Quantitative detection of mixed pesticide residue of lettuce leaves based on hyperspectral technique

摘要

To explore the best method for non-destructively and accurately quantitative detection of mixed pesticide residue in vegetables, the mixed pesticide (fenvalerate and dimethoate) of lettuce leaves was used as the research object detected by hyperspectral technique. The hyperspectral data was preprocessed using standard normalized variable. Then two different kinds of characteristic wavelengths were selected using competitive adaptive reweighed sampling (CARS) and random forest-recursive feature elimination (RF-RFE), respectively. The least squares support vector regression (LSSVR) model results of predicting fenvalerate and dimethoate showed that the CARS-screened wavelengths had the best modeling results for fenvalerate with RP2 of 0.8203 and RMSEP of 0.0222, and the RF-RFE-screened wavelengths had the best results for dimethoate with RP2 of 0.8712 and RMSEP of 0.0186. To simplify the calibration model, successive projections algorithm (SPA) was used for the second selection of characteristic wavelengths. Finally, the CARS-SPA-LSSVR model for predicting fenvalerate achieved the accuracy with RP2 of 0.8890 and RMSEP of 0.0182, RF-RFE-SPA-LSSVR model for predicting dimethoate with RP2 of 0.9386 and RMSEP of 0.0077. Thus, hyperspectral technique can be applied to quantitatively detect the mixed pesticide residue of lettuce leaves.