Development of online identification system for turquoise based on hyperspectral imaging technology

Wu Jinquan; Wang Jian; Xiong Wei; Huang Kunpeng; Lin Zhaoxiang; Feng Xin

doi:10.12086/oee.2021.210075

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Wu J Q, Wang J, Xiong W, et al. Development of online identification system for turquoise based on hyperspectral imaging technology[J]. Opto-Electron Eng, 2021, 48(7): 210075. doi: 10.12086/oee.2021.210075

Citation:

Wu J Q, Wang J, Xiong W, et al. Development of online identification system for turquoise based on hyperspectral imaging technology[J]. Opto-Electron Eng, 2021, 48(7): 210075. doi: 10.12086/oee.2021.210075

Development of online identification system for turquoise based on hyperspectral imaging technology

1.
Central South University for Nationalities, Wuhan, Hubei 430074, China
2.
China Tibetology Research Center, Beijing 100101, China

Fund Project: National Major Research and Development Project: Major New Drug Creation and Development(2014zx09301308)

Received Date 17 March 2021

Revised Date 28 June 2021

Published Date 15 July 2021

Abstract

Abstract

To prevent people from using the processed turquoise and counterfeit turquoise in medicine, this paper focuses on identifying the raw materials of medicinal turquoise. A turquoise identification system was developed using hyper-spectral imaging technology. The sample standard spectral line was obtained while the applicability was analyzed by the present sample, based on the high-resolution spectral data of ore samples from 6 representative producing areas of natural turquoise in China. A new method was summarized by the differences in correlation coefficients in the range of 400 nm~1000 nm and 400 nm~600 nm of the fake turquoise on the market and an experimental prototype system to identify the true or false of turquoise was developed. Further research will provide technical support to select raw materials in mineral medicine, which will greatly promote the modernization of Tibetan medicine.
- hyperspectral imaging technology /
- turquoise /
- near-infrared spectroscopy /
- dual-scale /
- correlation coefficient

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References

[1]	戴正之. 浅谈绿松石的鉴别与保养[J]. 质量与标准化, 2019(7): 30-33. doi: 10.3969/j.issn.2095-0918.2019.07.012 CrossRef Google Scholar Dai Z Z. Discussion on the identification and maintenance of turquoise[J]. Qual Stand, 2019(7): 30-33. doi: 10.3969/j.issn.2095-0918.2019.07.012 CrossRef Google Scholar
[2]	杭东. 浅谈绿松石文化[J]. 超硬材料工程, 2013, 25(6): 57-58. doi: 10.3969/j.issn.1673-1433.2013.06.020 CrossRef Google Scholar Hang D. Discussion on the culture of turquoise[J]. Superh Mater Eng, 2013, 25(6): 57-58. doi: 10.3969/j.issn.1673-1433.2013.06.020 CrossRef Google Scholar
[3]	才科. 二十五味松石丸治疗肝胆疾病[J]. 中国民间疗法, 2008, 16(9): 38. doi: 10.3969/j.issn.1007-5798.2008.09.038 CrossRef Google Scholar Cai K. The treatment of liver and gall diseases with 25 kinds of songshi pills[J]. China's Naturop, 2008, 16(9): 38. doi: 10.3969/j.issn.1007-5798.2008.09.038 CrossRef Google Scholar
[4]	曲雁. "微沁"绿松石的检验[J]. 宝石和宝石学杂志, 2018, 20(5): 44-50. Google Scholar Qu Y. Identification of "insignificant-filled" turquoise[J]. J Gems Gemmol, 2018, 20(5): 44-50. Google Scholar
[5]	陈全莉, 亓利剑, 张琰. 绿松石及其处理品与仿制品的红外吸收光谱表征[J]. 宝石和宝石学杂志, 2006, 8(1): 9-12. doi: 10.3969/j.issn.1008-214X.2006.01.003 CrossRef Google Scholar Chen Q L, Qi L J, Zhang Y. IR Absorption spectrum representation of turquoise, treated turquoise and imitation[J]. J Gems Gemmol, 2006, 8(1): 9-12. doi: 10.3969/j.issn.1008-214X.2006.01.003 CrossRef Google Scholar
[6]	左锐, 戴慧, 蒋小平, 等. 丙烯酸酯类聚合物充填绿松石的红外光谱特征[J]. 安徽地质, 2017, 27(3): 222-224, 236. doi: 10.3969/j.issn.1005-6157.2017.03.016 CrossRef Google Scholar Zuo R, Dai H, Jiang X P, et al. Infrared spectral features of turquoise filled with acrylic polymer[J]. Geol Anhui, 2017, 27(3): 222-224, 236. doi: 10.3969/j.issn.1005-6157.2017.03.016 CrossRef Google Scholar
[7]	徐娅芬, 狄敬如. 湖北天然绿松石与优化处理绿松石的宝石学鉴别特征[J]. 岩石矿物学杂志, 2018, 37(4): 646-654. doi: 10.3969/j.issn.1000-6524.2018.04.010 CrossRef Google Scholar Xu Y F, Di J R. Gemological identification of natural turquoise and treatment turquoise in Hubei[J]. Acta Petrol Mineral, 2018, 37(4): 646-654. doi: 10.3969/j.issn.1000-6524.2018.04.010 CrossRef Google Scholar
[8]	Schwarzinger B, Schwarzinger C. Investigation of turquoise imitations and treatment with analytical pyrolysis and infrared spectroscopy[J]. J Anal Appl Pyroly, 2017, 125: 24-31. doi: 10.1016/j.jaap.2017.05.002 CrossRef Google Scholar
[9]	闫敬文, 陈宏达, 刘蕾. 高光谱图像分类的研究进展[J]. 光学精密工程, 2019, 27(3): 680-693. Google Scholar Yan J W, Chen H D, Liu L. Overview of hyperspectral image classification[J]. Opt Precis Eng, 2019, 27(3): 680-693. Google Scholar
[10]	Zhu X Y, Li Y, Zhang Q, et al. Oil film classification using deep learning-based hyperspectral remote sensing technology[J]. ISPRS Int J Geo-Inf, 2019, 8(4): 181. doi: 10.3390/ijgi8040181 CrossRef Google Scholar
[11]	章文龙, 曾从盛, 高灯州, 等. 闽江河口湿地土壤全磷高光谱遥感估算[J]. 生态学报, 2015, 35(24): 8085-8093. doi: 10.5846/stxb201406241303 CrossRef Google Scholar Zhang W L, Zeng C S, Gao D Z, et al. Estimating the soil total phosphorus content based on hyper-spectral remote sensing data in the Min River estuarine wetland[J]. Acta Ecol Sin, 2015, 35(24): 8085-8093. doi: 10.5846/stxb201406241303 CrossRef Google Scholar
[12]	Ren Z L, Sun L, Zhai Q P. Improved k-means and spectral matching for hyperspectral mineral mapping[J]. Int J Appl Earth Obs Geoinformation, 2020, 91: 102154. doi: 10.1016/j.jag.2020.102154 CrossRef Google Scholar
[13]	郭文川, 董金磊. 高光谱成像结合人工神经网络无损检测桃的硬度[J]. 光学精密工程, 2015, 23(6): 1530-1537. doi: 10.3788/OPE.20152306.1530 CrossRef Google Scholar Guo W C, Dong J L. Nondestructive detection on firmness of peaches based on hyperspectral imaging and artificial neural networks[J]. Opt Precis Eng, 2015, 23(6): 1530-1537. doi: 10.3788/OPE.20152306.1530 CrossRef Google Scholar
[14]	张成业, 秦其明, 陈理, 等. 高光谱遥感岩矿识别的研究进展[J]. 光学精密工程, 2015, 23(8): 2407-2418. Google Scholar Zhang C Y, Qin Q M, Chen L, et al. Research and development of mineral identification utilizing hyperspectral remote sensing[J]. Opt Precis Eng, 2015, 23(8): 2407-2418. Google Scholar
[15]	贺佳, 刘冰锋, 李军. 不同生育时期冬小麦叶面积指数高光谱遥感监测模型[J]. 农业工程学报, 2014, 30(24): 141-150. doi: 10.3969/j.issn.1002-6819.2014.24.017 CrossRef Google Scholar He J, Liu B F, Li J. Monitoring model of leaf area index of winter wheat based on hyperspectral reflectance at different growth stages[J]. Trans Chin Soc Agric Eng, 2014, 30(24): 141-150. doi: 10.3969/j.issn.1002-6819.2014.24.017 CrossRef Google Scholar
[16]	张雷蕾, 李永玉, 彭彦昆, 等. 基于高光谱成像技术的猪肉新鲜度评价[J]. 农业工程学报, 2012, 28(7): 254-259. doi: 10.3969/j.issn.1002-6819.2012.07.042 CrossRef Google Scholar Zhang L L, Li Y Y, Peng Y K, et al. Determination of pork freshness attributes by hyperspectral imaging technique[J]. Trans Chin Soc Agric Eng, 2012, 28(7): 254-259. doi: 10.3969/j.issn.1002-6819.2012.07.042 CrossRef Google Scholar
[17]	单佳佳, 彭彦昆, 王伟, 等. 基于高光谱成像技术的苹果内外品质同时检测[J]. 农业机械学报, 2011, 42(3): 140-144. doi: 10.3969/j.issn.1000-1298.2011.03.027 CrossRef Google Scholar Shan J J, Peng Y K, Wang W, et al. Simultaneous detection of external and internal quality parameters of apples using hyperspectral technology[J]. Trans Chin Soc Agric Mach, 2011, 42(3): 140-144. doi: 10.3969/j.issn.1000-1298.2011.03.027 CrossRef Google Scholar
[18]	樊嵘, 孟大志, 徐大舜. 统计相关性分析方法研究进展[J]. 数学建模及其应用, 2014, 3(1): 1-12. doi: 10.3969/j.issn.2095-3070.2014.01.002 CrossRef Google Scholar Fan R, Meng D Z, Xu D S. Survey of research process on statistical correlation analysis[J]. Math Model Appl, 2014, 3(1): 1-12. doi: 10.3969/j.issn.2095-3070.2014.01.002 CrossRef Google Scholar
[19]	闻兵工, 冯伍法, 刘伟, 等. 基于光谱曲线整体相似性测度的匹配分类[J]. 测绘科学技术学报, 2009, 26(2): 128-131. doi: 10.3969/j.issn.1673-6338.2009.02.014 CrossRef Google Scholar Wen B G, Feng W F, Liu W, et al. Matching and classification based on the whole comparability measure of spectral curve[J]. J Geomat Sci Technol, 2009, 26(2): 128-131. doi: 10.3969/j.issn.1673-6338.2009.02.014 CrossRef Google Scholar
[20]	杨甲森. 卫星遥测数据相关性知识发现方法研究[D]. 北京: 中国科学院大学(中国科学院国家空间科学中心), 2019. Google Scholar Yang J S. Research on correlation knowledge discovery method of spacecraft telemetry data[D]. Beijing: University of Chinese Academy of Sciences (National Space Science Center, the Chinese Academy of Sciences), 2019. Google Scholar
[21]	赵虹霞, 伏修锋, 干福熹, 等. 不同产地绿松石无损检测及岩相结构特征研究[J]. 岩矿测试, 2007, 26(2): 141-144. doi: 10.3969/j.issn.0254-5357.2007.02.015 CrossRef Google Scholar Zhao H X, Fu X F, Gan F X, et al. Study on mineralogical characteristics of turquoise samples from different provenances by non-destructive analysis[J]. Rock Mineral Anal, 2007, 26(2): 141-144. doi: 10.3969/j.issn.0254-5357.2007.02.015 CrossRef Google Scholar
[22]	王戬. 基于ICP-MS和XRD的绿松石原矿真伪检测[D]. 武汉: 中南民族大学, 2019. Google Scholar Wang J. Detection of the authenticity of turquoise raw ore based on ICP-MS and XRD[D]. Wuhan: South-Central Minzu University, 2019. Google Scholar
[23]	方圣辉, 龚浩. 动态调整权重的光谱匹配测度法分类的研究[J]. 武汉大学学报·信息科学版, 2006, 31(12): 1044-1046. doi: 10.3321/j.issn:1671-8860.2006.12.003 CrossRef Google Scholar Fang S H, Gong H. Spectral simil arity scale based on dynamic wighting adjustment method[J]. Geomat Inf Sci Wuhan Univ, 2006, 31(12): 1044-1046. doi: 10.3321/j.issn:1671-8860.2006.12.003 CrossRef Google Scholar

Overview

Overview

Overview: Turquoise is a kind of copper-aluminum-phosphate minerals with abundant mineral reserves in China and a classic mineral medicinal material in Tibetan areas, which has excellent effects on treating wind-cold, lowering blood pressure, regulating the respiratory system and curing liver diseases. This paper focuses on identifying the raw materials of medicinal turquoise to prevent people from using the processed turquoise and counterfeit turquoise in medicine. The experimental prototype system, which can quickly and accurately pick up the true turquoise raw ore from the large amounts of fake turquoise on the market, was developed using hyper-spectral imaging technology. With the Pearson correlation between the data observed and the standard spectral line, the system was mainly composed of a control system, an optical imaging acquisition system (including the hyperspectral camera, a light source, and a darkroom), an analysis and identification system (the professional detection and analysis software) and a sorting system (the mechanical picking arm). The sample standard spectral line was obtained while the applicability was analyzed by the present sample, based on the high-resolution spectral data of ore samples from 6 representative producing areas of natural turquoise in China. A new method was summarized by the differences in correlation coefficients in the range of 400 nm~1000 nm and 400 nm~600 nm of the fake turquoise on the market. The system is going to be used to select raw materials in mineral medicine in some Tibetan medicine companies in the near future. These works will provide technical support for other research on mineral-identification, or Jewelry-identification. Further research will greatly promote the modernization of Tibetan medicine.