Analysis of the quality characteristics of Gastrodia elata across different climatic zones and a study on FTIR spectral identification models

Significant differences in environmental factors such as temperature, precipitation, and elevation across different climatic zones are key factors influencing the accumulation of secondary metabolites in Gastrodia elata . In this study, Gastrodia elata samples collected from different geographical origins were classified by climatic zone. The contents of total polysaccharides, total polyphenols, and total flavonoids were determined, and Fourier-transform infrared (FTIR) spectroscopy combined with machine learning algorithms was employed for differential analysis and traceability based on climatic zones. The results indicate that the total polysaccharide, total polyphenol, and total flavonoid contents of Gastrodia elata vary across different climatic zones. The combination of FTIR and machine learning (XGBoost) achieved a classification accuracy of 92.86%, with 867.81 cm −1 identified as the most important characteristic wavenumber. This study elucidated the patterns of variation in the active components of Gastrodia elata across different climatic zones, established a machine learning-based identification method using FTIR spectroscopy.