Origin identification of Gardeniae Fructus based on hyperspectral imaging technology.
10.19540/j.cnki.cjcmm.20220809.103
- Author:
Cong ZHOU
1
;
Hui WANG
2
;
Jian YANG
2
;
Xiao-Bo ZHANG
3
Author Information
1. State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700, China Academician Workstation of Jiangxi University of Traditional Chinese Medicine Nanchang 330004, China.
2. State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700, China.
3. State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700, China Research Center for Quality Evaluation of Dao-di Herbs Ganjiang New District 330000, China.
- Publication Type:Journal Article
- Keywords:
Gardeniae Fructus;
hyperspectral imaging;
pattern recognition;
place of origin
- MeSH:
Gardenia;
Hyperspectral Imaging;
Fruit;
Least-Squares Analysis;
Technology
- From:
China Journal of Chinese Materia Medica
2022;47(22):6027-6033
- CountryChina
- Language:Chinese
-
Abstract:
In order to realize rapid and non-destructive identification of the origin of Gardeniae Fructus, a technical method based on hyperspectral imaging technology was established in this study. Spectral information of Gardeniae Fructus samples from eight production origins was acquired from visible NIR(410-990 nm, VNIR) and short wavelength NIR(950-2 500 nm, SWIR) bands based on hyperspectral imaging techniques. The average spectral reflectance within the region of interest was extracted and calculated using the ENVI 5.3 software, resulting in 1 600 sample data. The visible short wavelength infrared band(fused bands) spectral data covering the range 410-2 500 nm were obtained after combining the spectral data of VNIR and SWIR. Data were de-noised by five common preprocessing methods, including multivariate scatter correction, Savitzky-Golay smoothing, standard normal variate, first derivative(FD), and second derivative from VNIR, SWIR, and fused bands(VNIR+SWIR). Partial least squares discriminant analysis, linear support vector classification(LinearSVC), and random forest were used to establish the model for origin identification of Gardeniae Fructus. The results indicated that the identification model of Gardeniae Fructus origin established after FD pretreatment of the spectral data in the fused bands could yield good results. According to the confusion matrix evaluation results, the model prediction set using LinearSVC reached 100% accuracy, so the optimum identification model of Gardeniae Fructus origin was determined as fusion bands-FD-LinearSVC. Therefore, the hyperspectral imaging technology can achieve rapid, nondestructive, and accurate identification of Gardeniae Fructus samples of different origins, which provides a technical reference for the differential detection of Gardeniae Fructus and other Chinese medicines.
