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| Identification of Xinhui Citri Reticulatae Pericarpium of Different Aging Years Based on Visible-Near Infrared Hyperspectral Imaging |
| ZHANG Yue1, 3, ZHOU Jun-hui1, WANG Si-man1, WANG You-you1, ZHANG Yun-hao2, ZHAO Shuai2, LIU Shu-yang2*, YANG Jian1* |
1. State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
2. Tianjin Jinhang Institute of Technical Physics, Tianjin 300381, China
3. School of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China
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Abstract Hyperspectral imaging (HSI) is an image data technology based on narrow bands. It combines imaging with spectral technology to obtain continuous and narrow-band image data with high spectral resolution. Hyperspectral imaging technology is widely used in rapidly identifying food, agricultural products, Chinese medicinal materials and other samples because of its rapid and non-destructive characteristics. Xinhui Citri Reticulatae Pericarpium has a high market value, and the market price of the sample is higher because of the longer storage age. At the same time, the accuracy of manual identification of the tangerine peel market is difficult. Based on hyperspectral imaging and chemometric, this study established a rapid and nondestructive identification method for Xinhui Citri Reticulatae Pericarpium of different aging years. Hyperspectral information of 5 aged samples in the vision-near-infrared band (400~1 000 nm) is collected. The average spectral value of the Region of interest (ROI) of the hyperspectral image was extracted as the original sample spectrum. Standard data were obtained after black-and-white correction. After denoising the data by 5 pretreatment methods, including Multiplicative scatter correction (MSC), first Derivative (D1) and Second Derivative (D2), SG smoothing (SG) and Standard Normal Variate Transformation (SNV), Partial least square-discriminant Analysis (PLS-DA), Random Forests (RF), and Support Vector Machine (SVM) and other methods are used to establish the classification model. The accuracy of prediction results is used as the evaluation index to select the best model. A confusion matrix was used to evaluate model classification performance.The results showed that the Multiplicative scatter correction (MSC) combined with the PLS-DA method was the optimal identification model for outer epidermis data, and the identification accuracy of the prediction set reached 97.59%. For inner epidermis data, the raw data combined with the PLS-DA method was used as the optimal identification model, and the identification accuracy of the prediction set reached 97.59%.Using the inner epidermis data and the 19 characteristic wavelength modeling based on the Successive projections algorithm (SPA), the accuracy rate of the whole simulation is still above 90%.The characteristic wavelength modeling extracted by SPA can achieve a similar recognition effect as the full-wavelength model. Removing redundant variables can greatly reduce the complexity of the model and reduce the operation time of the model. Hyperspectral imaging combined with the chemometric method can realize the rapid, nondestructive identification of Xinhui Citri Reticulatae Pericarpium samples of different aging years, providing a theoretical basis for developing exclusive miniaturized equipment.
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Received: 2022-09-30
Accepted: 2022-12-25
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Corresponding Authors:
LIU Shu-yang, YANG Jian
E-mail: yangchem2012@163.com; shuyangliu17@163.com
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