Objective To achieve accurate, nondestructive and low-cost identification of Panax ginseng age, a P. ginseng age identification method was established in this study based on hyperspectral imaging technology combined with machine learning. Methods Hyperspectral images of 84 P. ginseng samples and hyperspectral data of 1 680 regions of interest were obtained by acquiring P. ginseng hyperspectral images in the visible-near infrared (VNIR) and short-wave infrared (SWIR) bands from one to seven years old in Tonghua, Jilin, China, respectively. The hyperspectral data of ginseng samples were preprocessed with multiple scattering correction (MSC), standard normal variation (SNV), Savitzky-Golay smoothing, first-order derivative (FD) and second-order derivative (SD) in the VNIR, SWIR and VNIR + SWIR fusion bands, and then combined with partial least squares discriminant analysis (PLS-DA), linear Then, we combined PLS-DA and LinearSVC discriminant analysis methods to establish the identification models of P. ginseng years in two classification scales distinguished by “medicinal food”, three classification scales distinguished by greater than, less than, and equal to five years, and seven classification scales distinguished by seven years, respectively. Results In the VNIR 410—720 nm band range, there was an overall trend of sequential decrease in the average spectral reflectance of P. ginseng from one year to seven years at the same wavelength. The results of confusion matrix evaluation of different classification recognition models showed that the LinearSVC model with FD preprocessing in SWIR band and fusion band had better classification and higher accuracy at three annual scales, and the prediction set accuracy of 2, 3 and 7 classification models were 99.60%, 98.41% and 95.24%, respectively. The recognition models built using the feature bands screened by the continuous projection algorithm (SPA) have higher accuracy at 2 and 3 classifications, and use fewer bands for more efficient classification and recognition. Conclusion Hyperspectral imaging technology combined with machine learning and feature band screening methods can better achieve the identification of the age of P. ginseng of specific origin, and provide a reference for realizing the practical applications of this technology in P. ginseng age identification and quality control.

hyperspectral imaging  /  machine learning  /  characteristic bands  /  Panax ginseng C. A. Meyer  /  age identification