Detection of multiple pesticide residues on the surface of broccoli based on hyperspectral imaging - GUI Jiangsheng; GU Min; WU Zixian; BAO Xiao’an (浙江大学学报. 农业与生命科学版)
A method for the detection of pesticide residues on broccoli was proposed based on hyperspectral image technology. Four groups of broccoli samples were used as experimental samples, which contained imidacloprid, abamectin and propineb as the first third groups respectively, and the last group was sprayed with water. A total of 180 broccoli samples were scanned by hyperspectral image system in the range of 383.70-1 032.70 nm. The average spectral information of region of interest (ROI) was extracted. Then, piecewise multiplicative scatter correction (PMSC) was adopted to eliminate light scattering of the average spectral information. To increase efficiency of the model and reduce the redundancy of the hyperspectral image, using the principal component analysis (PCA) algorithm and successive projection algorithm (SPA) for feature extraction. Mahalanobis distance (MD), least square support vector machine (LSSVM), artificial neural networks (ANN) and extreme learning machine (ELM) models were created to predict the pesticide residues from full spectra and characteristic wavelengths. The results showed that the optimal model is the SPA-ELM model, and the accuracy of training set is 98.33%, and the correct rate of test set is 96.67%, suggesting that it is feasible to use the principal component analysis algorithm and the artificial neural network algorithm to identify the pesticide residues on the surface of broccoli. In sum, this study develops a new method for rapid and nondestructive detection of pesticide residues on the surface of broccoli.