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| Identification of Nephrite and Imitations Based on Terahertz Time-Domain Spectroscopy and Pattern Recognition |
| LIN Hong-mei1, CAO Qiu-hong1, ZHANG Tong-jun1, LI Zhao-xin1, HUANG Hai-qing1, LI Xue-min1, WU Bin2, ZHANG Qing-jian3, LÜ Xin-min4, LI De-hua1* |
1. Qingdao Key Laboratory of Terahertz Technology，College of Electronic and Information Engineering，Shandong University of Science and Technology，Qingdao 266590, China
2. The 41st Research Institute of CETC，Qingdao 266555, China
3. Technology Center of Qingdao Customs，Qingdao 266002，China
4. Technology Center of Alashankou Customs，Alashankou 833400，China |
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Abstract Jade is a rare mineral that people have favored. The identification of jade authenticity has always been a thorny problem in the jewelry identification industry. Traditional identification methods are difficult to identify the nephrite and their imitations.Terahertz standoff detection technology can realize quick non-destructive testing and has a variety of applications in the classification and identification of mixtures. In this paper, Terahertz Time-domain Spectroscopy (TDS) and pattern recognition are applied to identify nephrite and imitations. The terahertz spectrum of several nephrite jade samples from Afghanistan, China’s Qinghai, Pakistan and China’s Xinjiang and imitations, like glass, marble, and raw gemstone is measured with TDS in the frequency range 0.1~1.5 THz. Due to the complexity and diversity of the sample’s chemical composition, the nephrite jade and the imitation cannot be distinguished correctly withtheir characteristic spectrum. In order to distinguish Jade with their imitations, a classification model is established.Principal Component Analysis (PCA) performs dimension reduction and feature extraction on the refractive index. The scores of the first and second principal components of the sample were obtained. It can be found that nephrite and imitations can be clearly distinguished from each other. Based on the extracted data,third quarters of them are randomly selected as the training set, the rest as the test set, a Support Vector Machine (SVM) model is established, and the parameters of the Support Vector Machine is optimized by GridSearch, genetic algorithm (GA) and particle swarm algorithm (PSO). The optimal parameters of SVM based on grid search are c=2.828 4 and g=2 while that based on GA are c=1.740 1, g=4.544 6 and based on PSO c=11.287 2, g=1.833 1. The recognition rates of the three optimization algorithms are 97.7%, 98.3% and 98.6%, and the running time is 1.39, 3.6, 6.13 s respectively. Although the optimal parameters obtained by the three optimization algorithms are different from each other, all of them can achieve a correct classification. The results show that the Terahertz spectrum combined with the pattern recognition method is a promising technique for identifying nephrite with their imitations.
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Received: 2020-10-26
Accepted: 2021-03-29
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Corresponding Authors:
LI De-hua
E-mail: jcbwl@sdust.edu.cn
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