Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning

Abstract

Abstract With the growing global challenge of drug abuse, there is an urgent need for rapid, accurate, and cost-effective drug detection methods. This study introduces an innovative approach to drug abuse screening by quickly detecting ephedrine (EPH) in tears using drop coating deposition-surface enhanced Raman spectroscopy (DCD-SERS) combined with machine learning (ML). Using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), the average concentration of EPH in tear fluid of Sprague-Dawley (SD) rats, measured over 3 h post-injection, was 1235 ng/mL. DCD-SERS effectively identified EPH in tear samples, with distinct Raman peaks observed at 1001 cm−1 and 1242 cm−1. To enable rapid analysis of complex SERS data, three ML algorithms—linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), and random forest (RF)—were employed. These algorithms achieved over 90% accuracy in distinguishing between EPH-injected and non-injected SD rats, with area under the ROC curve (AUC) values ranging from 0.9821 to 0.9911. This approach offers significant potential for law enforcement by being easily accessible, non-invasive and ethically appropriate for examinees, while being rapid, accurate, and affordable for examiners.

Keywords

• Drug abuse
• Ephedrine
• Tear
• Surface enhanced Raman spectroscopy
• Machine learning