应用化学2025，Vol.42Issue(1)：69-77,9.DOI:10.19894/j.issn.1000-0518.240204
高光谱结合机器学习鉴别林下参参龄
Identification of Age of Forest Sun-Dried Ginseng Based on Hyperspectral Imaging Technology
摘要
Abstract
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.关键词
林下生晒参/不同年限/高光谱成像/鉴别Key words
Forest sun-dried ginseng/Different ages/Hyperspectralimaging/Identification分类
化学化工引用本文复制引用
韩路生,王昱丹,李宇航,李根悦,宋欣宏,杨凯丽,谭鑫,王恩鹏..高光谱结合机器学习鉴别林下参参龄[J].应用化学,2025,42(1):69-77,9.基金项目
国家自然科学基金(No.82073969)、长春市科技局项目(No.21ZGY10)和吉林省科技发展计划项目(Nos.20210401108YY,20220401104YY)资助 Supported by the National Natural Science Foundation of China(No.82073969),the Science and Technology Bureau Project of Changchun(No.21ZGY10)and the Science and Technology Development Program Project of Jilin Province(Nos.20210401108YY,20220401104YY) （No.82073969）
