Identification of Pesticide Residue Types in Spinach Leaves Based on Hyperspectral Imaging

Non-destructive identification of pesticide residues in spinach was studied using hyperspectral imaging. The hyperspectral images between 900 nm and 1 700 nm were obtained with the help of hyperspectral imager. The original spectra were corrected by multivariate scatter correction (MSC). The principal component analysis (PCA) was used to analyze the spectral data of different spinach samples, the results showed that PCA could effectively discriminate different kinds of pesticide residues spinach samples on the visualization level. In addition, chi-squared test feature selection algorithm was separately combined with four learning algorithms (e.g. support vector machine, naive Bayes, decision tree and linear discriminant analysis) to get the best bands and optimal discriminant model(linear discriminant model) with the help of 10-fold cross-validation technique. The selected eight characteristic wavelengths are 1 439.3, 1 442.5, 1 445.8, 1 449, 1 452.3, 1 455.5, 1 458.7, 1 462 nm and the prediction accuracy by optimal discriminant model is 0.993 and 10 times of cross validation standard deviation is 0.009. The results show that hyperspectral imaging technology can accurately identify the types of pesticide residues on spinach leaves. © 2018, Science Press. All right reserved.