A Rapid, Hyperspectral-based Method for Determining Sporoderm-broken Rate of Ganoderma Lucidum Spore Powder
10.13748/j.cnki.issn1007-7693.20231852
- VernacularTitle:基于高光谱技术的灵芝孢子粉破壁率快速检测方法研究
- Author:
Zaichen PAN
1
;
Yi ZHONG
2
;
Ling FANG
2
;
Zhechen QI
1
;
Jing XU
3
;
Zongsuo LIANG
1
;
Zhenhao LI
2
Author Information
1. Zhejiang Provincial Key Laboratory of Plant Secondary Metabolism Regulation, College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China
2. Zhejiang Shouxiangu Institute of Plant Medicine, Hangzhou 310012, China
3. Zhejiang Engineering Research Center of Rare Medicinal Plants, Wuyi 321200, China
- Publication Type:Journal Article
- Keywords:
hyperspectral imaging ； Ganoderma lucidum spore powder ；sporoderm-broken rate；chemometrics ； quantitative calibration model
- From:
Chinese Journal of Modern Applied Pharmacy
2024;41(6):760-766
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE :To establish a rapid nondestructive detection method for the sporoderm-broken rate of Ganoderma lucidum spore powder by hyperspectral technology combined with chemometrics.
METHODS
Hyperspectral images of Ganoderma lucidum spore powder samples with different sporoderm-broken rates were collected, and spectral data in the visible-shortwave near-infrared band(397−1 004 nm) range of each sample were calculated after selecting the region of interest. Compared 6 spectral preprocessing methods[standard normal variable transformation, multivariate scattering correction, Savitsky-Golay(SG) smoothing, wavelet transform, SG smoothing+standard normal variable transformation, and SG smoothing+multivariate scattering correction], 5 characteristic band extraction methods(competitive adaptive reweighting, successive projections algorithm, uninformative variables elimination, least angle regression, and genetic algorithm), and 5 algorithms(partial least squares regression, support vector regression, extreme learning machine, multilayer perceptron, and LightGBM) for constructing quantitative correction models to predicts performance.
RESULTS
The optimal combination was SG smoothing+competitive adaptive reweighted feature band selection+partial least squares. The quantitative correction model established based on the algorithm combination achieved a prediction set coefficient of 0.868 2, and a root mean square error of 0.011 7 for Ganoderma lucidum spore powder samples with a sporoderm-broken rate range of 90%−100%. The selected optimal algorithm combination was applied to construct a quantitative correction model with a sporoderm-broken rate range of 0−100%, the coefficient of determination for the test set was 0.973 1 and the root mean square error was 0.049 3, showing good generalization ability.
CONCLUSION
The established quantitative detection model can realize the rapid and non-destructive detection of the sporoderm-broken rate of Ganoderma lucidum spore powder, which provides technical support for the quality control of Ganoderma lucidum spore powder and its products.
