Wenyujin Rhizoma Concisum (PJH), the dried rhizome of Curcuma wenyujin Y.H. Chen et C. Ling, is a high-value protected geographical indication (PGI) industrial-medicinal crop rich in terpenes. The PJH market is plagued by inferior substitutes from non-PGI regions, while traditional detection methods are incompetent for industrial-scale quality control. This study aimed to construct a customized hyperspectral imaging -artificial intelligence platform for PJH’s rapid geographical traceability and quality assessment. A customized deep learning model (DeepGTNet) integrating 1D convolution, adaptive structure and lightweight modular design was proposed, specifically designed for hyperspectral data, achieved 99.29% recognition accuracy and a Kappa coefficient of 99.11% for geographical traceability, significantly outperforming conventional machine learning methods. Robust random forest and partial least squares regression models were established through optimized wavelength selection and feature fusion, enabling non-destructive prediction of six key terpenes with high performance (R² > 0.8, RPD ≥ 2.0). Additionally, the structure-spectrum relationship between terpene functional groups and HSI bands was clarified. This research provides a reliable and comprehensive solution for origin traceability and quality evaluation of PJH, and promotes intelligent sustainable development of the industrial crop processing industry. • Hyperspectral image database of Pianjianghuang from different geographical regions. • Terpenes’ functional groups correlate with characteristic spectral bands. • DeepGTNet model enables geographical authentication from hyperspectral data. • Terpene content can be quantitatively predicted from intact samples.
Liu et al. (Sat,) studied this question.