Identification of Age of Forest Sun-Dried Ginseng Based on Hyperspectral Imaging Technology

doi:10.19894/j.issn.1000-0518.240204

Chinese Journal of Applied Chemistry ›› 2025, Vol. 42 ›› Issue (1): 69-77.DOI: 10.19894/j.issn.1000-0518.240204

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Identification of Age of Forest Sun-Dried Ginseng Based on Hyperspectral Imaging Technology

Lu-Sheng HAN¹, Yu-Dan WANG¹, Yu-Hang LI², Gen-Yue LI¹, Xin-Hong SONG¹, Kai-Li YANG¹, Xin TAN²(), En-Peng WANG¹()

^1.Jilin Ginseng Academy，Changchun University of Chinese Medicine，Changchun 130117，China
^2.Changchun Institute of Optics，Fine Mechanics and Physics，Chinese Academy of Sciences，Changchun 130033，China

Received:2024-07-03 Accepted:2024-12-20 Published:2025-01-01 Online:2025-01-24
Contact: Xin TAN,En-Peng WANG
About author:wangep@ccucm.edu.cn；
xintan_grating@163.com
Supported by:
the National Natural Science Foundation of China(82073969);the Science and Technology Bureau Project of Changchun(21ZGY10);the Science and Technology Development Program Project of Jilin Province(20210401108YY)

Abstract

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 （ $R C 2$ ： 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.

Key words: Forest sun-dried ginseng, Different ages, Hyperspectralimaging, Identification

CLC Number:

O657.3

Lu-Sheng HAN, Yu-Dan WANG, Yu-Hang LI, Gen-Yue LI, Xin-Hong SONG, Kai-Li YANG, Xin TAN, En-Peng WANG. Identification of Age of Forest Sun-Dried Ginseng Based on Hyperspectral Imaging Technology[J]. Chinese Journal of Applied Chemistry, 2025, 42(1): 69-77.

Figures/Tables 8

Fig.1 Schematic structure of the visible-near-infrared hyperspectral imaging identification system

Fig.2 Samples of forest sun-dried ginseng

Fig.3 Mean spectral profiles of visible-near-infrared hyperspectral acquisition of forest sun-dried ginseng of different ages

Fig.4 Pre-processing results of visible-near-infrared hyperspectral data of forest sun-dried ginseng of different growth ages

Table 1 Preprocessing results of hyperspectral data based on PCR modeling

Models	Training set		Cross-Validation		Prediction set
Models	RMSEC	$R C 2$	RMSECV	$R C V 2$	RMSEP	$R P 2$	RPD
Original data-PCR	0.269 6	0.882 0	0.279 8	0.873 1	0.332 8	0.820 2	2
MSC-S-G smoothing-FD-PCR	0.261 9	0.888 6	0.270 0	0.888 6	0.262 5	0.884 0	3
MSC-S-G smoothing-SD-PCR	0.240 3	0.906 2	0.247 7	0.909 8	0.238 6	0.913 8	3

Table 1 Preprocessing results of hyperspectral data based on PCR modeling

Models	Training set		Cross-Validation		Prediction set
Models	RMSEC	$R C 2$	RMSECV	$R C V 2$	RMSEP	$R P 2$	RPD
Original data-PCR	0.269 6	0.882 0	0.279 8	0.873 1	0.332 8	0.820 2	2
MSC-S-G smoothing-FD-PCR	0.261 9	0.888 6	0.270 0	0.888 6	0.262 5	0.884 0	3
MSC-S-G smoothing-SD-PCR	0.240 3	0.906 2	0.247 7	0.909 8	0.238 6	0.913 8	3

Table 2 Modeling results based on MSC-S-G smoothing-SD

Models	Training set		Cross-Validation		Prediction set
Models	RMSEC	$R C 2$	RMSECV	$R C V 2$	RMSEP	$R P 2$	RPD
MSC-S-G smoothing-SD-PCR	0.240 3	0.906 2	0.247 7	0.909 8	0.238 6	0.913 8	3
MSC-S-G smoothing-SD-PLSR	0.215 3	0.924 8	0.235 2	0.911 5	0.215 4	0.924 5	3
MSC-S-G smoothing-SD-SVR	0.045 5	0.996 7	0.075 7	0.990 8	0.045 6	0.996 3	16

Table 2 Modeling results based on MSC-S-G smoothing-SD

Models	Training set		Cross-Validation		Prediction set
Models	RMSEC	$R C 2$	RMSECV	$R C V 2$	RMSEP	$R P 2$	RPD
MSC-S-G smoothing-SD-PCR	0.240 3	0.906 2	0.247 7	0.909 8	0.238 6	0.913 8	3
MSC-S-G smoothing-SD-PLSR	0.215 3	0.924 8	0.235 2	0.911 5	0.215 4	0.924 5	3
MSC-S-G smoothing-SD-SVR	0.045 5	0.996 7	0.075 7	0.990 8	0.045 6	0.996 3	16

Fig.5 Feature band algorithm screening process

Table 3 Model results based on feature extraction algorithm

Models	Training set		Cross-Validation		Prediction set
Models	RMSEC	$R C 2$	RMSECV	$R C V 2$	RMSEP	$R P 2$	RPD
CARS-MSC-S-G smoothing-SD-SVR	0.002 7	0.999 8	0.049 6	0.996 2	0.012 0	0.999 3	38
SPA-MSC-S-G smoothing-SD-SVR	0.018 1	0.999 4	0.052 5	0.995 7	0.020 1	0.998 9	30
UVE-MSC-S-G smoothing-SD-SVR	0.040 2	0.997 5	0.176 5	0.950 6	0.041 6	0.997 3	19

Table 3 Model results based on feature extraction algorithm

Models	Training set		Cross-Validation		Prediction set
Models	RMSEC	$R C 2$	RMSECV	$R C V 2$	RMSEP	$R P 2$	RPD
CARS-MSC-S-G smoothing-SD-SVR	0.002 7	0.999 8	0.049 6	0.996 2	0.012 0	0.999 3	38
SPA-MSC-S-G smoothing-SD-SVR	0.018 1	0.999 4	0.052 5	0.995 7	0.020 1	0.998 9	30
UVE-MSC-S-G smoothing-SD-SVR	0.040 2	0.997 5	0.176 5	0.950 6	0.041 6	0.997 3	19

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Identification of Age of Forest Sun-Dried Ginseng Based on Hyperspectral Imaging Technology

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