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| Research on Rapid Detection With Machine Learning-Based LIBS for
Occupational Chronic Lead Poisoning |
| ZHANG Rui1, KANG Li-zhu2, HUANG Zhi-jie2, YAN Wen-hao2, LIN Zhan-jian2, CHEN Ji2, LU Bing2, XUE Zhi-dong1*, LI Xiang-you2* |
1. Second Clinical Medical College, Binzhou Medical College, Yantai 264000, China
2. Huazhong University of Science and Technology, Wuhan National Research Center for Optoelectronics, Wuhan 430074, China
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Abstract Occupational chronic lead poisoning is a gradually developing disease.It is easy to accumulate lead and develop into severe lead poisoning due to atypical early symptoms, which seriously threaten the health and quality of life of occupational groups. Mainstream analytical techniques have problems, such as requiring high precision but complicated and time-consuming operations, or being easy to operate but having poor applicability, which cannot achieve rapid in-situ detection. Laser-induced breakdown spectroscopy (LIBS), a new detection method, has demonstrated great potential and promising applications in the field of elemental analysis. In this paper, the feasibility of rapidly diagnosing occupational chronic lead poisoning is demonstrated using LIBS technology combined with machine learning algorithms. The whole blood sample preparation method has been optimized, and it is proposed that ultrasonic treatment can make the whole blood matrix more uniform, thereby alleviating the sample fragmentation problem when the laser is applied to dry blood. A glass slide is the most suitable substrate type compared to filter paper, graphite, and boric acid substrates. The effects of various LIBS experimental parameters on the signal intensity and signal-to-background ratio of the characteristic spectral lines of the element lead (Pb) were investigated. Simulated blood LIBS data for different types of occupational chronic lead poisoning were collected, and data dimensionality reduction was achieved by extracting features using principal component analysis (PCA). A 10-fold cross-validation and support vector machine (SVM) and back-propagation neural network (BPNN) were used to construct a diagnostic model for the chronic lead poisoning category. The recognition accuracies of both models reached more than 90%, which solved the problem of weak spectral difference of Pb elements in low concentration, which was difficult to be distinguished by traditional methods, and the BPNN model showed excellent diagnostic effect, with the classification accuracy and precision rate of 95.56% and 96.08%, respectively. The results demonstrate that machine learning-based LIBS technology can facilitate the timely detection of lead elemental excess in whole blood, providing an auxiliary method for the rapid and accurate screening of blood lead abnormalities, and supplementing the clinical basis for the diagnosis of occupational chronic lead poisoning.
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Received: 2024-09-11
Accepted: 2024-12-23
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Corresponding Authors:
XUE Zhi-dong, LI Xiang-you
E-mail: zdxue@isyslab.org; xyli@mail.hust.edu.cn
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[1] DOU Jian-rui(窦建瑞). Labour Protection(劳动保护), 2020, (8): 74.
[2] Brief Guide to Analytical Methods for Measuring Lead in Blood, Second Edition(血铅水平测定分析方法简要指南，第2版). World Health Organization (WHO)(世界卫生组织), 2020.
[3] National Health and Family Planning Commission of the People's Republic of China(中华人民共和国国家卫生和计划生育委员会). GBZ 37—2015 Diagnosis of Occupational Chronic Lead Poisoning(国家标准GBZ 37—2015职业性慢性铅中毒的诊断), 2015.
[4] Du Xuqin, Zheng Wei, Ye Qiao, et al. Toxicology and Industrial Health, 2020, 36(12): 951.
[5] Fayyaz A, Asghar H, Alshehri A M, et al. Heliyon, 2023, 9(3): e13957.
[6] Goncharova Iryna, Guichaoua Dominique, Taboukhat Said, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2024, 217: 106943.
[7] Jeon Gookseon, Keum Hohyun, Lee Hyunkeun, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2024, 212: 106857.
[8] Zhao Zhifang, Ma Wenping, Teng Geer, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2023, 202: 106644.
[9] Zhao Zhifang, Xu Xiangjun, Bao Mengyu, et al. Microchemical Journal, 2024, 203: 110955.
[10] Feng Zhongqi, Li Shuaishuai, Gu Tianyu, et al. Molecules, 2022, 27(19): 6438.
[11] Al-Jeffery M O, Telle H. Proceedings of SPIE, 2002, 4613: 152.
[12] De Giacomo A, Koral C, Valenza G, et al. Analytical Chemistry, 2016, 88(10): 5251.
[13] Zhang D X, Zhang H, Zhao Y, et al. Applied Spectroscopy Reviews, 2022, 57(2): 89.
[14] Wang Z, Afgan M S, Gu W L, et al. TrAC-Trends in Analytical Chemistry, 2021, 143: 116385.
[15] Hao Z Q, Liu K, Lian Q L, et al. Frontiers of Physics, 2024, 19(6): 62501.
[16] Chen X, Zhang Y, Li X H, et al. Biomedical Optics Express, 2021, 12(6): 3584.
[17] Berlo K, Xia W, Zwillich F, et al. Scientific Reports, 2022, 12(1): 1614.
[18] REN Wen-xin, YANG Liang, ZHAO Han, et al(任文心，杨 亮，赵 韩，等). Chinese Journal of Analytical Chemistry(分析化学), 2024, 52(4): 559.
[19] YANG Wen-run, TIAN Sheng-hui, KE Jun(杨文润，田胜慧，柯 军). China Medical Device Information(中国医疗器械信息), 2016, 22(7): 41.
[20] Ma S X, Tang Y, Ma Y Y, et al. Journal of Analytical Atomic Spectrometry, 2020, 35(5): 967.
[21] Lian Q L, Li X Y, Lu B, et al. IEEE Access, 2023, 11: 141915.
[22] Rehan I, Khan S, Gondal M A, et al. Arabian Journal for Science and Engineering, 2024, 49(1): 1257.
[23] ZHOU Zhi-hua(周志华). Airport Journal(航空港), 2018, (2): 94.
[24] Yang L, Xiang Y, Li Y C, et al. AIP Advances, 2023, 13(7): 075024.
[25] SONG Shao-zhong, FU Shao-yan, LIU Yuan-yuan, et al(宋少忠，符少燕，刘园园，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2024, 44(6): 1553.
[26] Choi J, Kim W, Kim H Y. Soft Matter, 2020, 16(24): 5571.
[27] XUE Chang-guo, TANG Yu, LI Shi-qin, et al(薛长国，唐 毓，李世琴，等). Chinese Journal of Analytical Chemistry(分析化学), 2021, 49(1): 151.
[28] WU Zi-jun, LU Jing-qi, HUANG Jian, et al(吴子俊，卢景琦，黄 剑，等). Laser & Optoelectronics Progress(激光与光电子学进展), 2019, 56(14): 264.
[29] WANG Xue-mei, YANG Xue-feng, ZHAO Feng, et al(王雪梅，杨雪峰，赵 枫，等). Ecology and Environmental Sciences(生态环境学报), 2023, 32(6): 1007. |
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