BACKGROUND: Recently, chlorfenapyr and diafenthiuron have been widely used to prevent and control diseases and pests in tea production. However, rare studies have investigated the dissipation patterns of chlorfenapyr, diafenthiuron and their metabolites simultaneously in tea matrices. Here, we established an analytical method to investigate the degradation patterns of five target compounds in tea shoots and made tea samples. Moreover, the dietary intake risk assessment of chlorfenapyrdiafenthiuron mixture among Chinese populations was evaluated based on the supervised field experiment.RESULTS: The mean recoveries of the primary analytes at five spiking levels were between 95.6% and 112.6% in tea shoots and made tea, respectively, and the values of RSD (relative standard deviation) were lower than 9.7% for all the target analytes. The field trial results showed that the half-lives of chlorfenapyr and diafenthiuron based on the residue definition were 10.0-12.4 days and 4.3-5.9 days, respectively, in tea shoots. For the dietary intake risk assessment, the risk quotient (RQ) values in made tea ranged from 30.4% to 73.9% at the pre-harvest interval of 14 days, which were significantly less than 100%.CONCLUSION: The accuracy and precision of the developed method were satisfied by the measurement requirements according to the validation results. The dynamic dissipation experiments suggested that diafenthiuron was much easier to dissipate than chlorfenapyr. Moreover, the existence of tralopyril made the half-life of chlorfenapyr significantly increase, indicating that practical application of chlorfenapyr should take careful consideration of its metabolite. Finally, the potential chronic dietary risks of the chlorfenapyr-diafenthiuron mixture to human communities were within the acceptable range.

The “design–build–test–learn” (DBTL) cycle has been adopted in rational high-throughput screening to obtain high-yield industrial strains. However, the mismatch between build and test slows the DBTL cycle due to the lack of high-throughput analytical technologies. In this study, a highly efficient, accurate, and noninvasive detection method of gentamicin (GM) was developed, which can provide timely feedback for the high-throughput screening of high-yield strains. First, a self-made tool was established to obtain data sets in 24-well plates based on the color of the cells. Subsequently, the random forest (RF) algorithm was found to have the highest prediction accuracy with an R 2 value of 0.98430 for the same batch. Finally, a stable genetically high-yield strain (998 U/mL) was successfully screened out from 3005 mutants, which was verified to improve the titer by 72.7% in a 5 L bioreactor. Moreover, the verified new data sets were updated on the model database in order to improve the learning ability of the DBTL cycle.