Neural Network-Based Detection of Adulterants in Opioid Samples Using IR Absorption Spectroscopy.

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Forensic Science

Background:

• Illicit drug supplies increasingly contain low-concentration adulterants, challenging community drug checking services.
• Detecting these adulterants requires sophisticated analysis of spectral data from instruments like infrared spectrometers.
• Characteristic spectral features in complex mixtures are difficult to identify using conventional methods.

Purpose of the Study:

• To develop and evaluate neural network models for detecting bromazolam and para-fluorofentanyl in drug samples.
• To compare the performance of neural networks against other machine learning approaches for adulterant detection.
• To enhance the capabilities of point-of-care drug checking services.

Main Methods:

• Infrared absorption data was collected at a community drug checking service.
• Neural network models were trained using this spectral data.
• A random forest model was also trained and optimized on the same dataset for comparative analysis.

Main Results:

• Neural network models achieved high detection accuracy, with F1-scores of 0.88 for bromazolam and 0.89 for para-fluorofentanyl.
• Random forest models showed lower performance, with F1-scores of 0.66 for bromazolam and 0.76 for para-fluorofentanyl.
• Neural networks demonstrated superior performance in identifying these specific adulterants.

Conclusions:

• Neural networks are highly effective for complex drug detection tasks in drug checking services.
• This approach offers a significant improvement over traditional machine learning methods for identifying low-concentration adulterants.
• The findings support the integration of advanced machine learning for real-world drug analysis and public health surveillance.