{"title":"X-ray absorption spectroscopy combined with deep learning for auto and rapid illicit drug detection.","authors":"Zheng Fang, Xiefeng Zhan, Bichao Ye","doi":"10.1080/00952990.2024.2377262","DOIUrl":null,"url":null,"abstract":"<p><p><i>Background:</i> X-ray absorption spectroscopy (XAS) is a widely used substance analysis technique. It bases on the different absorption coefficients at different energy level to achieve material identification. Additionally, the combination of spectral technology and deep learning can achieve auto detection and high accuracy in material identification.<i>Objectives:</i> Current methods are difficult to identify drugs quickly and nondestructively. Therefore, we explore a novel approach utilizing XAS for the detection of prohibited drugs with common X-ray tube source and photon-counting (PC) detector.<i>Method:</i> To achieve automatic, rapid, and accurate detection of drugs. A CdTe detector and a common X-ray source were used to collect data, then dividing the data into training and testing sets. Finally, the improved transformer encoder model was used for classification. LSTM and ResU-net models are selected for comparation.<i>Result:</i> Fifty substances, which are isomers or compounds with similar molecular formulas of drugs, were selected for experiment substances. The results showed that the improved transformer model achieving 1.4 hours for training time and 96.73% for accuracy, which is better than the LSTM (2.6 hours and 65%) and ResU-net (1.5 hours and 92.7%).<i>Conclusion:</i> It can be concluded that the attention mechanism is more accurate for spectral material identification. XAS combined with deep learning can achieve efficient and accurate drug identification, offering promising application in clinical drug testing and drug enforcement.</p>","PeriodicalId":48957,"journal":{"name":"American Journal of Drug and Alcohol Abuse","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Drug and Alcohol Abuse","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/00952990.2024.2377262","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/19 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: X-ray absorption spectroscopy (XAS) is a widely used substance analysis technique. It bases on the different absorption coefficients at different energy level to achieve material identification. Additionally, the combination of spectral technology and deep learning can achieve auto detection and high accuracy in material identification.Objectives: Current methods are difficult to identify drugs quickly and nondestructively. Therefore, we explore a novel approach utilizing XAS for the detection of prohibited drugs with common X-ray tube source and photon-counting (PC) detector.Method: To achieve automatic, rapid, and accurate detection of drugs. A CdTe detector and a common X-ray source were used to collect data, then dividing the data into training and testing sets. Finally, the improved transformer encoder model was used for classification. LSTM and ResU-net models are selected for comparation.Result: Fifty substances, which are isomers or compounds with similar molecular formulas of drugs, were selected for experiment substances. The results showed that the improved transformer model achieving 1.4 hours for training time and 96.73% for accuracy, which is better than the LSTM (2.6 hours and 65%) and ResU-net (1.5 hours and 92.7%).Conclusion: It can be concluded that the attention mechanism is more accurate for spectral material identification. XAS combined with deep learning can achieve efficient and accurate drug identification, offering promising application in clinical drug testing and drug enforcement.
期刊介绍:
The American Journal of Drug and Alcohol Abuse (AJDAA) is an international journal published six times per year and provides an important and stimulating venue for the exchange of ideas between the researchers working in diverse areas, including public policy, epidemiology, neurobiology, and the treatment of addictive disorders. AJDAA includes a wide range of translational research, covering preclinical and clinical aspects of the field. AJDAA covers these topics with focused data presentations and authoritative reviews of timely developments in our field. Manuscripts exploring addictions other than substance use disorders are encouraged. Reviews and Perspectives of emerging fields are given priority consideration.
Areas of particular interest include: public health policy; novel research methodologies; human and animal pharmacology; human translational studies, including neuroimaging; pharmacological and behavioral treatments; new modalities of care; molecular and family genetic studies; medicinal use of substances traditionally considered substances of abuse.