Optical Characterization of Neem Leaf by Laser Beam Scanning Spectroscopy (LIBS) and its spectral Properties
DOI:
https://doi.org/10.30526/39.1.4216Keywords:
Laser ablation spectroscopy, Laser plasma, Neem (Azadirachta indica), Plasma temperature, Electron densityAbstract
The present investigation is concerned with the spectral characterization by laser-induced bioluminescence spectroscopy (LIBS) of neem (Azadirachta indica) generated plasma, which is one of the most versatile and fast methods for the qualitative and quantitative analysis of various chemical elements, in organic as well as organic materials. Sample excitation and plasma formation were achieved using a pulsed energy laser (50, 100, 150, and 200 mJ). Finally, the emitted spectrum was analyzed in the framework of the Boltzmann and Saha equations for obtaining the plasma temperature and electron density. The influence of the laser power on the spectral parameters, for example, spectral line intensity, electron temperature, and electron density, was investigated, which could contribute to knowing more about the interaction between plasma and the component of the plant sample. The results show that LIBS is a fast and reliable tool for the analysis of chemical elements of the Neem sample, and at the same time, it does not require complex sample preparation, so it is suitable for clinical, pharmacological, and environmental applications. The work demonstrates a direct dependency of the laser power, the associated rise in plasma temperature, and corresponding electron density on the sensitivity of the spectrometer, emphasizing the need to optimize these parameters to achieve accurate and highly repeatable measurements.
References
1. Norazlina H, Suhaila A, Lili Shakirah H, Nurul Aniyyah MS. Green and free hazardous substances of neem oil lotions in promising market sustainability. Mater Today Proc. 2023. https://doi.org/10.1016/j.matpr.2023.01.017
2. Navale SS, Patil A. Herbal drugs used in various skin condition. Int J Novel Res Dev. 2023;8(2).
3. Parham S, Kharazi AZ, Bakhsheshi-Rad HR, Nur H, Ismail AF, Sharif S. Antioxidant, antimicrobial and antiviral properties of herbal materials. Antioxidants (Basel). 2020;9(12):1309. https://doi.org/10.3390/antiox9121309
4. Boanyah GY, Brenyah RC. The combined efficacy of neem (Azadirachta indica) seed oil and orange (Citrus sinensis) peel oil cream as a mosquito repellent. GSC Adv Res Rev. 2022;12(1):57–67. https://doi.org/10.30574/gscarr.2022.12.1.0184
5. Norazlina H, Muzammil MRAZ, Lili Shakirah H. Extraction of neem balm from Azadirachta indica leaves using Soxhlet method. Int J Synergy Eng Technol. 2024;5(1):36–46.
6. Marwa KJ, Aadim AK. Characteristics of lead and sulfur plasma parameters by optical emission spectroscopy. Iraqi J Sci. 2023;64(1):188–196. https://doi.org/10.24996/ijs.2023.64.1.19
7. Zaman MH, Rehman F, Tahir MS, Khan A, Amin N. Effect of preprocessing and normalization on classification of plant samples in machine learning assisted LIBS. Arab J Sci Eng. 2024;49(7):10003–10019. https://doi.org/10.1007/s13369-024-08716-0
8. Mahapatra PS, Dash S, Ratha S, Nayak PK, Mishra S, Sahu RK. Evaluation of medicinal plants using LIBS combined with chemometric techniques. Spectrochim Acta B. 2023;203:106738. https://doi.org/10.1016/j.sab.2023.106738
9. Huang T, Bi W, Song Y, Yu X, Wang L, Sun J, Jiang C. DMC-LIBSAS: A LIBS analysis system with double-multi convolutional neural network for accurate traceability of Chinese medicinal materials. Sensors. 2025;25:2104. https://doi.org/10.3390/s25072104
10. Cui X, Wang Q, Wei K, Teng G, Xu X. Laser-induced breakdown spectroscopy for classification of wood materials using machine learning with feature selection. Plasma Sci Technol. 2021;23(5). https://doi.org/10.1088/2058-6272/abf1ac
11. Kazim WA, Ali AH. Analysis of aloe vera plasma parameters using optical emission spectroscopy. Iraqi J Phys. 2025;23(1):44–54. https://doi.org/10.30723/ijp.v23i1.1306
12. Betlej I, Skrzeczanowski W, Nasiłowska B, Bombalska A, Borysiuk P, Nowacka M, Boruszewski P. Application of LIBS to determine graphene oxide incorporation on wood surfaces. Coatings. 2025;15(1):34. https://doi.org/10.3390/coatings15010034
13. Ahmed MA, Algwari QT, Younus MH. Plasma properties of a low-pressure hollow cathode DC discharge. Iraqi J Sci. 2022;63(6):2532–2539. https://doi.org/10.24996/ijs.2022.63.6.20
14. Wang YF, Zhu XM. Development of optical emission spectroscopy method with neural network model: determining electron density in xenon microwave discharge. J Appl Phys. 2024;136(24):243302. https://doi.org/10.1063/5.0243484
15. Mishra H, Tichý M, Kudrna P. OES study of plasma parameters in low-pressure hollow cathode plasma jet and planar magnetron. Vacuum. 2022;205:111413. https://doi.org/10.1016/j.vacuum.2022.111413
16. Ahmed AF, Mutlak FA, Abbas QA. Cold plasma effect for achieving fullerene and ZnO-fullerene hydrophobic thin films. Appl Phys A. 2022;128:147. https://doi.org/10.1007/s00339-021-05252-8
17. Iftikhar Y, Jamil N, Nazeer N, Tahir MS, Amin N. Optical emission spectroscopy of nickel-substituted cobalt–zinc ferrite. J Supercond Nov Magn. 2021;34(7):1–6. https://doi.org/10.1007/s10948-020-05734-5
18. Majeed NF, Ali AH, Mazhir SN, Akram RS. Effect of alum activated by cold plasma on mice wounds using textural analysis. Baghdad Sci J. 2024;21(12):4118–4127. https://doi.org/10.21123/bsj.2024.9026
19. Mullick A, Balagopalan S, Sooraj S, Karthikeyan B. Spectroscopic analysis of acid rain-induced stress in neem (Azadirachta indica). SSRN. 2025. https://doi.org/10.2139/ssrn.5187401
20. Alexandros S, Theofanis G, Emmanouil K, Kosmidis C. Identification of wood specimens utilizing fs-LIBS and machine learning techniques. Eur Phys J Appl Phys. 2024;99:11. https://doi.org/10.1051/epjap/2024230215
21. Ali AH, Shakir ZH, Mazher AN. Influence of cold plasma on sesame paste and nano sesame paste based on co-occurrence matrix. Baghdad Sci J. 2022;19(4):855. https://doi.org/10.21123/bsj.2022.19.4.0855
22. Elli B, Dimitris S, Nikos G. LIBS assisted by machine learning for olive oil classification. Spectrochim Acta B. 2019;163:105746. https://doi.org/10.1016/j.sab.2019.105746
23. Zaplotnik R, Primc G, Vesel A. Optical emission spectroscopy as a diagnostic tool for atmospheric plasma jets. Appl Sci. 2021;11(5):2275. https://doi.org/10.3390/app11052275
24. Karthigaikumar P, Justin VL. LIBS with neural network approach for plastic identification in waste management. Appl Chem Eng. 2024;7(1). https://doi.org/10.24294/ace.v7i1.3092
25. Rachdi L, Sushkov V, Hofmann M. OES diagnostics for plasma parameters in Duo-Plasmaline discharge. Spectrochim Acta B. 2022;194:106432. https://doi.org/10.1016/j.sab.2022.106432
26. Giannakaris N, Gürtler G, Stehrer T, Mair M, Pedarnig JD. OES of industrial atmospheric plasma jet: electron temperature study. Spectrochim Acta B. 2023;207:106736. https://doi.org/10.1016/j.sab.2023.106736
27. Mohsen M, Fatemeh R, Parvin KD. Hybrid machine learning algorithms for quantitative LIBS analysis. J Appl Spectrosc. 2023;90(3). https://doi.org/10.1007/s10812-023-01585-9
28. Datta R, Ahmed F, Hare JD. Machine-learning-assisted analysis of visible spectroscopy in pulsed-power plasmas. IEEE Trans Plasma Sci. 2024;52(10):4755–4763. https://doi.org/10.1109/TPS.2024.3364975
29. Sarsa A, Jiménez-Solano A, Dimitrijević MS, Yubero C. Exact Stark analytical function for Hα line related with plasma parameters. SSRN. 2025. https://doi.org/10.2139/ssrn.4786396
30. Oliver TA, Michoski C, Langendorf S, LaJoie A. Automated Bayesian estimation of plasma temperature and density from emission spectroscopy. Rev Sci Instrum. 2024;95(7):073520. https://doi.org/10.1063/5.0192810.
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