The Latest Advancements and Applications of Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a popular technique for elemental analysis applications, often used in forensic and industrial contexts. Recent studies have demonstrated the importance of advanced data processing, such as background subtraction and normalization, to enhance analytical accuracy and sensitivity.

In this Q&A overview, we explore the latest advancements and trends in LIBS and how it is being used in material analysis.

What is laser-induced breakdown spectroscopy (LIBS), and what are its core advantages over traditional analytical techniques?

LIBS is an atomic spectroscopy technique that uses a high-energy pulsed laser to ablate a microscopic layer of a sample's surface, which generates a high-temperature microplasma.^1–3 Once the high-temperature plasma cools, excited particles emit light at element-specific wavelengths, which is collected and analyzed by a spectrometer to identify composition.²

There are a few key advantages of LIBS that make it a popular technique of choice in elemental analysis applications. First, LIBS does not require extensive sample preparation. The technique does not need to use chemical reagents or perform any acid digestion process; as a result, rapid analysis can be achieved in seconds.^2,4 Second, LIBS has the ability to detect nearly every element, including light elements (like lithium, beryllium, boron, carbon, and sodium) where portable XRF is blind or underperforms.² And finally, LIBS allows for excellent depth profiling. Because the technique uses a high-energy pulsed laser, researchers can peel back surface weathering, dust, or contamination to analyze internal chemical stratigraphy.²

How does LIBS assist in forensic anthropology, particularly in re-associating human bone remains?

LIBS has been widely used in forensic anthropology. Commingled bone remains present challenges in forensic and archaeological settings, especially when DNA is degraded.⁴ Because bones incorporate major and trace elements (such as Ca, P, Mg, Sr, Zn, Fe, Cu, and Mn) throughout life based on diet, metabolism, age, sex, and environment, each individual has a unique chemical signature.⁴

LIBS can capture this complete elemental fingerprint in seconds with a single pulse.⁴ Paired with artificial intelligence (AI), particularly supervised neural networks, it re-associates bones with 100% accuracy.⁴ Crucially, LIBS's direct ablation allows it to penetrate degraded or soil-contaminated outer layers.⁴ By generating plasma from cleaner internal microlayers, it accesses stable biologically integrated elements that preserve the individual's authentic chemical signature.⁴

What is the “self-absorption effect” in LIBS, and how does it behave differently in high-frequency fiber-laser systems?

The “self-absorption effect” occurs when photons emitted from the hot plasma center are reabsorbed by similar low-energy particles in the cooler outer layer.¹ This effect reduces observed spectral line intensity, causing calibration curve nonlinearity and self-reversal.¹

In conventional LIBS using low pulse repetition rate (PRR) lasers (≤100 Hz), self-absorption behaves differently than in high-PRR fiber-laser LIBS (FL-LIBS).¹ In FL-LIBS, self-absorption actually increases with higher laser output power and larger single-pulse ablation areas (SPAA).¹ The uncooled, high-frequency plasma shields subsequent laser pulses, lowering plasma temperature, while a larger SPAA creates a larger plasma volume that cools faster.¹ Optimizing these laser parameters can reduce the self-absorption factor from 1.29 to 0.61, greatly improving calibration linearity and measurement accuracy.¹

How do pulse width and repetition rate impact the analysis of aluminum alloys when using fiber lasers?

In aluminum alloys, tuning fiber laser parameters significantly enhances the detection sensitivity and improves the limits of detection (LOD) for key elements (Mg, Mn, Cu, Fe, Zn).⁵

Let’s consider pulse width first. Moderately long pulse widths (20 ns to 200 ns) are beneficial under limited pulse energy.⁵ They prolong laser-plasma interaction to further excite ablated analytes and reduce heat dissipation to elevate plasma temperature.⁵ Meanwhile, raising the repetition rate (for example, from 1 Hz to 37 kHz) induces a strong heat accumulation effect.⁵ Shortened delays prevent the sample from cooling fully, raising the initial temperature for the next pulse.⁵ This facilitates the melting and evaporation of elements with low melting/boiling points, achieving up to a 40.4% sensitivity boost and a 27.0% LOD improvement.⁵

How can LIBS be used to detect aging and surface hardness in PVC cable sheaths?

Aging in PVC sheaths evaporates plasticizers, increasing surface hardness and reducing insulation safety.⁶ Traditional mechanical testing is dangerous because of close high-voltage contact, making LIBS a safe, non-contact, in situ alternative.⁶

When a laser ablates a harder sample, the stronger shock wave accelerates free electrons, increasing collisional ionization and plasma excitation temperature.⁶ Hardness is then estimated via two models. The first model is the Ion-to-Atom Line Ratio. In this model, harder samples increase ion density relative to atom density. The intensity ratio of Ca II 422.01 nm to Ca I 429.90 nm correlates linearly with hardness.⁶ The second model is based on plasma excitation temperature, which is calculated using Ca I lines via the Boltzmann plot method and correlates linearly with hardness. This temperature model is more stable and less vulnerable to self-absorption errors.⁶

How does LIBS combined with neural networks address the challenges of tracking crop straw combustion smoke?

Tracing the origin of agricultural smoke is difficult because smoke concentrations fluctuate constantly.³ LIBS can analyze combustion smoke to detect carbon, air components (N, O), and trace heavy metals (Fe, Mn, Sr, Ba).³

Because raw spectra contain thousands of data points, machine learning is deployed to reduce complexity. Spectra are standardized using Z-score normalization.³ Emission lines of C, CN, Si, Mg, Ca, and so forth are selected as features to prevent high dimensionality and overfitting.³ A Back Propagation (BP) neural network is trained using these variables.³ By optimizing hyperparameters and using L2 regularization, the tuned BP model classifies and traces crop straw smoke (rapeseed, corn, or sesame) with 86.67% accuracy.³

References

1. Qin, Y.; Xu, Z.; Cai, D.; Li, J.; Ma, Q.; Zhao, N.; Guo, L.; Zhang, Q.; Lue, Q. Experimental Investigation of the Self-Absorption Effect in Laser-Induced Breakdown Spectroscopy Based on Fiber Laser Ablation. Spectroscopy Suppl. 2025, 40 (wp7). DOI: 10.56530/spectroscopy.rd2690f6
2. Senesi, G. S. LIBS in Geosciences: A Practical Tutorial. Spectroscopy 2026, ASAP. Available at: https://www.spectroscopyonline.com/view/libs-in-geosciences-a-practical-tutorial
3. Shen, L.; Tian, L.; Tian, D.; Ji, H.; Liu, Y. Laser Induced Breakdown Spectroscopy Combined with a Tuned Back Propagation Algorithm for Oil Crop Straw Combustion Smoke Detection and Traceability. Spectroscopy 2024, ASAP. Available at: https://www.spectroscopyonline.com/view/laser-induced-breakdown-spectroscopy-combined-with-a-tuned-back-propagation-algorithm-for-oil-crop-straw-combustion-smoke-detection-and-traceability
4. Caceres, J.; Wetzel, W. Analyzing Bone Chemistry with LIBS. Spectroscopy Online, 2026. Available at: https://www.spectroscopyonline.com/view/analyzing-bone-chemistry-with-libs (accessed August 20, 2026).
5. Zhang, S.; Xiao, Q.; Chen, F.; Chen, Y.; Wu, H.; Wang, G. Exploitation of Fiber Laser Induced Breakdown Spectroscopy on the Quantitative Analysis of Aluminum Alloys. Spectroscopy Suppl. 2026, 41 (wp8). Available at: https://www.spectroscopyonline.com/view/exploitation-of-fiber-laser-induced-breakdown-spectroscopy-on-the-quantitative-analysis-of-aluminum-alloys
6. Lin, X.; Wang, G.; Lin, J.; Huang, Y. Research on Hardness Detection Method of Polyvinyl Chloride Cable Sheath Material Based on Laser-Induced Breakdown Spectroscopy. Spectroscopy 2025, ASAP. DOI: 10.56530/spectroscopy.di9189u6