Spectroscopic Advances Propel LIBS to the Forefront of Forensic Bone Identification

At the October 2025 SciX Conference, Jorge Caceres of Complutense University (Madrid, Spain) highlighted a rapidly emerging trend in forensic and archaeological science: the use of laser-induced breakdown spectroscopy (LIBS) paired with advanced supervised classification methods to reassociate mixed or commingled bone remains. His work underscores a growing consensus that spectroscopy, particularly LIBS, offers a fast, minimally destructive, and analytically decisive pathway for bone identification in scenarios where traditional methods struggle.

LIBS stands out because it derives a complete elemental fingerprint from a single laser pulse. When the laser ablates a microscopic region of bone, the resulting plasma emits a characteristic spectrum reflecting the bone’s full atomic composition. This rapid spectral readout captures both major elements, such as calcium, phosphorus, and magnesium, and trace components like strontium, zinc, copper, and iron. Because the elemental makeup of bone reflects a lifetime of diet, metabolism, environmental exposure, and health history, everyone develops their own chemically distinct profile that can be recorded and compared with high fidelity through spectroscopy.

In a recent interview with Spectroscopy (1), Caceres emphasized that LIBS offers major advantages over conventional techniques used in forensic anthropology. Unlike DNA analysis, which may fail when samples are degraded, or wet chemical methods that require grinding, digestion, or solvents, LIBS demands virtually no sample preparation. Gentle cleaning to remove superficial debris is often sufficient, preserving both time and material. Just seconds of spectroscopic acquisition can yield a multidimensional dataset rich enough for confident classification.

Applying LIBS to bones does present several challenges. Variable porosity, roughness, mineral heterogeneity, and contamination can all influence spectral stability. To overcome these factors, Caceres and his team implemented rigorous spectroscopic protocols, which include collecting multiple spectra, removing outliers, targeting stable surfaces, and sometimes using initial laser pulses for surface “cleaning.” These steps ensure that the acquired spectra accurately reflect internal bone chemistry rather than environmental noise.

Once high-quality spectra are collected, data processing becomes pivotal. Background subtraction, intensity normalization, and careful emission-line selection are essential to ensuring reproducibility across samples. Caceres compared seven supervised classification models and found neural networks to be the most robust, achieving 100% accuracy, sensitivity, and generalization capability. The strength of neural networks lies in recognizing subtle spectral patterns and distinguishing between true individual signatures and surface-induced artifacts.

LIBS’s ability to penetrate both surface and subsurface bone layers further enhances reliability. In degraded or contaminated remains, which are common in mass grave investigations, archaeological contexts, and long-term burials, surface chemistry may not reflect true biological composition. By probing deeper layers, LIBS retrieves chemically stable information that strengthens individual reassociation and reduces misclassification risk.

Looking forward, Caceres sees broad potential for spectroscopy-driven identification. LIBS and AI classification could streamline individual reassembly in complex forensic cases, enable rapid in situ triage at crime scenes or archaeological digs, and support studies of diet, mobility, and health through bone biogeochemistry. With demonstrated precision and minimal operational burden, LIBS-based spectroscopy is poised to become a standard tool in forensic and bioarchaeological workflows, bringing speed, clarity, and scientific rigor to contexts where answers are urgently needed.

Reference

1. Wetzel, W. Analyzing Bone Chemistry with LIBS. Spectroscopy. https://www.spectroscopyonline.com/view/analyzing-bone-chemistry-with-libs (accessed 2025-12-01)

This article was written with the assistance of AI.