Extended application of deep learning combined with 2DCOS: Study on origin identification in the medicinal plant of Paris polyphylla var. yunnanensis.

Abstract

Medicinal plants are very important to human health, and ensuring their quality and rapid evaluation are the current research concerns. Deep learning has a strong ability in recognition. This study extended it to the identification of medicinal plants from the perspective of spectrum. In order to realise the rapid identification and provide a reference for the selection of high-quality resources of medicinal plants, a combination of deep learning and two-dimensional correlation spectroscopy (2DCOS) was proposed. For the first time, Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR) spectroscopy 2DCOS images combined with residual neural network (ResNet) was used for the origin identification of Paris polyphylla var. yunnanensis. In total 1593 samples were collected and 12821 2DCOS images were drawn. The climate of different origins was briefly analysed. The xishuangbanna, puer, lincang, honghe and wenshan are the five regions with more ecological advantages. The synchronous 2DCOS models of FT-MIR and NIR could realise origin identification with the accuracy of 100%. The synchronous images were suitable for the identification of medicinal plants with complex systems. The full band, feature band and different contour models had no big difference in distinguishing ability, so they were not the key factors affecting the discrimination results. The ResNet models established were stable, reliable, and robust, which not only solved the problem of origin identification, expanded the application field of deep learning, but also provided practical reference for the related research of other medicinal plants.