Researchers develop AI to detect fentanyl and derivatives remotely
The method uses infrared light spectroscopy and can be used in a
portable, tabletop device

Date:
August 25, 2020
Source:
University of Central Florida
Summary:
To help keep first responders safe, researchers have developed an
artificial intelligence method that not only rapidly and remotely
detects the powerful drug fentanyl, but also teaches itself to
detect any previously unknown derivatives made in clandestine
batches. The method uses infrared light spectroscopy and can be
used in a portable, tabletop device.



FULL STORY ==========================================================================
To help keep first responders safe, University of Central Florida
researchers have developed an artificial intelligence method that not
only rapidly and remotely detects the powerful drug fentanyl, but also
teaches itself to detect any previously unknown derivatives made in
clandestine batches.


==========================================================================
The method, published recently in the journal Scientific Reports, uses
infrared light spectroscopy and can be used in a portable, tabletop
device.

"Fentanyl is a leading cause of drug overdose death in the U.S.," said
Mengyu Xu, an assistant professor in UCF's Department of Statistics
and Data Science and the study's lead author. "It and its derivatives
have a low lethal dose and may lead to death of the user, could pose
hazards for first responders and even be weaponized in an aerosol."
Fentanyl, which is 50 to 100 times more potent than morphine according
to the U.S. Centers for Disease Control and Prevention, can be prescribed legally to treat patients who have severe pain, but it also is sometimes
made and used illegally.

Subith Vasu, an associate professor in UCF's Department of Mechanical
and Aerospace Engineering, co-led the study.

He said that rapid identification methods of both known and emerging
opioid fentanyl substances can aid in the safety of law enforcement and military personnel who must minimize their contact with the substances.



========================================================================== "This AI algorithm will be used in a detection device we are building
for the Defense Advanced Research Projects Agency," Vasu said.

For the study, the researchers used a national organic-molecules database
to identify molecules that have at least one of the functional groups
found in the parent compound fentanyl. From that data, they constructed machine-learning algorithms to identify those molecules based on their
infrared spectral properties. Then they tested the accuracy of the
algorithms. The AI method had a 92.5 percent accuracy rate for correctly identifying molecules related to fentanyl.

Xu said this is the first time a systematical analysis has been conducted
that identifies the fentanyl-related functional groups from infrared
spectral data and uses tools of machine learning and statistical analysis.

Study co-author Chun-Hung Wang is a postdoctoral scholar in UCF's
NanoScience Technology Center and helped study the compounds' spectral properties. He said identifying fentanyls is difficult as there are
numerous formulations of analogues of fentanyl and carfentanil.

Artem Masunov, a co-author and an associate professor in UCF's NanoScience Technology Center and Department of Chemistry, investigated the functional groups that are common to the chemical structures of fentanyl and its analogues.

He said that despite differences in the analogues, they have common
functional groups, which are structural similarities that enable the
compounds to bind to receptors within the body and perform a similar
function.

Anthony Terracciano, study co-author and a research engineer in UCF's Department of Mechanical and Aerospace Engineering, worked with Wang to
examine the infrared spectra properties. He said profiling and analysis
of infrared spectra is rapid, highly accurate, and can be done with a
tabletop device.

The current research used infrared spectral data from compounds
in gas form, but the researchers are working on a similar study to
use machine-learning to detect fentanyl and its derivatives in powder
form. The product of the technology is expected to be mature for practical on-site rapid identification by 2021.


========================================================================== Story Source: Materials provided by
University_of_Central_Florida. Original written by Robert Wells. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Mengyu Xu, Chun-Hung Wang, Anthony C. Terracciano, Artem E. Masunov,
Subith S. Vasu. High accuracy machine learning identification of
fentanyl-relevant molecular compound classification via constituent
functional group analysis. Scientific Reports, 2020; 10 (1) DOI:
10.1038/ s41598-020-70471-7 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200825113616.htm

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