Tanshinone I

The evaluation of the herbal medicine quality often involves time-consuming and labor-intensive high-precision instrument testing.This study used Salvia miltiorrhiza as a case study, with 120 samples collected from various regions.Hyperspectral data, Fourier Transform Near-IR data, heavy metals and harmful elements data, and active ingredient data were systematically gathered.Using multivariate statistical anal., multimodal information integration, and deep learning algorithms, a geog. origin tracing model was developed, achieving an accuracy rate of 100 %.Addnl., spectral-ingredient prediction models for five heavy metals and harmful elements and four active components were established, allowing the heavy metals and harmful elements and active components of Salvia miltiorrhiza to be rapidly and accurately assessed.This study compared the performance of the hyperspectral and FT-NIR techniques in herbal medicine quality control.The results indicated that hyperspectral demonstrated broader applicability and superior performance in predicting heavy metals and harmful elements contents, whereas FT-NIR was more effective in analyzing chem. constituents.This research provides a scientific basis for Salvia miltiorrhiza origin tracing and quality prediction while also confirming the potential of multidimensional spectra techniques in the quality evaluation.