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Determination of forbidden and restricted pesticides in flowers and fruits of traditional chinese medicines / 中国药学杂志
Chinese Pharmaceutical Journal ; (24): 404-412, 2016.
Article in Chinese | WPRIM | ID: wpr-859195
ABSTRACT

OBJECTIVE:

To establish a method for simultaneous determination of 187 kinds of pesticides in flowers and fruits of traditional Chinese medicine (TCM) by using gas and liquid chromatography tandem mass spectrometry, taking medlar and honeysuckle as examples.

METHODS:

The forbidden, restrictted and commonly used pesticides were picked out as detecting indexes according to the principals of risk management. The factors affecting the extraction, purification, and detection were optimized, and the final condition was established as follows the samples were extracted with acetonitrile, and cleaned-up by carb solid phase extraction column. The separation of target compounds were performed by capillary or liquid column, and quantitative analysis was carrid out by GC-MS/MS or LC-MS/MS with MRM model. Internal standard was used to improve the accuracy of this method.

RESULTS:

Honeysuckle flower and Wolfberry were used to evaluate the accuracy of this method. The calibration curves were linear in the range of 1-100 ng · mL-1 with correction coefficients of greater than 0.99. The recoveries of more than 88% pesticides were ranged from 70% to 120% at three spiked levels (5, 20 and 100 μg · kg-1).

CONCLUSION:

The detecting indexes in this method cover most forbidden and restricted pesticides, which is meaningful for the safety supervision of TCM. With the advantage of rapidness and accuracy, this method can be used for routine determination of multi-pesticides in flowers and fruits of TCM.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Pharmaceutical Journal Year: 2016 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Pharmaceutical Journal Year: 2016 Type: Article