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

作　　者：韩路生 王昱丹 李宇航 李根悦 宋欣宏 杨凯丽 谭鑫[2] 王恩鹏 HAN Lu-Sheng;WANG Yu-Dan;LI Yu-Hang;LI Gen-Yue;SONG Xin-Hong;YANG Kai-Li;TAN Xin;WANG En-Peng(Jilin Ginseng Academy,Changchun University of Chinese Medicine,Changchun 130117,China;Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China)

机构地区：[1]长春中医药大学,吉林省人参科学研究院,长春130117 [2]中国科学院长春光学精密机械与物理研究所,长春130033

出　　处：《应用化学》2025年第1期69-77,共9页Chinese Journal of Applied Chemistry

基　　金：国家自然科学基金(No.82073969);长春市科技局项目(No.21ZGY10);吉林省科技发展计划项目(Nos.20210401108YY,20220401104YY)资助。

摘　　要：建立一种基于高光谱技术结合机器学习快速、无损鉴别市售林下生晒参参龄的方法。以常见市售林下生晒参为研究对象,采集不同年限的林下生晒参高光谱图像并基于阈值分割方法从感兴趣区域提取光谱数据,然后对数据进行多元散射矫正(MSC)-Savitzky-Golay smoothing(S-G smoothing)-一阶导数(FD)和MSC-S-G smoothing-二阶导数(SD)预处理消除干扰,将预处理后的数据进行3种机器学习,包括支持向量机回归(SVR)、主成分回归(PCR)和偏最小二乘回归(PLSR)。高光谱数据存在冗余性,采用竞争性自适应重加权采样法(CARS)、连续投影算法(SPA)和无信息变量消除算法(UVE)3种算法筛选波段去除无用的波长信息。CARS-MSC-S-G smoothing-SD-SVR有较低的均方根误差RMSEC(0.0027),RMSEP(0.0120),较高的决定系数R_(C)^(2)(0.9998),R_(P)^(2)(0.9993),以及相对分析误差RPD(38),竞争性自适应重加权采样法(CARS)结合SVR模型效果最好,能够实现对林下生晒参年限的准确分类。基于高光谱成像技术结合机器学习能够实现对林下生晒参参龄的快速、无损鉴别。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(R_(C)^(2):0.9998,R_(P)^(2):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 

分 类 号：O657.3[理学—分析化学] TP181[理学—化学] R282.5[自动化与计算机技术—控制理论与控制工程]