Determination of Geographical Origin and Anthocyanin Content of Black Goji Berry (Lycium ruthenicum Murr.) Using Near-Infrared Spectroscopy and Chemometrics

In order to rapidly and efficiently determine geographical origin and characterization categories in five varieties of black Goji berry (Lycium ruthenicum Murr.), near-infrared (NIR) spectroscopy and chemometrics were utilized for data acquisition. Using this data, synergy interval partial least squares (Si-PLS), linear discriminant analysis (LDA), K-nearest neighbors (KNN), back propagation artificial neural network (BP-ANN) and least-squares support vector machine (LS-SVM) regression were systematically evaluated and compared during model development. LS-SVM was initially performed to calibrate the discrimination model to identify the geographical origins and categories of the black Goji berry samples. Compared with other models, the recognition rate of LS-SVM was more than 98.18 %, which showed excellent generalization for identification results. Total anthocyanin content was closely related with the quality of black Goji berry. Synergy interval partial least squares (Si-PLS) was applied to develop the prediction model of total anthocyanin content. The model was optimized by a leave-one-out cross-validation, and model performance was evaluated by assessing the root mean square error of the prediction (RMSEP) and correlation coefficient (R t ) in the prediction set. Experimental results showed that the optimum results of the Si-PLS model were achieved as follows: RMSEP = 0.602 mg/g and R t = 0.899 in the prediction set. The overall results sufficiently demonstrate that spectroscopy coupled with the Si-PLS regression tool has the potential to successfully discriminate black Goji berry varieties.