How Infrared Spectroscopy Reveals Age from a Trace of Blood
Imagine a crime scene where the only evidence is a dried bloodstain. Traditional DNA analysis can identify a person if their profile exists in a database, but what if it doesn't? For investigators, this dead end could mean the difference between solving a case and it going cold forever. What if that bloodstain could tell us not just who someone is, but how old they are? This isn't science fiction—it's the cutting edge of forensic science made possible by a sophisticated analytical technique called Attenuated Total Reflection Fourier Transform-Infrared (ATR FT-IR) spectroscopy.
Extract demographic information from biological evidence through phenotype profiling.
Determine a person's age rapidly, nondestructively, and potentially at the crime scene.
Detect subtle biochemical changes that occur as we age through infrared light analysis.
Blood is far more complex than it appears. This vital fluid contains a rich biochemical signature that changes throughout our lives in predictable ways. Newborns, for instance, have fetal hemoglobin (HbF) which differs structurally from the adult version (HbA), containing different protein subunits 2 . Additionally, concentrations of components like lipids, glucose, and various proteins shift as we develop from infancy through adolescence into adulthood 2 .
ATR FT-IR spectroscopy works by shining infrared light onto a bloodstain sample and measuring which wavelengths are absorbed. Each molecule in the blood absorbs specific wavelengths, creating a unique spectral fingerprint.
The technique is nondestructive, preserving evidence completely intact for subsequent DNA analysis 1 . This is crucial for maintaining the chain of evidence.
In a landmark 2020 proof-of-concept study published in ACS Omega, researchers set out to determine whether ATR FT-IR spectroscopy could reliably classify blood donors into age categories based on their bloodstains 2 7 . The experiment was carefully designed to test this hypothesis across a diverse age range.
Blood samples were obtained from 20 donors representing three distinct age groups: newborns (under 1 year), adolescents (11-13 years), and adults (43-68 years) 2 7 .
The blood samples were spotted onto clean surfaces and allowed to dry completely, simulating real-world bloodstain evidence found at crime scenes.
Using an ATR FT-IR spectrometer, researchers directed infrared light at each bloodstain sample and measured the absorption across a spectrum of wavelengths.
The resulting spectral data was processed using sophisticated statistical algorithms called chemometrics, specifically Partial Least Squares Discriminant Analysis (PLSDA) 2 .
The study successfully demonstrated that despite visual similarities in blood spectra across different ages, statistical models could detect meaningful patterns that correlated with age.
The experimental results demonstrated that despite visual similarities in the blood spectra across different ages, the statistical models could detect meaningful patterns that correlated with age. The PLSDA model achieved an impressive 92% correct classification rate when using leave-one-out cross-validation, a statistical technique that tests the model's reliability 2 7 .
| Age Category | Age Range | Key Biochemical Features | Classification Accuracy |
|---|---|---|---|
| Newborn | <1 year | Fetal hemoglobin, higher red blood cell volume | High accuracy in differentiation |
| Adolescent | 11-13 years | Transitional hemoglobin profile, developing adult patterns | High accuracy in differentiation |
| Adult | 43-68 years | Adult hemoglobin, age-related metabolic changes | High accuracy in differentiation |
| Spectral Region (cm⁻¹) | Biochemical Assignment | Age-Related Changes |
|---|---|---|
| 3000-2800 cm⁻¹ | Lipids (fats) | Changing lipid profiles with development |
| 1700-1500 cm⁻¹ | Proteins (Amide I & II) | Hemoglobin changes from fetal to adult forms |
| 1390 cm⁻¹ | Lipids and proteins | Metabolic shifts between age groups |
| 1082 cm⁻¹ | Glucose (sugar) | Developmental changes in glucose metabolism |
The most significant differences between age groups were observed at wavelengths of 2909, 2871, 1659, and 1538 cm⁻¹, corresponding to specific molecular vibrations in proteins and lipids 2 .
The experiment required several specialized materials and analytical tools to produce its compelling results. These components represent the essential "toolkit" for forensic scientists applying this technology.
Measures infrared absorption by blood components. Portable versions exist for potential crime scene use.
Creates internal reflection for enhanced sensitivity. Allows minimal sample preparation.
Standardized medium for bloodstain storage. Preserves sample integrity for analysis.
Statistical analysis of spectral data. Identifies patterns invisible to human observation.
The implications of this research extend far beyond the laboratory. With the development of portable ATR FT-IR instruments, this technology could eventually be deployed directly to crime scenes, giving investigators immediate age estimates from blood evidence 1 . This real-time intelligence could significantly narrow suspect pools and guide investigations during their most critical early hours.
Future deployment of portable instruments could bring age determination directly to crime scenes.
This technology complements rather than replaces DNA analysis, preserving samples for genetic testing.
Future research aims to provide more precise age differentiations beyond broad categories.
The ability to determine age from a bloodstain using ATR FT-IR spectroscopy represents a remarkable convergence of analytical chemistry, statistics, and forensic science. This technology transforms what was once merely identifying evidence into intelligence-rich biological testimony that speaks to the very timeline of our lives. As research advances, we move closer to a future where a mere bloodstain can reveal not just someone's identity, but key chapters of their life story—beginning with how many years they've lived.