Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning
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 e...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85451-y |
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| author | Yingbin Wang Yulong Huang Xiaobao Liu Chishan Kang Wenjie Wu |
| author_facet | Yingbin Wang Yulong Huang Xiaobao Liu Chishan Kang Wenjie Wu |
| author_sort | Yingbin Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5aa26e6749704badbc34978b12e3082c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5aa26e6749704badbc34978b12e3082c2025-01-12T12:20:48ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-85451-yRapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learningYingbin Wang0Yulong Huang1Xiaobao Liu2Chishan Kang3Wenjie Wu4Department of Ophthalmology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalShengli Clinical Medical College, Fujian Medical UniversityShengli Clinical Medical College, Fujian Medical UniversityShengli Clinical Medical College, Fujian Medical UniversityDepartment of Ophthalmology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalAbstract 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.https://doi.org/10.1038/s41598-025-85451-yDrug abuseEphedrineTearSurface enhanced Raman spectroscopyMachine learning |
| spellingShingle | Yingbin Wang Yulong Huang Xiaobao Liu Chishan Kang Wenjie Wu Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning Scientific Reports Drug abuse Ephedrine Tear Surface enhanced Raman spectroscopy Machine learning |
| title | Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning |
| title_full | Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning |
| title_fullStr | Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning |
| title_full_unstemmed | Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning |
| title_short | Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning |
| title_sort | rapid detection of drug abuse via tear analysis using surface enhanced raman spectroscopy and machine learning |
| topic | Drug abuse Ephedrine Tear Surface enhanced Raman spectroscopy Machine learning |
| url | https://doi.org/10.1038/s41598-025-85451-y |
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