A portable short-wavelength infrared microscope hyperspectral imager (SMHI) combined with machine learning algorithms for the purpose of classifying geographical origins as well as root types of Lindera aggregata is developed. The spectral range of the SMHI system is 1090–1820 nm (5500–9100 cm-1) with spectral and spatial resolutions of 4 nm and 27.3 μm, respectively. Utilizing PCA-RF algorithms, the geographic origin of tuberous roots and leaves from five different origins were classified with accuracies of 97.5% and 97.8%, respectively. In addition, spatial identification of tuberous root and taproot tubers in a mixed sample was done with an accuracy of 98.98%. The accuracy of origin classification and spatial identification are high enough which indicate the significant potential of applying SMHI system into the non-invasive spatial mapping and rapid quality assessment of medicinal herbs.

便携式短波长红外显微镜高光谱成像仪 (SMHI) 与机器学习算法相结合，用于对Lindera Aggregata的地理起源和根类型进行分类被开发。SMHI 系统的光谱范围为 1090–1820 nm (5500–9100 cm-1)，光谱和空间分辨率分别为 4 nm 和 27.3 μm。利用 PCA-RF 算法，对来自五个不同来源的块茎根和叶的地理来源进行分类，准确率分别为 97.5% 和 97.8%。此外，对混合样品中块根和直根块茎的空间识别准确率为98.98%。原产地分类和空间识别的准确性足够高，这表明将 SMHI 系统应用于无创空间测绘和药材质量快速评估的巨大潜力。