麻豆淫院

Researchers from the Texas A&M College of Agriculture and Life Sciences Department of Entomology and Department of Biochemistry and Biophysics have developed a technique that uses infrared light and machine learning to reveal the sex of blow fly larvae found on human remains. This innovative approach may help investigators estimate time of death with greater speed and accuracy.

The study, in the Journal of Forensic Sciences, was led by Aidan Holman, a doctoral student in the lab of Dmitry Kurouski, Ph.D., associate professor in the Department of Biochemistry and Biophysics, who supervised the research.

The project also involved Aaron Tarone, Ph.D., a professor in the Department of Entomology; Davis Pickett, a research assistant in Kurouski's lab; and Hunter West, a postbaccalaureate researcher in the Tarone lab.

How blow fly biology impacts forensics

In forensic entomology, investigators can use the time of insect colonization to estimate time of death based on how long flies have been developing on remains. Blowflies are often the first insects to colonize a body after death, making their developmental stage a critical clue for forensic entomologists.

Male and female flies of the species they studied develop at slightly different rates鈥攁t least nine hours apart depending on temperature, which can skew postmortem estimates. At the larval stage, males and females are nearly impossible to visually distinguish, and current laboratory methods for doing so require destroying the insects for molecular analysis.

The researchers' new method works differently by using spectroscopy, a technique that shines light on a sample and measures how its molecules respond. The way a sample scatters light creates distinct "fingerprints" based on the molecular makeup of the insect, which the researchers found could indicate whether the insect was male or female.

A tool for forensic field work

For the study, the researchers first separated fly colonies by sex, then used a handheld infrared spectroscopy device to scan live larvae and generate the spectra or "fingerprint" based on the composition of proteins and fats within the insect.

From there, they tested three machine learning models to sort the larvae by sex using the spectra. Two of the models achieved more than 90% accuracy, with the most successful at more than 95%.

Because the technique is quick, requires only a handheld device and doesn't destroy the samples, the researchers believe it could provide value to real-world casework by informing the development of sex-specific growth curves.

"You could use this handheld device out in the field and still be able to conduct further testing back in the lab afterward," Holman said.

The technique also shows how machine learning might apply to forensics, which Holman believes could allow for greater objectivity and speed in forensic analysis.

Uses from crime scenes to pest control

While the study focused on forensic timelines, the method used may have broader implications. For instance, sex determination in larvae could play a role in insect-based biocontrol efforts such as the sterile insect technique, which relies on releasing sterile male insects to suppress pest populations.

Tarone said this is of particular interest right now, as the New World screwworm, a species of blow fly related to the one studied in this paper, garners more attention.

"Accurately sorting male from female larvae has potential applications beyond forensics, including agriculture and biosecurity," he said.

Holman's research is housed within the Kurouski biochemistry lab, where vibrational spectroscopy is used to learn more about everything from human health and nutrition to plant pathology. The project showcases just one of the many potential applications of the technology.

"We're taking advanced analytical chemistry and using it for public service," Kurouski said. "It's a great example of how interdisciplinary work can solve practical problems."