Lawyers for Casey Anthony, the Florida mother accused of murdering her two-year-old daughter, Caylee in 2008, say the smell of death coming from her car days after the disappearance of her daughter was the result of a garbage bag that had been left in the trunk. A scientist used an untested forensics technique to tell a different story: that smell was the lingering odor of human decomposition.
Lawyers for Casey Anthony, the Florida mother accused of murdering her two-year-old daughter, Caylee in 2008, say the smell of death coming from her car days after the disappearance of her daughter was the result of a garbage bag that had been left in the trunk. A scientist used an untested forensics technique to tell a different story: that smell was the lingering odor of human decomposition.
Prosecutors brought in Oak Ridge National Laboratory senior researcher Arpad Vass, an expert in odor detection, to test air from Anthony’s car for signs of human composition. Vass and a colleague were given a sealed can containing a scrap of upholstery from the car, and using a syringe they extracted some of the air inside.
The scientists then ran the air through a gas chromatography-mass spectrometry device to analyze it for substances, and cross-referenced those results against a database of more than 400 chemical traces of decomposition that Vass has compiled. According to Vass, the air contained an “overwhelmingly strong” scent of decomposition.
Vass said he and his team identified 51 chemical components from the carpet of the car trunk. Of those, 41 were consistent with human decomposition, including butyric acid, the first compound found in human decomposition, in the carpet.
Although this technique has never been used in court before, it could become a regular tool in both the investigation and prosecution of crimes.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.