Identification of Pueraria lobata origin using terahertz precision spectroscopy and CNN-transformer hybrid network algorithm.

This study addresses the underexplored potential of terahertz (THz) spectroscopy for geographical origin authentication of Pueraria lobata. We developed a novel non-destructive approach integrating THz spectroscopy with a CNN-Transformer hybrid network to classify samples from eight Chinese regions. High-Performance Liquid Chromatography (HPLC) validated the correlation between THz spectral features and bioactive components (puerarin, daidzein, daidzin). Comparative analysis with Raman spectroscopy and five machine learning algorithms demonstrated THz spectroscopy's superiority. Under optimal conditions, the hybrid model reached an accuracy of 91.67%, which is significantly higher than that of traditional methods (60.42% to 64.58%) and the standard CNN architecture(85.42%). Additionally, it achieved perfect classification (F1-score = 1.000) for the Jiangxi/Shaanxi samples.These results establish THz spectroscopy coupled with deep learning as a robust, accurate tool for origin traceability in traditional medicine quality control.