Estimation of nutrient contents in wolfberry (Lycium barbarum L.) based on hyperspectral analysis

Abstract

Rapid and accurate determination of the nutrient contents in wolfberry (Lycium barbarum L.) is of great significance for identifying the quality and origin of the fruit. Compared to traditional chemical analysis methods, hyperspectral remote sensing has the advantages of high speed and low cost. In this study, the dried fruits of wolfberry (cultivar ‘Ningqi No. 7’) taken from the Huinong, Yinchuan, Zhongning, and Tongxin regions of Ningxia, China, in 2020 were selected as samples. Two methods, the variable importance measure (VIM) under random forest and the successive projection algorithm (SPA), were applied to select the hyperspectral characteristic variables. The test set coefficient of determination (R2p), root mean square error of prediction (RMSEP), and relative percent deviation (RPD) for different pre-treatments and characteristic variable selection methods were compared. Finally, the optimal estimation models of partial least squares regression (PLSR) for the contents of eight nutrients in wolfberry were established. The results were as follows: (1) The variation trends of the original hyperspectral reflectance curves of wolfberries from different yield areas and harvest dates were similar, and the spectral characteristic absorption bands were significantly enhanced after first-order differential transformation. (2) After extracting the characteristic bands using SPA, the RPD values were all above 2.0, and the estimation performance was significantly better than that of the full bands. (3) The optimal estimation model for total sugar (TS), crude protein (CP), and Ca was found to be SG-MSC-SPA-PLSR; the optimal estimation model for Lycium barbarum polysaccharide (LBP), Mn, and Zn was found to be SG-2ndD-SPA-PLSR; and the optimal estimation model for Cu and Fe was found to be SG-1stD-SPA-PLSR. The results have important reference value for the quality evaluation and origin traceability of wolfberry.

Hyperspectral imaging combined with GA‐SVM for maize variety identification

“…Lodging resistance of maize varieties was identified by using hyperspectral imaging technology combined with machine learning algorithms; the SPA‐SVM model worked best with 97.40% and 98.33% in the training and test sets, respectively (Zhang, Li, et al., 2022; Zhang, Zhang, et al., 2022). Non‐destructive identification of black wolfberry and prickly berry based on hyperspectral imaging technology, the recognition rates of ELM and SVM models established by FS and SPA are 100% (Zhao et al., 2021). The accuracy of the CSA‐SVM model under the combination of spectral and texture features reached 96.57% based on hyperspectral imaging technology (Wang et al., 2021).…”