Identification of Age of Forest Sun-Dried Ginseng Based on Hyperspectral Imaging Technology
The study establishes a method for rapid and non-destructive identification of the age of commercially available forest sun-dried ginseng based on hyperspectral technology combined with machine learning.Using common commercially available forest sun-dried ginseng as the research object,hyperspectral images of ginseng of different ages were first collected,and spectral data were extracted from regions of interest using a threshold segmentation method.The data were then preprocessed using Multiplicative Scatter Correction(MSC)-Savitzky-Golay smoothing(S-G smoothing)-First Derivative(FD)and MSC-S-G smoothing-Second Derivative(SD)to eliminate interference.Three machine learning models,including Support Vector Machine Regression(SVR),Principal Component Regression(PCR),and Partial Least Squares Regression(PLSR),were applied to the preprocessed data.Due to the redundancy in hyperspectral data,three algorithms—Competitive Adaptive Reweighted Sampling(CARS),Successive Projections Algorithm(SPA),and Uninformative Variable Elimination(UVE)were used to select bands and remove redundant wavelength information.The CARS-MSC-S-G smoothing-SD-SVR model showed the best performance,with lower Root Mean Square Errors(RMSEC:0.0027,RMSEP:0.0120),higher Correlation Coefficients(R2C:0.9998,R2P:0.9993),and RPD(38).This model effectively achieved accurate classification of the age of forest sun-dried ginseng.Combining hyperspectral imaging technology with machine learning enables rapid and non-destructive identification of the age of forest sun-dried ginseng.
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