The scent fingerprint of Rosa laevigata Michx., Rosaceae, samples harvested at different periods was investigated. Principal component analysis (PCA) and discriminant factor analysis (DFA) were done on the scent response values measured by an electronic nose (EN) sensor. Statistical quality control (SQC) analysis was also conducted. The R. laevigata samples were clustered into two categories after being analyzed by PCA and DFA. One cluster consisted of samples No. 1 to No. 6, and the other consisted of samples No. 7 to No. 10. The combination of EN, PCA, and DFA for scent analysis can be used for the quality control of traditional Chinese medicines.
harvest; Rosa laevigata; quality; scent
