Trace Detection of Adulterants in Illicit Opioid Samples Using Surface-Enhanced Raman Scattering and Random Forest...

Area of Science:

• Analytical Chemistry
• Forensic Science
• Public Health

Background:

• Illicit drug supply variability necessitates drug checking for harm reduction.
• Community drug checking requires rapid, point-of-care analytical methods.
• Traditional methods may lack the sensitivity or speed for community settings.

Purpose of the Study:

• To evaluate surface-enhanced Raman spectroscopy (SERS) combined with a random forest classifier for detecting adulterants in street opioid samples.
• To assess the performance of SERS against mass spectrometry and infrared spectroscopy for trace adulterant detection.
• To demonstrate the feasibility of SERS for point-of-care drug checking services.

Main Methods:

• Surface-enhanced Raman spectroscopy (SERS) was employed for spectral acquisition.
• A random forest classifier was developed to analyze SERS data.
• Samples were analyzed using both SERS and mass spectrometry for comparison.
• Infrared spectroscopy was also used to benchmark SERS performance.

Main Results:

• SERS successfully detected bromazolam (0.32-36% w/w) and xylazine (0.15-15% w/w) in street opioid samples.
• High specificity (88% bromazolam, 96% xylazine) and sensitivity (88% bromazolam, 92% xylazine) were achieved.
• SERS performance surpassed that of infrared spectroscopy for the tested samples.

Conclusions:

• SERS combined with a random forest classifier is a viable technique for point-of-care detection of adulterants in complex opioid samples.
• This method offers high accuracy and exceeds the performance of infrared spectroscopy.
• SERS presents a promising tool for community-based drug checking and harm reduction efforts.