20%. The validated method was then used to routinely monitor pesticide residues in 50 commercial chrysanthemum-tea samples. As a result, 56% of samples were detected with 5-13 pesticides. This research presents a method for the efficient analysis of multi-pesticide residues in chrysanthemum matrices.

Targeted and non-targeted analysis for the investigation of pesticides influence on wheat cultivated under field conditions

A comprehensive approach was applied to evaluate the effects of pesticides on the metabolism of wheat (Triticum aestivum L). The application of commercially available pesticide formulations under field cultivation conditions provided a source of metabolic data unlimited by model conditions, representing a novel approach to study the effects of pesticides on edible plants. Gas and liquid chromatography coupled to tandem mass spectrometry were employed for targeted and non-targeted analysis of wheat roots and shoots sampled six times during the six-week experiment. The applied pesticides: prothioconazole, tebuconazole, fluoxastrobin, diflufenican, florasulam, and penoxulam were found at concentrations ranging 0.0070-25.20 mg/kg and 0.0020-2.2 mg/kg in the wheat roots and shoots, respectively. The following pesticide metabolites were identified in shoots: prothioconazole-desthio (prothioconazole metabolite), 5-(4-chlorophenyl)-2,2-dimethyl-3-(1,2,4-triazol-1-ylmethyl)pentane-1,3-diol (tebuconazole metabolite), and N-(5,8-dimethoxy[1,2,4]triazolo[1,5-c]pyrimidin-2-yl)-2,4-dihydroxy-6-(trifluoromethyl)benzene sulphonamide (penoxulam metabolite). The metabolic fingerprints and profiles changed during the experiment, reflecting the cumulative response of wheat to both its growth environment and pesticides, as well as their metabolites. Approximately 15 days after the herbicide treatment no further changes in the plant metabolic profiles were observed, despite the presence of pesticide and their metabolites in both roots and shoots. This is the first study to combine the determination of pesticides and their metabolites plant tissues with the evaluation of plant metabolic responses under field conditions. This exhaustive approach contributes to broadening the knowledge of pesticide effects on edible plants, relevant to food safety.