Optimal Detecting Part of Ophiocordyceps sinensis for Identifying Wild and Cultivated Categories Using Hyperspectrum

Hyperspectral technology has become an important method for identifying wild and cultivated Ophiocordyceps sinensis, but existing studies mainly focus on intact samples. In the actual circulation process, Ophiocordyceps sinensis often breaks, and samples missing the stroma part but retaining the larva part still possess practical identification value. However, accommodating the identification of both intact samples and stroma-missing samples requires clarifying the difference in spectroscopic identification information contribution between the larva part and the stroma part, thereby optimizing the data acquisition strategy for specialized Ophiocordyceps sinensis detection instruments. Based on the segmentation of hyperspectral images of intact Ophiocordyceps sinensis, this study constructed simulated single and mixed part datasets for machine learning model training to analyze the spectral feature differences of different parts. Furthermore, real stroma-missing samples were used to verify the identification capability of the models in the practical scene. Finally, the optimal detecting part was determined by comprehensively considering the highest identification accuracy and the maximum application scope. Both the larva part and the stroma part exhibit effective spectroscopic identification information contribution for identifying wild and cultivated Ophiocordyceps sinensis. However, the spectroscopic identification information contribution of the larva part is greater than that of the stroma part. Multiple comparative experimental results show that the model trained with intact samples can be directly used for the identification of stroma-missing Ophiocordyceps sinensis with the highest accuracy of 98.52%. This conclusion provides a basis for the practical application of hyperspectral technology in Ophiocordyceps sinensis.