Nondestructive and rapid detection of foreign materials in wolfberry by hyperspectral imaging combing with chemometrics

The foreign materials in wolfberries, such as wolfberry flowers, leaves, branches and other plant fruits, can affect the overall grade and economic value of wolfberry. Identifying and removing these foreign materials is a crucial step after wolfberry picking. This study aims to provide a method of hyperspectral imaging combing with chemometrics (HSIcC) for identification of foreign materials in wolfberries. Wolfberry flower, leaf, branch and dogwood are taken as the foreign materials in this paper. Hyperspectral images of wolfberries and four foreign materials are collected in wavelength range of 370–1060 nm. And the analytical model is established. Different spectral preprocessing algorithms, including vector normalization (VN), the savitzky–golay (SG) method, the first-derivative, the second-derivative, and standard normal variable (SNV) transformation, are used and compared. Principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) are employed as data dimension reduction methods. K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) are utilized for modeling. The optimal results are obtained by "SG+CARS +KNN" model, with accuracy for training, validation, and testing set of 100%, 100% and 100%, respectively. The results show that HSIcC can provide a rapid and nondestructive on-line detection method for foreign materials identification of wolfberries.