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Evaluation of Sample Preparation Methods for the Classification of Children’s Ca-Fe-Zn Oral Liquid by LIBS
Abstract
Different manufacturers do not produce the same quality of children’s Ca-Fe-Zn oral liquid due to different production materials and processes. To improve the phenomenon of counterfeit and imitation oral liquid on the market and effectively monitor its quality, laser-induced breakdown spectroscopy (LIBS) fingerprinting with sample preparation methods can provide a tool for real-time and rapid detection of oral liquids. The sample preparation methods include filter paper adsorption (FPA), filter paper adsorption with elemental Cu (FPA with Cu), adding dropwise to glass slides (ADS), adding dropwise to glass slides with elemental Cu (ADS with Cu), and gel preparation (GP). This work collected LIBS spectrum of oral liquids from eight manufacturers. The model for eXtreme Gradient Boosting (XGBoost) was constructed for classifying oral liquids based on five sample preparation methods. The accuracy was 91.25, 94.17, 55.42, 91.25, and 91.29%, respectively. The results show that the FPA method is more straightforward, efficient, and less affected by the specificity of the color of the sample. Both ADS and GP are susceptible to the color characteristics of the sample and are not well suited to the direct detection of transparent liquids. This work demonstrated that oral liquids could be discriminated by analyzing LIBS spectrum combined with the XGBoost model. Additionally, sample preparation, like the simple FPA method, can improve the accuracy of LIBS classification.
Keywords
Supporting agencies: This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 32260632), the Science and Technology Research Project of Jiangxi Provincial Education Department (No. GJJ2104), the Natural Science Foundation of Jiangsu province for Distinguished Youth Scholars (No. BK20200190), the Opto-Electronic Information Technology Laboratory (No. KYPT202101Z), the 2023 Jiangsu Science and Technology Think Tank for Youth Talent Plan (No. JSKJZK2023070).
About the Authors
W. Xie
College of Engineering, Jiangxi Agricultural University
China
China
Nanchang
G. Fu
College of Engineering, Jiangxi Agricultural University
China
China
Nanchang
J. Xu
College of Engineering, Jiangxi Agricultural University
China
China
Nanchang
M. Zeng
College of Engineering, Jiangxi Agricultural University
China
China
Nanchang
Q. Wan
College of Engineering, Jiangxi Agricultural University
China
China
Nanchang
X. Yao
College of Vocational Education and Technology, Jiangxi Agricultural University
China
China
Nanchang
P. Yang
Changzhou College of Information Technology
China
China
Changzhou
M. Yao
College of Engineering, Jiangxi Agricultural University
China
China
Nanchang
References
1. Z. Zhang, F. Li, B. A. Hannon, D. S. Hustead, M. M. Aw, Z. Liu, K. A. Chuah, Y. L. Low, D. T. T. Huynh, Nutrients, 13, No. 9, 3036 (2021).
2. C. K. Lutter, L. Grummer-Strawn, L. Rogers, Nutr. Rev., 79, No. 8, 825–846 (2021).
3. P. Dua, R. S. Chanan, Int. J. Health Sci., II, 11688–11696 (2022).
4. G. Cormick, A.P. Betran, I. B. Romero, M. S. Cormick, J. M. Belizan, A. Bardach, A. Ciapponi, Nutrients, 13, No. 2, 316 (2021).
5. K. Li, X. F. Wang, D. Y. Li, Y. C. Chen, L. J. Zhao, X. G. Liu, Y. F. Guo, J. Shen, X. Lin, J. Deng, R. Zhou, H. W. Deng, Clin. Intervent. Aging, 13, 2443–2452 (2018).
6. M. Y. Hsu, E. Mina, A. Roetto, P. E. Porporato, Cells, 9, No. 12, 2591 (2020).
7. L. Pivina, Y. Semenova, M. D. Doşa, M. Dauletyarova, G. Bjørklund, J. Mol. Neurosci., 68, No. 1, 1–10 (2019).
8. C. T. Chasapis, P.-S. A. Ntoupa, C. A. Spiliopoulou, M. E. Stefanidou, Arch. Toxic., 94, No. 5, 1443–1460 (2020).
9. T. Rocha, J. S. Amaral, M. Oliveira, Comprehensive Rev. Food Sci. Food Safety, 15, No. 1, 43–62 (2016).
10. M. C. Nahata, Arch. Disease in Childhood – Fetal and Neonatal Ed., 94, No. 6, 392–393 (2009).
11. R. Thilakaratne, R. Castorina, M. Gillan, D. Han, T. Pattabhiraman, A. Nirula, M. D. Miller, M. Marty, A. Lehmkuhler, A. Mitchell, A. Bradman, J. Exposure Sci. Environ. Epidemiology, 1–7 (2022).
12. K. Wei, X. Cui, G. Teng, M. N. Khan, Q. Wang, Plasma Sci. Technol., 23, No. 8, 85507 (2021).
13. Y. Liu, P. Tan, F. Li, Y. Qiao, Drug Testing Analysis, 5, No. 6, 480–484 (2013).
14. S. H. F. Scafi, C. Pasquini, Analyst, 126, No. 12, 2218–2224 (2001).
15. J. Liang, C. Yan, Y. Zhang, T. Zhang, X. Zheng, H. Li, Chemometr. Intell. Lab. Systems, 197, 103930 (2020).
16. H. Zhang, D. Hua, C. Huang, S. K. Samal, R. Xiong, F. Sauvage, K. Braeckmans, K. Remaut, S. C. De Smedt, Adv. Mater., 32, No. 11, 1905486 (2020).
17. Y. Jiang, D.-W. Sun, H. Pu, Q. Wei, Trends Food Sci. Technol., 75, 10–22 (2018).
18. G. Fu, Z. Li, J. Xu, W. Xie, P. Yang, Y. Xu, M. Yao, Appl. Opt., 61, No. 10, 2536–2541 (2022).
19. V. Lazic, S. Jovićević, Spectrochim. Acta B: At. Spectrosc., 101, 288–311 (2014).
20. O. Gazeli, E. Bellou, D. Stefas, S. Couris, Food Chem., 302, 125329 (2020).
21. K. Wei, Q. Wang, G. Teng, X. Xu, Z. Zhao, G. Chen, Appl. Sci., 12, No. 10, 4981 (2022).
22. P. Yang, G. Fu, J. Wang, Z. Luo, M. Yao, J. Anal. At. Spectrom., 100, 1–3 (2022).
23. S. C. Jantzi, V. Motto-Ros, F. Trichard, Y. Markushin, N. Melikechi, A. De Giacomo, Spectrochim. Acta B: At. Spectrosc., 115, 52–63 (2016).
24. X. Yu, Y. Li, X. Gu, J. Bao, H. Yang, L. Sun, Environ. Monitor. Assess, 186, No. 12, 8969–8980 (2014).
25. H.A. Harun, R. Zainal, J. Nonlinear Opt. Phys. Mater., 27, No. 2, 1850023 (2018).
26. Y. Zhang, T. Zhang, H. Li, Spectrochim. Acta B: At. Spectrosc., 181, 106218 (2021).
27. G. Bilge, B. Sezer, I. H. Boyaci, K. E. Eseller, H. Berberoglu, Spectrochim. Acta B: At. Spectrosc., 145, 115–121 (2018).
28. S. Moncayo, J. D. Rosales, R. Izquierdo-Hornillos, J. Anzano, J. O. Caceres, Talanta, 158, 185–191 (2016).
29. S.-U. Choi, S.-C. Han, J.-I. Yun, Spectrochim. Acta B: At. Spectrosc., 162, 105716 (2019).
30. Y. He, X. Wang, S. Guo, A. Li, X. Xu, N. Wazir, C. Ding, T. Lu, L. Xie, M. Zhang, Y. Hao, W. Guo, R. Liu, Appl. Opt., 58, No. 2, 422–427 (2019).
31. A. Metzinger, E. Kovacs-Szeles, I. Almasi, G. Galbacs, Appl. Spectrosc., 68, No. 7, 789–793 (2014).
32. K. Keerthi, S. D. George, S. D. Kulkarni, S. Chidangil, V. K. Unnikrishnan, Opt. Laser Technol., 147, 107622 (2022).
33. P. Yang, Y. Zhu, X. Yang, J. Li, S. Tang, Z. Hao, L. Guo, X. Li, X. Zeng, Y. Lu, J. Cereal Sci., 80, 111–118 (2018).
34. M. Perez-Rodriguez, P. M. Dirchwolf, T. V. Silva, R. N. Villafane, J. A. G. Neto, R. G. Pellerano, E. C. Ferreira, Food Chem., 297, 124960 (2019).
35. Q. Ruan, Q. Wu, Y. Wang, X. Liu, F. Miao, Computing, 101, No. 6, 531–545 (2019).
36. M. G. Goydaragh, R. Taghizadeh-Mehrjardi, A. A. Jafarzadeh, J. Triantafilis, M. Lado, Catena, 202, 105280 (2021).
Review
For citations:
Xie W., Fu G., Xu J., Zeng M., Wan Q., Yao X., Yang P., Yao M. Evaluation of Sample Preparation Methods for the Classification of Children’s Ca-Fe-Zn Oral Liquid by LIBS. Zhurnal Prikladnoii Spektroskopii. 2024;91(1):167.
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