Corpses leave clues behind in the soil long after they’re gone

ASU research has potential to help forensic teams solve cases when a victim’s body has been moved

Newswise — President’s Professor Pamela Marshall (left) and Assistant Professor Katelyn Bolhofner pose with soil samples in one of their labs on Thursday, Feb. 19, on the West Valley campus. The researchers analyze the microbial and chemical traces left behind when remains are moved, uncovering patterns of postmortem change that can guide forensic investigations. Photo by Charlie Leight/ASU News.  Download article assets

 It is not uncommon for a body to be moved after a murder, usually to hide or eliminate evidence.

And while the Arizona desert may seem like the perfect place to commit such a crime, a new study shows that a cadaver can still leave critical clues behind in that harsh environment.

Arizona State University researchers have found that trace elements linger at an original dump site even after an extensive amount of time. These elements can provide insights into postmortem processes, helping forensic investigators uncover clandestine burials and relocate the remains of murder victims.

“A lot of times a murderer will kill someone and put the body somewhere, stash it, panic and then move it. And how can you ever trace where they have done this?” said Assistant Professor Katelyn Bolhofner with the School of Interdisciplinary Forensics, who collaborated with President’s Professor Pam Marshall from the School of Mathematical and Natural Sciences on the study.

“The surprising result was that even with the hot Arizona summer, we could still tell that there had been something that was dying and decomposing in that spot in the desert,” Bolhofner said.

Uncovering signatures in the soil

Prior to the study, Bolhofner and Marshall believed that any evidence on the original site of a transported body would be baked under Arizona’s scorching summer sun.

That was far from the case.

The study used two 200-pound pig models that were dressed up in jeans and a button-up shirt by students, since murder victims are commonly clothed. They were left to decompose in large cages (to keep scavenging animals away) in various environments and seasons in the Sonoran Desert.

After 25 days, the remains were moved to a secondary burial location. Then, over a period of nine months, the researchers tested the soil where the model was originally placed, where it was moved and in a location adjacent to the original burial as a control.

“It’s a multifaceted, year-round project to try to determine timing, insects involved, and the humidity and the temperature and many other of these factors,” Bolhofner said.

What they found were distinct microbial fingerprints where death gave way to new life — bacteria and fungi that once lived inside or on the body and were released into the surrounding ground as decomposition occurred.

“It turned out to be a really crazy finding,” Bolhofner said. “It’s like the murder victim is leaving a signature of themselves in death … almost like leaving breadcrumbs right around the desert (indicating) that they had been there, and those breadcrumbs stayed there in the soil, invisible to the naked eye for a year.”

“No one has ever done an experiment like this,” Marshall said. “It was unique because no one had looked at a dumped body that was then moved. It was also unusual because no one’s been looking at the Sonoran Desert.”

It’s like the murder victim is leaving a signature of themselves in death … almost like leaving breadcrumbs right around the desert.

Kaitlyn BolhofnerAssistant professor of forensics

Counting on collaboration

The study was a collective and collaborative effort.

ASU graduate Jennifer Matta Salinas worked on the study for her honors thesis. She collected and processed the data, and extracted DNA for the study.

“I felt like my results definitely opened the door to a novel area of forensic science that has many avenues to explore and to still verify,” said Salinas, who earned a bachelor’s degree in forensic science. “I’m hoping someday it is used to help find someone’s loved ones months or years after their disappearance no matter where the environment is.”

The DNA was then prepped and analyzed by Kristina Buss in ASU’s Bioinformatics Facility and Desert Southwest Genomics Center, and Teaching Professor Ken G. Sweat performed the chemical analysis of the soil.

“We here in the School of Mathematical and Natural Sciences and the School of Interdisciplinary Forensics are very collaborative — we depend on each other,” Marshall said. “Without Jennifer needing to write her thesis, this wouldn’t have happened. Without Ken doing the elemental analysis, that part wouldn’t have happened either.”

Future forensic potential

Stuart Somershoe, a retired police detective with the Phoenix Police Department’s missing-persons division, was also a part of the project.

According to the World Population Review, Arizona has one of the highest number of missing persons in the nation, with more than 1,000 people missing and 1,588 resolved cases in 2025.

Somershoe says the desert plays into those statistics. He sees the potential application of this study in cold cases and missing persons cases both now and in the future.

“I read the study and could see the value in police investigations,” Somershoe said. “It would certainly be something that could be used.”

Somershoe said that as this research develops and becomes more well-known, it could become a technique as commonly used as DNA testing.

But first, more experiments and studies will be needed.

“We’re way in our infancy,” Marshall said.

The researchers are interested in taking the study on the road to see if the findings can be confirmed in other climates, but Marshall is hopeful.

“This study is really specific to this climate and this landscape and this geography,” Marshall said. “Our soil and our climate (are) so harsh and so odd. The fact that this can be proven here should show that in other climates, it’s much more doable. Those climates are much friendlier.”

The researchers also plan to verify that human remains would yield similar results.

“We need to confirm that the things we’re seeing in pigs are the same in humans,” she said. “We need to figure out how what we have discovered is transferable.”

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Newswise — New forms of fentanyl are created every day. For law enforcement, that poses a challenge: how do you identify a chemical you’ve never seen before?

Researchers at Lawrence Livermore National Laboratory (LLNL) aim to answer that question with a machine learning model that can distinguish opioids from other chemicals with an accuracy over 95% in a laboratory setting. The foundation for this new technique was published in Analytical Methods.

To identify synthetic opioids like fentanyl now, chemists try to match their signature to a library of a few hundred known samples. But studies suggest there could be thousands of unknown forms, some more dangerous than others. Recognizing those new versions requires a reference-free identification system: a way to catch an opioid even if it does not exist in a chemical database yet.

“When law enforcement finds a new clandestine drug operation, those labs often produce never-before-seen fentanyl derivatives. We can’t just go check a database, and we can’t just go back to who made it and ask how they did it,” said LLNL computational mathematician and author Colin Ponce. “And law enforcement needs to identify the samples they find quickly because there’s going to be another sample tomorrow. I think that’s a little bit of a unique situation.”

Machine learning might seem like a natural fit to identify novel or unknown opioids. And it is — to an extent. The method works best with large data sets, which are difficult to generate for toxic substances like synthetic opioids. 

To even get a machine learning algorithm off the ground, the team had to create the chemical data. They did so with LLNL’s mass spectrometry capabilities coupled to an autosampler, which enabled them to measure hundreds of samples under the same experimental conditions. This minimized variables for the machine learning algorithms. 

“In the world of AI, data is gold, and if you don’t have good data, then you’re not going to generate accurate machine learning models,” said LLNL chemist and author Carolyn Fisher. “Good data is something that we can control and generate at LLNL.” 

With that data in hand, they tried different machine learning techniques as they homed in on the best method: a random forest model. 

“When a model like this eventually gets into the hands of a user, the output has to be interpretable and trustworthy,” said LLNL scientist and author Kourosh Arasteh. “We explored machine learning methods ranging from simple regression and random forests to more complex neural network approaches to balance interpretability with performance.” 

The random forest approach runs through a collection of decision trees. Each tree asks a series of questions about the data and, based on each answer, lands on a prediction: opioid or not. Together, they vote on the final classification.

“Our 650 samples are not the same as having 300,000 samples. On the machine learning side, we needed to make sure that we were designing techniques that that were appropriate for that kind of scale,” said Ponce.

This study trained and tested the algorithm with analytically pure samples. These ideal chemicals contain no contaminants or impurities.

“The challenge is that nothing is analytically pure in the real world,” said Fisher. “The next step is to add in background noise and have the AI understand what it should care about during a classification task.”

Fisher and Ponce emphasized that this work would have been impossible without collaboration across the disciplines of data science and chemistry. The two are friends outside of work, and this study, a Laboratory Directed Research and Development project, emerged from a series of organic conversations between them.

“To me, this project really captures what LLNL does best,” said fellow author and LLNL software engineer Steven Magana-Zook. “When you get chemists and data scientists working side by side, you end up with results that neither group could get on their own. That kind of cross-disciplinary work is exactly what makes this place so strong.”

That approach, while essential to the work, initially proved to be an obstacle. The team faced rejection of this manuscript from two journals — reviewers in chemistry didn’t fully grasp the machine learning aspects and experts on the computational side felt uncertain about the chemistry.

“I don’t think people talk about failure enough. It’s so common in science. We fail so much more than we succeed,” said Fisher. “But we keep iterating and improving. I’m proud of our resilience.” 

The team’s persistence paid off. Looking ahead, they aim to further develop their algorithm using real-world samples with higher background signals. 

Other LLNL coauthors include Roald Leif, Alex Vu, Mark Dreyer, Brian Mayer and Audrey Williams.