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| Key Feature Analysis in Identification and Authenticity of Ziziphi Spinosae Semen by Using Hyperspectral Images Based on 1DCNN and PLSDA |
| ZHAO Xin1, 4, SHI Yu-na1, LIU Yi-tong1, JIANG Hong-zhe2, CHU Xuan3, ZHAO Zhi-lei1, 4, WANG Bao-jun1, 4*, CHEN Han1 |
1. School of Quality and Technical Supervision, Hebei University, Baoding 071002, China
2. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
3. College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
4. National & Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China
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Abstract Ziziphi spinosae semen is an important raw material of health care products and traditional Chinese medicine preparations because it nourishes the heart and the liver, making it ideal for calming the nerves and helping sleep. At present, the adulteration of ziziphi spinosadsemen in the market is serious, which greatly damages the interests of consumers and disrupts the market order. Traditional manual detection or laboratory-based high-performance liquid chromatography methods have problems of low efficiency and difficult promotion. In this study, a hyperspectral imaging method for ziziphi spinosadsemen authenticity identification was proposed based on convolutional neural network and partial least squares discrimination, and the key spectral features in the two types of models were discussed and studied. The study will reference the subsequent development of multispectral systems and portable instruments. The average spectra of all single kernels in the hyperspectral images (400~1 000 nm) of ziziphi spinosae semen and its common counterfeits (Ziziphus mauritiana lam, Hovenia dulcis Thunb. and Lens culinaris) were extracted. The partialleast squares discriminant analysis (PLSDA) model and the one-dimensional convolutional neural network (1DCNN) model were respectively established based on the average spectra. The competitive adaptive reweighting algorithm (CARS) selects characteristic wavelengths before PLSDA modeling. A custom wavelength selection layer was added to the 1DCNN model. T-distributed stochastic neighborhood embedding (t-SNE) was applied to the outputs of convolutional and fully connected layers for visual analysis. To effectively compare with the CARS-PLSDA model, a 5W-1DCNN model based on five wavelengths was constructed. The results showed that both the CARS-PLSDA and1DCNN models could achieve precision prediction results, and the classification accuracies of both the calibration set and the prediction set are above 0.99. Comparing the feature wavelengths selected by CARS and custom layers, wavelengths near 670, 721, and 850 nm play important roles in both models. The research results provided a reference for multispectral systems and portable equipment for rapid detection of the authenticity of ziziphi spinosad semen.
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Received: 2023-09-05
Accepted: 2024-07-14
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Corresponding Authors:
WANG Bao-jun
E-mail: wbj498@163.com
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