Rapid Identification of Dendrobium Species Using Near-Infrared Hyperspectral Imaging Technology

<i>Dendrobium officinale</i> is a valuable Chinese medicinal herb, but distinguishing it from other Dendrobium species after processing is challenging, leading to low classification accuracy and time-consuming analysis. This study proposes a rapid classification model based on near-infrared hyperspectral imaging (NIR-HSI), incorporating data preprocessing and feature wavelength selection. Five Dendrobium species-<i>D. officinale</i>, <i>D. aphyllum</i>, <i>D. chrysanthum</i>, <i>D. fimbriatum</i>, and <i>D. thyrsiflorum</i>-were used. Spectral preprocessing techniques like normalization and smoothing were applied, and Support Vector Machine (SVM) models were constructed. Normalization improved both accuracy and stability, with the full-spectrum Normalize-SVM model achieving 97% accuracy for calibration and 88% for prediction. <i>D. chrysotoxum</i> performed best, with all metrics reaching 100%, while <i>D. aphyllum</i> had poor classification (40% recall and 51.74% F1 score). To improve efficiency and performance, feature wavelength selection was performed using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA). The CARS-Normalize-SVM model yielded the best results: 98% accuracy for calibration and 96% for prediction, improving by 1% and 8%, respectively. D. aphyllum's classification also improved significantly, with a 100% recall rate and 95.24% F1 score. These findings highlight hyperspectral imaging's potential for rapid Dendrobium species identification, supporting future quality control and market supervision.