Quality assessment and geographical origin traceability of Gastrodia elata based on machine vision, flash GC e-nose, HPLC, and machine learning algorithms.

Gastrodia elata Bl. (GE), a widely utilized edible and functional material, exhibits notable quality variations due to distinct growing conditions across different geographical origins, making reliable methods for origin authentication and quality evaluation essential. This study integrated machine vision, flash GC e-nose and HPLC to analyze GE samples from Anhui (AH), Shaanxi (SN), and Yunnan (YN) provinces in China. Results showed comparable color characteristics among geographical origins, but distinct textural differences: SN samples displayed more complex textures with higher surface roughness, YN samples showed richer textural details but less distinct edges and lower periodicity, while AH samples exhibited smoother surfaces. Eleven flavor compounds were identified as potential flavor markers. Although no significant differences were found in the total content of six marker components, three linear machine learning models (PLS, SVM, and LDA) established based on multidimensional features achieved 100% classification accuracy in training and prediction sets, providing a reliable approach for GE geographical origin traceability.