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利用表面增强拉曼光谱和机器学习通过泪液分析快速检测药物滥用。

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

作者信息

Wang Yingbin, Huang Yulong, Liu Xiaobao, Kang Chishan, Wu Wenjie

机构信息

Department of Ophthalmology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, 134 Dongjie Rd, Fuzhou, 350001, Fujian, China.

Shengli Clinical Medical College, Fujian Medical University, Fuzhou, Fujian, China.

出版信息

Sci Rep. 2025 Jan 7;15(1):1108. doi: 10.1038/s41598-025-85451-y.

DOI:10.1038/s41598-025-85451-y
PMID:39774298
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11707011/
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 and 1242 cm. 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.

摘要

随着全球药物滥用挑战的日益严峻,迫切需要快速、准确且经济高效的药物检测方法。本研究介绍了一种创新的药物滥用筛查方法,即通过滴涂沉积-表面增强拉曼光谱法(DCD-SERS)结合机器学习(ML)快速检测泪液中的麻黄碱(EPH)。使用超高效液相色谱-串联质谱法(UPLC-MS/MS),在注射后3小时内测量的Sprague-Dawley(SD)大鼠泪液中EPH的平均浓度为1235 ng/mL。DCD-SERS有效地识别了泪液样本中的EPH,在1001 cm和1242 cm处观察到明显的拉曼峰。为了能够快速分析复杂的SERS数据,采用了三种机器学习算法——线性判别分析(LDA)、偏最小二乘判别分析(PLS-DA)和随机森林(RF)。这些算法在区分注射EPH和未注射EPH的SD大鼠方面的准确率超过90%,ROC曲线下面积(AUC)值在0.9821至0.9911之间。这种方法具有显著的执法潜力,因为它易于获取、非侵入性且对受检者符合伦理规范,同时对检查者来说快速、准确且经济实惠。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/2d9b25595f9c/41598_2025_85451_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/3163fd139aa6/41598_2025_85451_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/c6b0032ec6d6/41598_2025_85451_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/ff12b8816ec4/41598_2025_85451_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/0a16f30d829a/41598_2025_85451_Fig5a_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/80294d418353/41598_2025_85451_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/2d9b25595f9c/41598_2025_85451_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/3163fd139aa6/41598_2025_85451_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/c6b0032ec6d6/41598_2025_85451_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/0f11c8b3bb22/41598_2025_85451_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/ff12b8816ec4/41598_2025_85451_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/0a16f30d829a/41598_2025_85451_Fig5a_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/80294d418353/41598_2025_85451_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb21/11707011/2d9b25595f9c/41598_2025_85451_Fig7_HTML.jpg

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