Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng.

ABSTRACT: Red ginseng (RG) has been traditionally valued in Northeast Asia for its health-enhancing properties. Recent advancements in hyperspectral imaging (HSI) offer a non-destructive, efficient, and reliable method to assess critical quality indicators of RG, such as reducing sugar content (RSC), water content (WC), and hollow rate (HR). This study developed predictive models using HSI technology to monitor these quality indicators over the spectral range of 400-1700 nm. Image features were enhanced using Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), followed by classification through Spectral Angle Mapping (SAM). The best-performing model for RSC achieved an R² value of 0.6198 and a root mean square error (RMSE) of 0.013. For WC, the optimal model obtained an R

SUBMITTER: Bai X 

PROVIDER: S-EPMC11422046 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature