Development of a method for analysis and risk assessment of residual pesticides in ginseng using liquid and gas chromatography-tandem mass spectrometry

In this study, we developed a method for detecting 335 pesticides in ginseng using liquid chromatography quadrupole mass spectrometry (LC-MS/MS) and gas chromatography quadrupole mass spectrometry (GC-MS/MS). Additionally, the linearity, sensitivity, selectivity, accuracy, and precision of the method was validated. The limits of detection (LOD) and limits of quantification (LOQ) for the instrument used in these experiments was 0.1-5.8 μg/kg and 0.3-17.5 μg/kg, respectively. The average recovery was 71.6-113.4%. From 2016 to 2019, 467 ginseng samples were analyzed, of which 304 samples detected pesticide residues, but most of them were below the standard. It can be observed that the hazard quotient (HQ) of ginseng for detected pesticides was less than 1, thus implying that the risk was low. Hence, in this study, we developed a specific, reliable, and suitable method for a fast and simultaneous analysis of 335 pesticides in ginseng.

A novel concept of Q-markers: Molecular connectivity index

BACKGROUND

Drugs derived from botany have been playing essential role in both clinical treatment and pharmaceutical industry, unfortunately our worry is still that its quality and therapeutic efficacy are inconsistent. Recently many scientists launched a new project on quality (Q)-marker of medicinal herbs, this study was thus designed to generate a novel concept of quality (Q)-markers: molecular connectivity index (MCI), and to test and verify the new concept of molecular connectivity index (MCI).

METHODS

The first-order term (¹χ) was selected to calculate and study quality (Q)-marker for TCM. Houttuynia cordata Thunb. (HCT) was adopted as a model to verify the hypothesis. Volatile oils of HCT were determined using gas chromatography-mass (GC-MS). SIMCA 13.0 and SPSS 21.0 were used to deal with the data.

RESULTS

The minimum of the MCI values was 1.273, belonging to the peak 15, but the maximum (12.822) belonged to the peak 34, and the average value of fifty volatile oils was 5.798. The results demonstrated that MCI was the principle component, and monoterpenoid and sesquiterpenoid were also the principle components in oils. Fig. 2a shows peak 5, 24, 34 were the significant ingredients, while Fig. 2b shows peak 2, 5, 24 were the significant components.

CONCLUSION

The data demonstrated that MCI was associated with the structure of molecules and the therapeutic efficacy, MCI could directly exhibit the relationship between ingredients and effectiveness of Traditional Chinese Medicine (TCM). So MCI could be a potential and promising parameter for quality (Q)-marker. Therefore, MCI may be developed as a novel potential concept to control the quality of TCM.