Traditional Chinese medicine (TCM) is still considered a global complementary or alternative medical system, but exogenous hazardous contaminants remain in TCM even after decocting. Besides, it is time-consuming to conduct a risk assessment of trace elements in TCMs with a non-automatic approach due to the wide variety of TCMs. Here, we present MRTCM, a cloud-computing infrastructure for automating the probabilistic risk assessment of metals and metalloids in TCM. MRTCM includes a consumption database and a pollutant database involving forty million rows of consumption data and fourteen types of TCM potentially toxic elements concentrations. The algorithm of probabilistic risk assessment was also packaged in MRTCM to assess the risks of eight elements with Monte Carlo simulation. The results demonstrated that 96.64% and 99.46% had no non-carcinogenic risk (hazard indices (HI) were < 1.0) for animal and herbal medicines consumers, respectively. After twenty years of exposure, less than 1% of the total carcinogenic risk (CR_t) was > 10^-4 for TCM consumers, indicating that they are at potential risk for carcinogenicity. Sensitivity analysis revealed that annual consumption and concentration were the main variables affecting the assessment results. Ultimately, a priority management list of TCMs was also generated, indicating that more attention should be paid to the non-carcinogenic risks of As, Mn, and Hg and the carcinogenic risks of As and Cr in Pheretima and Cr in Arcae Conch. In general, MRTCM could significantly enhance the efficiency of risk assessment in TCM and provide reasonable guidance for policymakers to optimize risk management.

Earthworms are known to accumulate inorganic contaminants from the soil; they are also used as a traditional Chinese medicine (TCM) called Pheretima, which might cause safety problems with long-term exposure. Here, this study was conducted to determine and analyze the level of heavy metal contamination such as arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), and lead (Pb) in Pheretima and then explore the probabilistic health risks caused by 8 heavy metals in 98 batches of Pheretima using Monte Carlo simulation. A risk assessment strategy was conducted to assess heavy metal–associated health risk of Pheretima based on consumption data. For random consumption sampling, the results found that the non-carcinogenic risk of As is higher than the acceptable level, and the carcinogenic risk levels of As and Cr exceeded the acceptable risk recommended by the USEPA. Cr and As were regarded as the priority metals for risk control in the present study. Finally, it was recommended that the dosing frequency should be less than 24 d/y. In general, this study conducted a probabilistic risk assessment of heavy metals in Pheretima, which would be of significance for policy makers to take effective strategies to improve the quality and safety of Pheretima.

An efficient method for the determination of benzoic acid, sorbic acid and saccharin in soft drinks was developed using the technique of electromembrane extraction coupled with high performance liquid chromatography. Target analytes were extracted into an alkaline solution by applying an electric potential via a liquid membrane supported by hollow fiber. The EME efficiency was tested for several fundamental parameters. The extraction was carried out using 1-octanol as a supported liquid membrane, with an applied potential of 20 V. The pH of the donor phase was maintained at 8.0 and the most suitable solution for the acceptor phase was found to be 0.5 mol L⁻¹ NaOH. The stirring rate was kept at 1200 rpm, and the total extraction time was 30 minutes. The linear range of benzoic and sorbic acids and saccharin was 0.02–2 mg L⁻¹ with correlation coefficients larger than 0.993. The limit of detection ranged from 0.3–0.5 μg L⁻¹, and the limit of quantification ranged from 1.0–1.7 μg L⁻¹. The enrichment factors of benzoic and sorbic acids and saccharin were 179, 122, and 212, respectively. The relative recoveries of the spiked soft drinks were in the range of 89–96%. The results illustrate that it is a simple, promising, and environment-friendly way of detecting and quantifying benzoic and sorbic acids and saccharin in soft drinks.