Development and validation of an analytical method for detecting chlorantraniliprole residues in fresh tea leaves

An efficient method using multiwalled carbon nanotubes(MWCNTs)as dispersive solid-phase extraction sorbent was established for determining chlorantraniliprole residues in fresh tea leaves,which are known to be a troublesome matrix containing abundant pigments,via gas chromatography with an electron capture detector.Acetonitrile was used as the extraction solvent,with sodium chloride enhancing the analyte partition in the organic phase.The optimal mixture of MWCNTs and primary secondary amine(PSA)was based on the distribution of the target analyte recovery and on the clean-up efficiency;while matrix-matched calibration was recommended to combat the matrix effect.Mean recoveries of 95.2%–108.8%were obtained with intraday and interday precisions of less than 7.9%and 10.3%,respectively.Good linearity was observed for concentrations of 0.02–1.0 mg/kg with a correlation coefficient of 0.9984.The limits of detection and quantification were 0.005 mg/kg and 0.02 mg/kg,respectively.The method was employed to investigate the dissipation dynamics of chlorantraniliprole in fresh tea leaves with real field samples.Consequently,the dissipation rates of chlorantraniliprole in fresh tea leaves followed pseudo-first-order kinetics with a half-life of 1.9 d,and the average chlorantraniliprole residue content was below 0.02 mg/kg with a harvest withholding period of 14 d.

Determination of 18 Kinds of Amino Acids in Fresh Tea Leaves by HPLC Coupled with Pre-column Derivatization

A rapid and accurate quantitative method of high performance liquid chromatography( HPLC) with fluorescence detector has been developed for the analysis of 18 kinds of amino acids in fresh tea leaves. The samples were minced and mixed,and extracted with ultra pure water at 90℃ for 20 min. The 6-aminoquinolyl N-hydroxy-succinimidyl carbamate( AQC) was used as pre-column derivatization reagent. Gradient HPLC separation was performed on a C_(18) column( Symmetry C_(18),3. 9 mm × 15 cm,4 μm). Good linearity between concentrations and peak areas was achieved in the concentration range of 5. 0-250 μmol/L for 18 kinds of amino acids. The method was validated by the analysis of five replicates. The 18 kinds of amino acid standards were spiked in fresh tea leaf samples and the average recovery rate was 86. 25%-109. 05% with relative standard deviations( n = 5) ranging from 6. 03% to 10. 56%. The limit of detection( LOD) for the analytes was0. 05-1. 27 μmol/L. The method was successfully applied to the analysis of the 18 kinds of amino acids in fresh tea leaves from east Dongting and west Dongting mountains in Suzhou. The results indicate that the method is simple,rapid,precise and reliable.

Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision

The harvesting time of fresh tea leaves has a significant impact on product yield and quality.The aim of this study was to propose a method for real-time monitoring of the optimum harvesting time for picking fresh tea leaves based on machine vision.Firstly,the shapes of fresh tea leaves were distinguished from RGB images of the tea-tree canopy after graying with the improved B-G algorithm,filtering with a median filter algorithm,binary processing with the Otsu algorithm,and noise reduction and edge smoothing using open and close operations.Then the leaf characteristics,such as leaf area index,average length,and leaf identification index,were calculated.Based on these,the Bayesian discriminant principle and method were used to construct a discriminant model for fresh tea-leaf collection status.When this method was applied to a RGB tea-tree canopy image acquired at 45°shooting angle,the fresh tea-leaf recognition rate was 90.3%,and the accuracy for fresh tea-leaf harvesting status was 98%by cross validation.Hence,this method provides the basic conditions for future tea-plantation operation and management using information technology,automation,and intelligent systems.