Species classification and origin identification of Lonicerae japonicae flos and Lonicerae flos using hyperspectral imaging with support vector machine

Lonicerae japonicae flos (Jinyinhua, JYH) and Lonicerae flos (Shanyinhua, SYH) have high medical and economical value. Due to their similar appearance, the more expensive JYH is often adulterated with the cheaper SYH for economic gain. In this study, near-infrared hyperspectral imaging (HSI) was used to identify the geographical origins of JYH and SYH and differentiate JYH from SYH. Support vector classification (SVC) models using linear kernel function were established to achieve the research goals. For the identification of geographical origin, we explored the impact of different sample batches on classification performance. The overall classification accuracy of JYH and SYH was in the range of 60.10-85.59% and 63.35-91.67%, respectively. For species classification, the impact of sample geographical origins and sample batches on model performances was explored. The overall classification accuracy for distinguishing JYH and SYH was 98.46-100%. These results demonstrated the significant impact of sample sources on the performance of the models. Using SVC models, the important wavelengths contributing more to the classification were identified by recursive feature elimination (RFE). The results showed that HSI holds great potential for the identification of JYH and SYH, as well as their geographical origins. This technique can provide crucial technical support for the development and standardization of the Traditional Chinese Medicine industry.