Abstract
Fentanyl is an anesthetic with a high bioavailability and is the leading cause of drug overdose death in the U.S. Fentanyl and its derivatives have a low lethal dose and street drugs which contain such compounds may lead to death of the user and simultaneously pose hazards for first responders. Rapid identification methods of both known and emerging opioid fentanyl substances is crucial. In this effort, machine learning (ML) is applied in a systematic manner to identify fentanyl-related functional groups in such compounds based on their observed spectral properties. In our study, accurate infrared (IR) spectra of common organic molecules which contain functional groups that are constituents of fentanyl is determined by investigating the structure–property relationship. The average accuracy rate of correctly identifying the functional groups of interest is 92.5% on our testing data. All the IR spectra of 632 organic molecules are from National Institute of Standards and Technology (NIST) database as the training set and are assessed. Results from this work will provide Artificial Intelligence (AI) based tools and algorithms increased confidence, which serves as a basis to detect fentanyl and its derivatives.
Highlights
The data set for both training and testing was obtained from the public database of National Institute of Standards and Technology, which provides spectral information of the selected 632 compound molecules considered in this work., Such compounds can be categorized into one of the following eight groups: (i) amide only, (ii) aniline only, (iii) benzene only, (iv) piperidine only, (v) amide and aniline simultaneously, (vi) amide and benzene simultaneously (vii) distinct aniline and benzene simultaneously, and (viii) none of the above constituent functional groups
The functional data analysis is implemented using the R package fda.usc[28] and the Receiver Operating Characteristic Curve (ROC) curve and Area Under the Curve (AUC) are obtained from R package p ROC29
The algorithm has a relatively high error rate in distinguishing the aniline group and benzene, due to fact that these functional groups share common structure. Synthetic opioid analogues such as, fentanyl, have been the cause of many accidental deaths across the world, and the detection of low concentrations of these harmful substances at a distance via spectroscopic techniques is crucial for law enforcement
Summary
We aimed to answer the following question: can we identify constituent functional groups of fentanyl and its analogues from the IR absorption data?. The goal of this study is to distinguish if a given molecule contains a certain functional group such as amide
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