A group of insects munching on a rotting murder victim is not a sight for the squeamish, but for some, it’s proof. The age and species of a maggot can provide essential information to forensic entomologists investigating murders. (For example, a single swinging horse fly maggot found on a dead body far from water gave entomologists in 2022 an important clue as to where the body came from.) By sifting through these fly larvae, investigators can potentially learn when and where the crime occurred, whether the body was moved or what toxins were involved.
For example, blowflies are among the earliest insect colonizers of corpses; They usually smell the dead body and lay their eggs within a few minutes to a few hours. How fast maggots (also called larvae) develop depends on heat, moisture, and the species and sex of the insect. To use this evidence, investigators must typically grow the larvae to adulthood in a laboratory setting and then identify them visually or by genetic sequencing. But what if the larvae are dead or missing, there is no high-quality DNA or there is no time or equipment to sequence the genomes of the flies? “People in crime labs don’t have the local expertise or resources to be able to routinely do DNA analysis on insect evidence,” says Rabi Musah, a bioorganic chemist at Louisiana State University.
To overcome these challenges, Moses and other researchers have combined machine-learning algorithms with methods such as infrared spectroscopy and chemical profiling to quickly determine the species and sex of maggots. Such tools can help experts rapidly identify insects without larval DNA or without larvae altogether, leaving only what they leave behind – saving time and money typically spent on sequencing. They can also help investigators take measurements at a crime scene to determine larval sex.
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Moses measured the insect’s chemical profile, called a metabolome. eggs, larva And pupa Using a type of mass spectrometry – a technique that can separate molecules called metabolites based on their mass and charge. With these data, he and his team are building a large metabolic database for most insects that colonize decomposed remains. His team’s machine-learning algorithms trained on the data will let investigators use a mass spectrometer, which is less expensive and much easier to use than a DNA sequencer, to reliably match a new chemical profile to an insect species in less than five minutes.
A similar approach could also work without the larvae themselves. Sometimes people find completely dismembered bodies several months or years after a murder. By that time, Musah says, the larvae are long gone, and the only remaining evidence of the insect is the hard shell-like exterior of the pupa, the tools of metamorphosis discarded after the larva becomes an adult fly.
It is impossible to identify pupa casings with the naked eye, and in many cases, the DNA contained within them is too old and degraded for sequencing. But as Moses’ group pointed out in a recent newspaper In forensic chemistryTheir method – machine-assisted classification followed by chemical fingerprinting – also works with casings. Detecting the chemical profile of the casings can also reveal toxins in the victims’ bodies as the larvae store them in their pupal casings. (The rate of molecular breakdown may someday also give an indication of the age of the shells.)
Other groups are also attempting to use machine learning to catalog larval visitors to crime scenes: for example, a team of Texas A&M researchers recently developed a method that combines Infrared measurement from a handheld device Using machine learning to identify the sex of blowfly larvae.
Male and female larvae develop at different speeds and can help investigators determine when they first occupied the remains, but their sex cannot be distinguished by eye. To identify sex, investigators can crush the larvae and amplify their DNA using PCR, which takes time, renders the larvae useless for any further study, and has only an 80 percent chance of working correctly. Texas A&M toxicology graduate student Aidan Holman and his colleagues set out to determine the sex of the larvae without crushing them.
After first rearing male and female larvae separately, Holman’s group used a handheld infrared spectroscopy instrument to “zap” them and measure the light emitted. The proteins, fats and other molecules that make up the larvae scatter light in unique ways, producing a distinctive “spectral signature” depending on sex. The researchers then trained a machine-learning model on this spectral data and found that it could predict the sex of the larvae with more than 90 percent accuracy. Next, they will collect data from a very large selection of flies to train their model.
Paola Magni, a forensic entomologist at Murdoch University who is not involved in either project, emphasizes that these machine-learning databases will need to be officially checked, as DNA sequence banks are, so that the results are not later legally overturned. And using AI more widely in the process could be risky, she says. “An artificial intelligence coin flip can be very dangerous in a forensic context because you can actually cause a miscarriage of justice,” she says. Additionally, both he and Moses highlight that more research is needed into how other substances in the body may skew molecular markers — and Moses is pulling data from as large and global an insect sample as possible to find markers that remain stable. “Growth and expansion of databases involves a never-ending process,” says Musah.
Texas A&M forensic entomologist Jeff Tomberlin, who was not involved in either project, believes cutting-edge methods like machine learning should be integrated into forensic case work. But, they noted, their long-term accuracy, precision and potential biases also need to be carefully studied. “We are in the early stages of applying these methods to this particular area,” he says. “So if you think of it like an arc, we’re at the beginning of the arc.”
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